Data Analysis and Visualization for Communication Science […] This class will be the intersection of data analysis, visual design, and communication science. We’ll examine some good and bad data visualizations, and make plenty of our own. No prior knowledge of R will be required to take this course, but students should be prepared to learn a new programming language and to work with data. Short answer: Sure! Just don’t trust them. With a university email address, you can sign up for the GitHub Student Developer Pack, which will let you use the GitHub Copilot AI tool for free. UZH also … Read more →

Emily Peterson Welcome to Modern Regression! Course Number: 526 Section Number: 1 Credit Hours: 3 Semester: Fall 2024 Class Hours and Location: Monday & Wednesday 1:30-2:50pm atCNR 1051 Instructor Name: Emily Peterson (Emily) emily.nancy.peterson@emory.edu Teaching Assistants: Yutong Liu yutong.liu@emory.edu Office hours: Mon 3pm-5pm at GCR 369 or Zoom (https://zoom.us/j/5503938635) request remote session via email. Course Description: This course introduces students to modern regression techniques commonly used in analyzing public health data. Specific topics include: (1) methods for … Read more →

This book provides a gentle introduction to data science for students of any discipline with little or no background in data analysis or computer programming. Based on notions of representation, measurement, and modeling, we examine key data types (e.g., logicals, numbers, text) and learn to clean, summarize, transform, and visualize (rectangular) data. By reflecting on the relations between representations, tasks, and tools, the course promotes data literacy and cultivates reproducible research practices that precede and enable practical uses of programming or statistics. This book is still being written and revised. It currently serves as a scaffold for a curriculum that will be filled with content as we go along. Read more →

Producing and Using Data in Cognitive Science […] Welcome to the course ‘Producing and using data in Cognitive Science’. This course covers what would traditionally be included in a statistics course, plus some of a research methods course, plus some of a data science course. The basic idea is the following: to answer scientific questions in cognitive science, we have to: produce the right data, the data that have the best chance of answering our question; handle those data right, organizing them, manipulating them in ways that are transparent, storing them permanently and making them … Read more →

This document contains the notes for KSU STAT 225 […] Bookdown is a documentation platform written in Pandoc Markdown syntax through R. You don’t need to have R or RStudio downloaded in order to access this or any bookdown document, however you do need to be connected to the internet. The document will be updated weekly with the most recent class topics, and the previous topics will remain for your personal reference. Since this is a publicly available link you could share this with others in the course. If you feel it’s necessary to do that, I encourage it. The link to this bookdown will … Read more →

STA 444/5 - Introductory Data Science using R […] This book is intended for use during the STA 444/445 courses at Northern Arizona University. The book is broken into two sections based on the related course material. The STA 444 section covers basic introductory content for getting started with statistical programming in R. This course is intended for students of all backgrounds and pairs importantly with courses such as STA 570 (Statistical Methods I) and STA 471 (Regression Analysis). The first section covers details to allow students to work on basic statistical programming while … Read more →

Introduction to Statistics. […] There are a lot of ways to approach an introductory statistics class. Historically, the topics found in this text have been taught in a way that emphasizes hand calculations and the use of tables full of numbers. My philosophy is a little different. This class is designed for students who will need to read statistical results and may need to produce basic statistics using a computer. If you go on to be a scientist and need more statistical know how, this course will give you enough background knowledge to take the inevitable next course in statistics. There … Read more →

This is a set of minimal introductory notes, developed by Dr. Jessica Kraker for use at UW - Eau Claire and in community education. Original notes were devised for the UWEC DS 140 course. […] Contact: Please contact Jessica Kraker at krakerjj@uwec.edu with questions or … Read more →

This is the full book written in preparation for my course on “Survey data in the field of Economics and Finance” given at the University of Luxembourg in 2024-2025 (Master 2 ‘Economy and Finance’). […] Counting populations through exhaustive censuses has been a practice that has been established for millennia, dating back to the era of ancient civilizations such as the Babylonians, Egyptians and Romans. These populations conducted censuses for supporting a range of economic decisions, including taxation, scaling the labor force, food distribution and industrial investment. Censuses are … Read more →

These are the course notes for the Machine Learning module of Durham University’s Masters of Data Science course. […] Welcome to the material for the first term of the module Advanced Statistical Modelling MATH3411 at Durham University. These pages will update as the course progresses, consisting of relevant lecture notes, practical demonstrations (in R), exercise sheets and practical sessions. I would recommend that you use the html version of these notes (they have been designed for use in this way), however, there is also a pdf version of these notes, which will also be updated as the … Read more →

Volatility modelling and market risk analysis […] This course notes are being prepared for the students at Gdańsk University of Technology. Dear students. For the purposes of our class I am testing bookdown (http://bookdown.org). We will see how it … Read more →

Regression Models […] These notes contain both the theory and practice for the statistical models presented in the course. Regression Analysis is the most common statistical modeling approach used in data analysis, and it is the basis for more advanced statistical and machine learning modeling. In this course, you will received the foundation knowledge in the use of widely used tools in regression analysis. You will learn the basics of regression analysis such as linear regression, logistic regression, Poisson regression, generalized linear regression and generalized additive models. … Read more →

Fantastic Genes and Where to Find Them […] First, I would like to acknowledge the Genome Education Partnership (GEP) for inspiration. They created a resource for learning about Eukaryotic gene structure using the UCSC Genome Browser (Laakso et al. 2017). Their focus is on the Drosophila genome. My focus is on humans. This manual has been written for a undergraduate course entitled Principle of Genetic analysis (BIOL 310) at Cal State University, East Bay but can be used by anyone wanting to learn more about gene structure and function. My hope is that skills learned within these pages and … Read more →

Script for Analysis of High-Dimensional Data […] Prediction and Feature Assessment This script was written for the course on Analysis of High-Dimensional Data held at the University of Bern and the ETH Zurich. Much of the content is based on the book from Hastie, Tibshirani, and Friedman (2001). The course has a focus on applications using R (R Core Team 2023). All data sets used throughout the script can be downloaded from github. What are high-dimensional data and what is high-dimensional statistics? The Statistics Department of the University of California, Berkeley summarizes it as … Read more →

This is a minimal example of using the bookdown package to write a book. set in the _output.yml file. The HTML output format for this example is bookdown::gitbook, [...] This is the task book for the SWBio Bioinformatics course. This book summarises the tasks that you should work through during the workshops. The first three workshops are planned to be in-person with the instructor(s) present. During those in-person workshops there will be time to complete at least some of the tasks. Later in the week there are remote self-study sessions where you work at your own pace and the ... Read more →

Econometrics […] I truly hope you will benefit from the course with respect to the variety of econometrics applications it covers. The course purpose is to introduce a formal framework for analyzing real-life problems with actual data, enabling students to improve their understanding of when econometric methods and models should be used and how to apply them in practice. The entire course is supported by numerous worked-out examples using RStudio (open-source software for data … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::bs4_book, set in the _output.yml file. [...] Welcome to website of the course “Introduction to Bayesian Inference and Statistical Learning.” offered by the Chair of Spatial Data Science and Statistical Learning at the University of Goettingen. This course is designed to equip you with both the theoretical foundations and practical tools necessary for applying Bayesian and statistical learning approaches to real-world data. By the end of the course, you will be ... Read more →

This is a book developed by Siegfred Codia for Stat 136 class in UP Diliman. […] Linear regression model; model selection; regression diagnostics; use of dummy variables; remedial measures. Prereq: Stat 131, Stat 135. 3h. 3 u. The course shall equip the learners with the foundation necessary to perform linear regression analysis. In this course, we specifically aim to: discuss linear regression modeling as a methodology built upon the theories of statistical inference and matrix algebra apply regression theory to real-life data, solve research questions, and distinguish between proper … Read more →

Alex Edwards and Emily Peterson Authors: Alex Edwards Fall 2024 This Advanced GIS (DATA 532) class is a project-based exploration of advanced topics in GIS and geospatial technology, with a focus on spatial modeling, advanced spatial analysis and geoprocessing, spatial data manipulation, and geocomputation. For information on course expectations, assignments, grading, and schedule, please review the course syllabus listed on Canvas. We will use this e-book for lectures, and in-class activities. All course content will be housed in this book for your reference. Advanced GIS analysis methods … Read more →

Linear Algebra 2024 Notes […] Welcome to Linear Algebra 2024. These are lecture notes for the first half of the first year Linear Algebra course in Bristol. Changes are made from year to year, so please do let me know if you find any typos (email r.m.carey@bristol.ac.uk). These notes have been written by Rachael Carey based on notes originally written by Roman Schubert and further developed by Misha Rudnev and John Mackay. These notes are provided exclusively for educational purposes by the School of Mathematics, University of Bristol. This material is copyright of the University. For … Read more →

B.A. Seminar at LMU Munich, Fall 2024/2025 […] This online tutorial will accompany the seminar “Studying Online News via Computational Methods”. It is part of the undergraduate course “Communication Studies” (LMU, Department of Media and Communication) and takes place Friday 10 am to 2pm in the CIP pool, Akademiestr. 7. You will find all necessary information on the seminar’s structure, important dates and assessments/evaluations via Moodle. Please carefully consider the syllabus when deciding whether to participate in this seminar. This tutorial will introduce you to two main aspects: You … Read more →

This is a free, activity-based introductory statistics class, suitable for high-school and college students. The course is designed around active learning, statistical modeling, and simulation-based inference. Students use Monte Carlo Simulation to model variability, and they make conclusions based on the outcomes of their models. This book is part of the free Statistical Reasoning: A Modeling and Simulation Approach curriculum. More resources are available at https://www.fapeck.com/statistical-reasoning/ This work is licensed under a Creative Commons Attribution 4.0 International License. … Read more →

This is a draft course-book for the MSc Data Science Analytics module CS5702 Modern data […] This book cover image was generated using R and the famous cars dataset1. Courtesy of Giora Simchoni’s fun R package {kandinsky} which generates random images from datasets in the style of the painter … Read more →

Actuarial Faculty Development Program 2024 - ACTEX Learning […] This course is designed to introduce actuarial students to the R programming language This course is designed to equip you with the technical skills to use R in actuarial science. You’ll gain the necessary knowledge to succeed in the rapidly evolving world of risk analysis, insurance, and finance. Throughout the program, you’ll discover the power of R, one of the most widely-used programming languages in statistics and actuarial science, for performing essential tasks like pricing, reserving, and risk management. Whether you’re … Read more →

Accompanying IMSID course […] This instruction manual belongs to the course Introduction to R which is taught at the Institute of Medical Statistics, Computer and Data Sciences at Jena University Hospital. Each chapter belongs to one of the five course dates. It is written in a way that should allow you to reproduce the entire course by yourself on your personal computer. There are a lot of code examples in this instruction manual. You can generally recognize a piece of R code in this document by the grey highlighting. If the code returns a result, the result is displayed directly below the … Read more →

TUTORIALS for quantitative research in science, engineering and health (including research design, hypothesis testing and confidence intervals in common situations) […] This book has been prepared for use with the book Scientific Research and Methodology, to be used in the course Science Research Methods at the University of the Sunshine Coast (UniSC). This course is an introduction to quantitative research methods in the scientific, engineering and health disciplines. It introduces the whole research process, from asking a research question to analysis and reporting of the data. The focus, … Read more →

R for Applied Economics: A Beginner’s Guide […] Welcome to the R guide! This is an evolving document, and I appreciate your feedback about how I can make it most helpful. Please email me (mbrown35@stanford.edu) if you have comments or suggestions. I have written this guide for two audiences. The narrow audience is students in Prof. Hunt Allcott’s Empirical Environmental Economics (E3) course at Stanford.1 This website is a companion text for that course. E3 involves several data-based problem sets as well as a final project. We want students to be able to take E3 without any prior … Read more →

Applied Omics Science for Drug Discovery and Development […] The lab module includes four sections: This course will cover several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research as it relates to genomics and the omic sciences. For the bulk of the course we cover topics related to genomics and high-dimensional data. Here we cover experimental techniques used in genomics including RNA-seq and variant analysis. We start with an introduction to R, including data structures, data wrangling and plotting methods. We then we walk you … Read more →

Regression Models (Level M) […] Welcome to Regression Models Level M. This course will be taught by In this document, you can find the full set of lecture notes. We will describe to you how we expect you to use these lecture notes. For the next 10 weeks, we will have 20 lectures. You will notice that the sections here are labelled Lecture 1 through to Lecture … Read more →

A comprehensive guide to a wide range of portfolio designs, bridging the gap between mathematical formulations and practical algorithms. A must-read for anyone interested in financial data modeling and portfolio design. It is suitable as a textbook for portfolio optimization and financial data modeling courses. […] … Read more →

This textbook accompanies the course HE-930 in the PhD in HPEd program at MGH Institute of Health Professions. This book introduces students to basic predictive analytics and machine learning, with examples and applications related to education and healthcare. […] This textbook accompanies the course HE-930—Statistics/Predictive Analytics for Health Professions Education—in the PhD in HPEd program at MGH Institute of Health Professions. HE-930 is a data analytics course that introduces students to basic predictive analytics (PA) and machine learning (ML), with examples and applications … Read more →

Course notes for Regression and Analysis of Variance (STAT 705) at Kansas State University for Summer 2024 […] This document contains the course notes for Regression and Analysis of Variance at Kansas State University (STAT 705). During the semester we will cover the basics such as regression and ANOVA modeling, parameter estimation, model checking, inference, and prediction. We may also cover modern topics such as regularization, random effects, generalized linear models, machine learning approaches, and Bayesian regression and … Read more →

This textbook accompanies the course HE-902 in the PhD in HPEd program at MGHIHP (http://mghihp.edu/phdhped). HE-902 is a statistics course that equips students to analyze healthcare or behavioral data in R. […] Welcome to HE-902 at MGH Institute of Health Professions! Please watch the following welcome video: This video can be viewed externally at https://youtu.be/o_DVZ8GplGY. Please additionally read the following items: This textbook accompanies the course HE-902—Advanced Statistical Modeling for Health Professions Education—in the PhD in HPEd program at MGH Institute of Health … Read more →

These are the lectures note for UChicago’s course ECON 21020 ‘Econometrics.’ […] This document contains the course notes for ECON 21020 ‘Econometrics’ at the University of Chicago, taught by Melissa Tartari. This is a sample book written in Markdown. You can use anything that Pandoc’s Markdown supports, e.g., a math equation (a^2 + b^2 = c^2). The bookdown package can be installed from CRAN or Github: Remember each Rmd file contains one and only one chapter, and a chapter is defined by the first-level heading #. To compile this example to PDF, you need XeLaTeX. You are recommended to … Read more →

This text was written to provide Wright State University MPH students an introduction to the R programming language for use in research. […] This online book was written to provide Wright State University MPH students an introduction to the R programming language for use in research. The datasets used in this text are not being made publicly available. If you are using this material as part of a Wright State University course or tutorial, datasets will be provided to you by the instructor or in your learning management system. If you have any comments or suggestions, feel free to contact … Read more →

Felix Lennert Dear student, if you read this script, you are either participating in one of my courses on digital methods for the social sciences, or at least interested in this topic. If you have any questions or remarks regarding this script, hit me up at felix.lennert@ensae.fr. This script will introduce you to two techniques I regard as elementary for any aspiring (computational) social scientist: the collection of digital trace data via either scraping the web or acquiring data from application programming interfaces (APIs) and the analysis of text in an automated fashion (text mining). … Read more →

These are the course notes for the Machine Learning module of Durham University’s Masters of Data Science course. […] Welcome to the material for the first half of the Machine Learning module MATH42815 of the Masters of Data Science course at Durham University. These pages will update as the course progresses, and consist of relevant lecture notes, practical demonstrations (in R) and practical workshop sessions. I would recommend that you use the html version of these notes (they have been designed for use in this way), however, there is also a pdf version of these notes. If you would like … Read more →

This book studies exploratory data analysis and data visualization in the context of a university degree in Data Sciences. […] This document is an English translation of the book Análisis Exploratorio de Datos y Visualización, it covers the contents of an introductory course on exploratory data analysis and visualization in a university degree in Data Sciences. Exploratory data analysis is a very broad field, and it is not possible to teach all its aspects in depth in a single course. This course, of an introductory nature, aims to provide a solid foundation in the most important tools in … Read more →

Notes and tutorials on building online courses. […] This handbook is still in draft form and many chapters are unfinished or in a “notes state”. Chapters, sections, and practices are filled in as need arises. This book summarizes my experiences building and teaching online courses at Northeastern University’s Khoury College of Computer Sciences. I wrote this handbook for faculty new to teaching online and new to building courses for online delivery. The writing of this handbook was accelerated when the COVID-19 pandemic forced all classes to go into an online or remote instruction mode. It … Read more →

The Infinite Hermetic Mind: Bridging the Above and the Below […] This book unfolds as a Socratic dialogue with artificial intelligence, engaging in a profound exploration of intuitive intelligence and the intricate landscape of consciousness. Our discourse navigates through the mind’s three axes—will, heart, and intelligence—as we traverse the Bridge of Light (Antahkarana), illuminating our path. Together, we reexamine Plato’s Allegory of the Cave and reflect on the Hermetic Principle of Analogy, contemplating the mirrors between the macrocosm and microcosm. Through the lens of practice of … Read more →

This bookdown contains the teaching materials for the projectcourse Financial Data Science at the LMU Munich. […] Here you will find the course pages for the projectcourse Financial Data Science. The projectcourse is offered regularly in the winter and summer term and aims at providing in-depth knowledge about the programming language Python and its most important libraries for data analysis. Each summerterm, the course is taught in cooperation with the Institute for Finance & Banking and consists of two parts. Each winterterm, the course extends the introduction of programming language … Read more →

to programming with R! I hope learning to code will change your life like it did mine :). Since 2012, I teach R courses with great joy, see brry.github.io. This website is here to help you start your coding journey. My main slidedeck (in a single PDF) is avalaible through two openHPI courses. These have autograded exercises and are fairly suitable to learn R on your own. Feel free to book me as a trainer in addition. German, ca 20-50 hours English, ca 30-70 hours There is also material for a course taking a few hours. My old slides are still directly available. A few notes on this website: The … Read more →

Notes for Predictive Modeling. MSc in Big Data Analytics. Carlos III University of Madrid. [...] Welcome to the notes for Predictive Modeling. The course is part of the MSc in Big Data Analytics from Carlos III University of Madrid. The course is designed to have, roughly, one session per main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the treatment of certain methods somehow superficial. Nevertheless, the course will hopefully give you a respectable panoramic view of different available statistical methods for predictive modeling. ... Read more →

This is a web-interface to the teaching materials for the lab course ‘Landscape Genetic Data Analysis with R’ associated with the distributed graduate course ‘DGS Landscape Genetics’. The output format is bookdown::gitbook. […] This is a web-interface to the teaching materials for the lab course ‘Landscape Genetic Data Analysis with R’ associated with the distributed graduate course ‘DGS Landscape Genetics’. The Landscape Genetics Distributed Graduate Seminar (DGS) is an international collaboration that provides a unique opportunity for interdisciplinary graduate training. The course draws … Read more →

Applied regression analysis […] 24-04-2024 The text is under development and updates are constant This text book uses notes written by Dr. Linda van der Merwe, who was responsible for the course in previous years and is currently being … Read more →

This bookdown contains the teaching materials for the projectcourse Financial Data Science at the LMU Munich. The files have been set up by Lisa Kaminski. [...] Here you will find supporting material on how to write academically. This guide is a generalized framework for seminar reports, bachelor and master theses. Disclaimer: The following guidelines should not be seen as static set of immutable rules, but rather as a profound and generic guideline. This guide is subject to constant revision and extension! If you come across missing subjects, redundancies or inconsistencies, we are ... Read more →

A set of notes for the Cambridge Canoe Club Sea Kayak Award Course, 2024 […] Welcome to the CCC beginner’s sea kayak course! The course aims to take beginner sea kayakers to the point where they can: Join intermediate level club trips (see our trip designations here). Plan and undertake their own trips on the sea, with peers, in simple conditions We’ll (loosely) we working with the syllabus of the British Canoeing Sea Kayak Award and we’re hoping that all participants reach a level where we can sign them off for this award. These notes are based around the Sea Kayak Award syllabus and are … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] Welcome to the beginning of the bioinformatics component of BMS5021 - Introductory Bioinformatics. Over the past four weeks, you have taken a crash course in the fundamentals of molecular biology. These concepts underpin the analyses we do in bioinformatics. Bioinformatics combines the strengths of computer science, statistics, and biology in order to analyse and interpret large-scale biological data, ultimately ... Read more →

The course book for BIDA302 […] Latest update: 2024-04-03 This book is based on the lessons for BIDA302, “Data Analytics Coding Fundamentals”, offered at the University of Victoria’s Continuing Studies Department. From UVic Continuing Studies website This course will explore the fundamentals of coding and scripting for Data Analytics. You will develop the ability to script and code for basic tasks in Data Analytics in common data analytic tools such as R, Python, and Excel. This will allow you to import and export data appropriately and perform fundamental data manipulations and to automate … Read more →

Notes for Nonparametric Statistics. MSc in Statistics for Data Science. Carlos III University of Madrid. [...] Welcome to the notes for Nonparametric Statistics. The course is part of the MSc in Statistics for Data Science from Carlos III University of Madrid. The course is designed to have, roughly, one session per each main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the treatment of certain methods somehow superficial. Nevertheless, the course will hopefully give you a respectable panoramic view of different available topics on ... Read more →

Notes for Statistical Inference. MSc in Statistics for Data Science. Carlos III University of Madrid. [...] Welcome to the notes for Statistical Inference. The course is part of the MSc in Statistics for Data Science from Carlos III University of Madrid. The course is designed to have, roughly, one session per each main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the exposition of certain methods somehow superficial. Nevertheless, the course and exercises will hopefully give you a respectable panoramic view of the fundamentals of ... Read more →

These are the course notes for the module Advanced Statistical Modelling III of Durham University’s degree for Mathematics and Statistics. … Read more →

This is a working document that will eventually become the official textbook for all of Dr. Alex P. Leith’s MC 451 course at Southern Illinois University Edwardsville. […] Welcome to “Quantitative Research in Mass Communications: R and RStudio,” a comprehensive guide designed to navigate the intricate pathways of quantitative research in the ever-evolving field of mass communications. This textbook is a culmination of my journey in academia and a reflection of my commitment to advancing the understanding of mass communication research methods, particularly through the lens of quantitative … Read more →

A textbook for an introductory (first-year or second-year undergraduate) course in statistics for economics majors. […] As its name suggests, Introductory Statistics for Economics is a textbook intended for use in an introductory (first or second year) statistics course for economics majors. It was written for use as a textbook for ECON 233, the introductory statistics course for economics majors at Simon Fraser University. The content is similar to most other introductory statistics courses for business and economics students, but with a few important differences. When I was assigned to … Read more →

Introduction to Google Earth Engine […] https://courses.spatialthoughts.com/end-to-end-gee.html https://www.earthdatascience.org/tutorials/ https://developers.google.com/earth-engine/guides https://developers.google.com/earth-engine/tutorials/tutorials https://developers.google.com/earth-engine/datasets https://developers.google.com/earth-engine/tutorials https://github.com/samapriya/awesome-gee-community-datasets https://github.com/giswqs/Awesome-GEE Random forest classification time series charts Landsat derived forest disturbance … Read more →

Notes and course info for Stat 344TM […] These notes are divided into three sections: This document isn’t designed to be read through in order – hence all the hyperlinks. Use the sidebar to navigate to what you need (you can also bookmark specific locations in your browser), and feel free to ask (or start a discussion thread!) if you can’t find something. Happy … Read more →

A Course Companion Website. […] Step into a realm of innovation with the Intel Unnati Certificate Programme, an avant-garde initiative meticulously crafted to delve into the intricacies of advanced machine learning. This course is not just an educational endeavor; it’s a gateway to a world where theoretical understanding seamlessly converges with hands-on practical application, providing you with a distinctive edge in an ever-evolving tech landscape. In a rapidly changing technological landscape, the Intel Unnati Certificate Programme is your ticket to mastering the latest advancements in … Read more →

Notes and course info for Stat 343 […] These notes are divided into three sections: This document isn’t designed to be read through in order – hence all the hyperlinks. Use the sidebar to navigate to what you need (you can also bookmark specific locations in your browser), and feel free to ask (or start a discussion thread!) if you can’t find something. Happy … Read more →

This book contains case studies and course notes for STAT 240, Introduction to Data Modeling, at the University of Wisconsin, including instruction for many tidyverse packages […] Statistics 240 is a first course in data science and statistical modeling at the University of Wisconsin - Madison. The course aims to enable you, the student in the course, to gain insight into real-world problems from messy data using methods of data science. These notes chart an initial path for you to gain the knowledge and skills needed to become a data scientist. The structure of the course is to present a series … Read more →

Felix Lennert Dear student, if you read this script, you are either participating in one of my courses on digital methods for the social sciences, or at least interested in this topic. If you have any questions or remarks regarding this script, hit me up at felix.lennert@ensae.fr. This script will introduce you to two techniques I regard as elementary for any aspiring (computational) social scientist: the collection of digital trace data via either scraping the web or acquiring data from application programming interfaces (APIs) and the analysis of text in an automated fashion (text mining). … Read more →

Tyler R. Pritchard Report errors, recommendations, or concerns to trpritchard@grenfell.mun.ca. Latest Updates: Jan 2024 Dec 2023 From the university calendar: PSYC 3950 Research Methods and Data Analysis in Psychology III will cover advanced research methods, including survey methods, and supporting statistical concepts and techniques. Designs will include single factor designs and multi-factor designs with both random and fixed factors. Supporting statistical concepts will include analysis of variance (ANOVA) from a linear model perspective, statistical power, and multiple regression, … Read more →

B.A. Seminar at LMU Munich, Fall 2023/2024 […] This online tutorial will accompany the seminar “Understanding Digital Information Flow via Computational Methods”. It is part of the undergraduate course “Communication Studies” (LMU, Department of Media and Communication) and takes place Friday 10 am to 2pm in the CIP pool, Akademiestr. 7. You will find all necessary information on the seminar’s structure, important dates and assessments/evaluations via Moodle. Please carefully consider the syllabus when deciding whether to participate in this seminar. This tutorial will introduce you to two … Read more →

This online course describe how to extract and use open source data for factor analysis in R. […] Course Overview Hi All, Welcome to the Online Course on “Exploratory Factor Analysis in R”. This is an online course designed to deepen your understanding of how to conduct factor analysis in R. The target audience are graduate students, researchers, and anyone interested in learning how to use open-source data for factor analysis. Factor analysis is a data reduction method used to explore and validate the structure of observed variables in multivariate data. Please read the course syllabus … Read more →

Notes and course info for Stat 340 […] These notes are divided into three sections: This document isn’t designed to be read through in order – hence all the hyperlinks. Use the sidebar to navigate to what you need (you can also bookmark specific locations in your browser), and feel free to ask (or start a discussion thread!) if you can’t find something. Happy … Read more →

Accompanying IMSID course […] These assignments accompany the course Introduction to R and should be done at home before each course date. We will link to the respective book section in each of the exercises, so you’ll be able to find instructions on how to solve this kind of exercise quickly. We will discuss solutions in the seminar session, complete code solutions will be published after the … Read more →

Economics 395: Forecasting […] This resource is a compilation of Dr. Jaya Jha’s notes and code for Economics 395: Economic Forecasting. This course uses Introduction to Time Series Analysis and Forecasting as the primary textbook.1 The code sections for this class use the same data and visualizations from Introduction to Time Series Analysis and Forecasting, but not the methods.2 The methods used are from Forecasting: Principles and Practice by Rob J. Hyndman and George Athanasopoulos.3 Montgomery, Jennings, and Kulahci, Introduction to Time Series Analysis and Forecasting.↩︎ Montgomery, … Read more →

This is a minimal example of using the bookdown package to write a book. set in the _output.yml file. The HTML output format for this example is bookdown::gitbook, [...] This is a course on models for multilevel nested data. These data arise in nested designs, which are quite common to education and applied social, behavioral and policy science. Traditional methods, such as OLS regression, are not appropriate in this setting, as they fail to model the complex correlational structure that is induced by these designs. Proper inference requires that we include aspects of the design in the ... Read more →

These are the course notes for the Machine Learning and Neural Networks module (MATH3431) at Durham University. […] Welcome to the material for the first half of the Machine Learning and Neural Networks module (MATH3431) at Durham University. These pages consist of relevant lecture notes will be updated as the course progresses. I would recommend that you use the html version of these notes (they have been designed for use in this way), however, there is also a pdf version of these notes. In this first half of the module (Michaelmas Term), we will be focusing on “Machine Learning” rather … Read more →

This is a textbook for the course J381M at UT-Austin. […] Welcome to the J381M Textbook! In this course, we will learn how to use R for Computational Communication Research and Data Science, focusing on skills such as data wrangling, basic statistics, data visualization, data collection, NLP, network analysis, and machine learning. This is a survey course that is meant to give you a taste of data science. In truth, many of these topics are rich enough to warrant full courses. This textbook is best paired with the J381M course materials, including lectures, readings, and course assignments. … Read more →

Lecture Notes for Foundations of Statistics […] In this course the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. The course will cover the core foundations of statistical theory consisting of: The course highlights the importance of computers, and in particular, statistical packages, in performing modern statistical analysis. Students will be introduced to the statistical package R as a statistical and programming tool and will gain experience in interpreting and communicating its output. Learning Outcomes A student who completes … Read more →

This are notes for STA 125 at Murray State University for students in Dr. Christopher Mecklin’s class. […] These notes are meant to supplement, not replace your textbook. I will occasionally cover topics not in your textbook, and I will stress those topics I feel are most important. “Statistical Reasoning” is a new course at Murray State, where the major goal of the course is to become a “consumer” of statistics rather than a “producer” of statistics. Thus, our emphasis will be on the correct interpretation of statistical results that you might run across in the media, particularly as … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] This is a document that contains syllabi for the courses taught by Hannah Lunkenheimer. Please reference this doc any time you have questions about your course. If you can’t find an answer, please try troubleshooting, checking your e-mail, Canvas, Slack, or e-mail me your question. Also, if you find any mistakes, please let me know. ... Read more →

This is an Excel computer lab manual to be used in an Introduction to Statistics course at QCC. […] Statistics is present in many ways in our lives. Statistical methodology can be found in surveys, sampling, clinical trials, studies of biomedical treatments, digital marketing, finance, etc. In recent years, Statistics has undergone changes in its techniques and approaches because of the need to analyze exceptionally large and complex data sets that arise all around us but cannot be done by hand. Since we have more powerful computers available to us at the present time, we can employ them to … Read more →

This contains materials for the Advanced R course of the doctoral school of Grenoble, France. […] This material is licensed under the Creative Commons Attribution-ShareAlike 3.0 License. Florian Privé is a researcher in predictive human genetics, fond of Data Science and an R(cpp) enthusiast. He is also the founder and former organizer of the Grenoble R user group. You can find him on Twitter and GitHub as @privefl and on Stack Overflow as F. Privé. … Read more →

This is a compilation of R code and output interpretation to accompany an Intermediate Inferential Statistics course taught at JMU. It is not intended to teach the statistics, but rather to support R coding and interpretation. [...] This book is intended to serve as a guide for the R functions you will be running in PSYC 605. The statistics will be covered in class; this is to support your analysis using R. This is not intended to be an exhaustive guide to R but rather a tool to aid in your work. For clarity’s sake, repeated tests between statistical techniques will be fully explained ... Read more →

This will serve as a course reader for SOC 128D, Summer 2023. […] Office Hours: Fridays and Mondays, 11:00am-12:30pm https://calendly.com/tylermcdaniel/tyler-s-office-hours Course Description: Data science has rapidly gained recognition within the social sciences because it offers powerful new ways to ask questions about social systems and problems. This course will examine how tools from data science can be used to analyze pressing issues relating to disaster, inequality, and scarcity in the Anthropocene (the current period in which humans are the primary driver of planetary changes). We … Read more →

Fantastic Genes and Where to Find Them […] First, I would like to acknowledge the Genome Education Partnership (GEP) for inspiration. They created a resource for learning about Eukaryotic gene structure using the UCSC Genome Browser (Laakso et al. 2017). Their focus is on the Drosophila genome. My focus is on humans. This manual has been written for a undergraduate course entitled Principle of Genetic analysis (BIOL 310) at Cal State University, East Bay but can be used by anyone wanting to learn more about gene structure and function. My hope is that skills learned within these pages and … Read more →

Homeworks for James’ Stats classes […] Howdy! This is a collection of 4 assignments from James Scott’s probability and Statistics courses, written by Zhifei Yu a.k.a … Read more →

Ben Prytherch STAT 331, as the title states, is an “applied” statistics course. It is intended for anyone who has taken at least one introductory level statistics course, and who wants to learn more about the use of statistical methods in quantitative research. It covers many statistical tools that are usually considered too advanced for an introductory level class, but are nonetheless very popular. It also provides guidance on making data analysis decisions. Most assignments will involve looking up a published scientific paper for which the data are available and reproducing the main … Read more →

Introduction to R course, as used on MSc Health Data Science […] As a Health Data Scientist, it is vitally important that you have a firm understanding of a statistical programming language, and that you can work in a clear, reproducible fashion. This course will provide you with the baseline skills to use R for health data science. Get you ‘up and running’ using R and RStudio on your machine. Introduce the basics of programming in R (a key skill for a health data scientist). Introduce good practices of workflows and reproducibility in data science. Enable you to develop your skills … Read more →

Companion document to Introduction to Statistical Investigations using R/RStudio. […] This companion was designed for use in STAT160 (Introduction to Data Science), however could be used for any intro-level data science course. The textbook for the course is Introduction to Statistical Investigations (Tintle et. al). Through in-class and home work assignments, students will learn to use R and RStudio. In this companion, we will review the commands and functions students will need to perform statistical analysis and generate statistical … Read more →

This book is supposed to introduce the reader (i.e., you) to a fundamental technique for computational social science research: the quantitative analysis of text. […] Dear student, if you read this script, you are either participating in one of my courses on digital methods for the social sciences, or at least interested in this topic. If you have any questions or remarks regarding this script, hit me up at felix.lennert@ensae.fr. This script will introduce you to the quantitative analysis of text using R. Through the last decades, more and more text has become readily available. Think for … Read more →

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] In this book, we will provide one way to master R language for beginners. … Read more →

This book serves as an accompanying script for the R sessions of the 2023 Summer Institute for Computational Social Science (SICSS), taking place at the Institut Polytechnique de Paris. […] Dear student, if you read this script, you are either participating in the SICSS itself or came across it while browsing for resources for your studies. In any case, if you find inconsistencies or mistakes, please do not hesitate to point them out by shooting an email to felix.lennert@ensae.fr. This script will introduce you to the automated acquisition and subsequent quantitative analysis of text data … Read more →

This textbook is designed to support the Advanced Higher Statistics course. … Read more →

Notes for the R labs of the SPEF 22/23 course @UniBg […] You are reading the lecture notes of the R labs for the Statistics and Probability for Economics and Finance (SPEF) course at University of Bergamo (academic year 2022/23) taught by Prof. Raffaele Argiento, Prof. Michela Cameletti and Prof. Tommaso Lando. R is a great programming language especially designed for statistical analysis and data visualisation. The SPEF R labs are designed for those who don’t have any programming background. It will be a step-by-step path; at the end you will have the basic R knowledge for analysing … Read more →

Notes for the R labs of the MLFE course @ Unibg […] You are reading the lecture notes of the R labs for the Machine learning for Economics (MLFE) course at University of Bergamo (academic year 2022/23). The MLFE course is the second module of the Coding for Data Science course. The MLFE R labs are designed for students who already have some experience with R programming thanks to the first module of the Coding and Machine Learning course. Click here and here to access the R lab notes of the first module regarding introduction to R language and the tidyverse package. Enjoy the journey! … Read more →

Notes for the R labs of the AIMLFF course @Unibg […] You are reading the lecture notes of the R lectures for the AI and Machine Learning for Finance (AIMLFF) course at University of Bergamo (academic year 2022/23). See here for more details. In this notes R programming language for data science will be introduced (with respect to data manipulation, data visualization and communication and implementation of machine learning methods). For this part I suggest the following on-line book: Enjoy the journey! In the following lecture notes, this font (with grey background) represents R code. The … Read more →

This is a minimal example of using the bookdown package to write a book. set in the _output.yml file. The HTML output format for this example is bookdown::gitbook, [...] This is a course in advanced statistical techniques that covers generalized linear models and extensions that are commonly used in health and policy research. Assuming a strong foundation in the general linear model (linear regression and ANOVA) and exposure to the linear mixed model (a.k.a. multilevel models), this course focuses on data analysis that utilizes models for categorical, discrete or limited outcomes, some ... Read more →

Course notes for Applied Bayesian Modeling and Prediction (STAT 768) at Kansas State University for Spring 2023 semester […] This document contains the course notes for Applied Bayesian Modeling and Prediction (STAT 768) at Kansas State University. During the semester we will cover the basics such as the Bayesian model development, implementation, checking, and inference/prediction. We will focus on formulating and implementing bespoke Bayesian models that are tailored to answer scientific questions or applied problems ranging from environmental management to … Read more →

This bookdown contains the teaching materials for the projectcourse Financial Data Science at the LMU Munich. The files have been set up by Lisa Kaminski. [...] Here you will find the course pages for the projectcourse Financial Data Science. The course is offered regularly in the summer term and aims at providing in-depth knowledge about the programming language Python and its most important libraries for data analysis. Furthermore, the course introduces the topic of database management and the process of retrieving, aggregating and manipulating data using SQL. Students will learn to ... Read more →

These notes cover the second semester in a two-semester sequence on statistical modeling. It focuses on constructing, drawing conclusions from, and critiquing probabilistic models. Planned topics include generalized linear models, the bootstrap, hierarchichal models, nonparametric estimation, and generalized estimating equations. [...] In this collection of notes, we briefly outline the main thrust of this course by discussing (at a high level of generality) the topic of probabilistic modeling. Throughout this course we will focus on the application of probabilistic modeling with an eye ... Read more →

Welcome | Teachvatory documentation. […] This is a small reference book to learn how to install, edit, and deploy the app Teachvatory. Teachvatory is an app created by Dan Levy and a group of his former students and teaching fellows. Its goal is to provide an easy visualization of student’s performance in the courses that Dan teaches. This book has 5 chapters that covers most of the development process for the app: The first chapter explains how to download and install the app locally. The second chapter explains the app architecture, which follows the R package convention and uses the … Read more →

Survey design and analysis using R. […] This is a compilation of notes from my study of survey design and analysis. I completed Data Camp courses Survey and Measurement Development in R (Mount, n.d.) and Analyzing Survey Data in R (McConville, n.d.), then moved on to Thomas Lumley’s Complex Surveys: a guide to analysis using R (Lumley 2010). The following resources are also helpful. Only simple random sample survey designs can be analyzed with with normal statistical test functions - complex survey designs require special treatment. The survey package (Lumley 2021) handles both simple and … Read more →

Course Materials […] Hello and Welcome to this introductory Lecture in Probability! These Course Notes are a complement to the Lecture Probability I. The Lecture is divided in the following Chapters, and each Chapter contains several themes. The Lectures will take place in the room MR280 on Thursday from 12h15 to 14h00. For those students who cannot attend the lectures, a recording will be available on Mediaserver. Seminars will take place on Thursday from 16h15 to 18h00 in the room MS130. The seminars are not recorded. Every two week (starting from the third one), tutorials will take place … Read more →

Learn Mathematics and Computer Science with Isabelle […] The goal is to develop deep understanding in mathematics while at the same time also learn Isabelle and its standard library. I couldn’t find any existing resources like this so I decided to write one myself. Our goal is to first learn the basics of Isabelle without getting too deep into irrelevant details. Then we should move on to studying actual mathematics as quickly as possible. Any remaining details of Isabelle will be introduced as needed. We will go though all of undergradute maths courses (and touch on graduate level topics) … Read more →

This is an accompanying book for the R workshops for the Methods II and Methods II Advanced courses in the Master’s programme in peace and conflict studies at Uppsala University. […] Welcome to this draft online edition of The R Researcher’s companion. This is a project that has been in the works for a long time and is finally, hopefully, coming together. In this ‘book’ (really, it is just a collection lecture and workshop notes) my ambition is to convince you that R is not an obstacle to be overcome. Rather, I want to convince you that knowing R is a gateway to a fantastic new world of … Read more →

These are the course notes for the Machine Learning module (MATH42815) at Durham University. […] Welcome to the material for the second half of the Machine Learning module (MATH42815) at Durham University. These pages consist of relevant lecture notes that will be updated as the course progresses. I would recommend that you use the HTML version of these notes (they have been designed for use in this way), however, there is also a pdf version of these notes. In this second half of the module, we will first look into the simple yet powerful tree-based models and then dive into the famous yet … Read more →

Statistical modelling […] This is an evolving set of lecture notes for the course From Data to … Read more →

This book is an attempt to re-express the code in the second edition of McElreath’s textbook, ‘Statistical rethinking.’ His models are re-fit in brms, plots are redone with ggplot2, and the general data wrangling code predominantly follows the tidyverse style. […] This ebook is based on the second edition of Richard McElreath’s (2020a) text, Statistical rethinking: A Bayesian course with examples in R and Stan. My contributions show how to fit the models he covered with Paul Bürkner’s brms package (Bürkner, 2017, 2018, 2022j), which makes it easy to fit Bayesian regression models in R (R … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::bs4_book, set in the _output.yml file. [...] This interactive online book provides an introduction to the world of geographic information system (GIS). At the end of each chapter references as well as free and paid courses are listed which will allow you to explore GIS ... Read more →

This is an undergraduate-level seminar on international relations. I started teaching this course at the University of Salzburg in 2022. The intention of maintaining this website is to facilitate my teaching and to keep track of the resources. Feel free to use this website for education purposes. If you see any errors or have suggestions, please do let me know (contact me). The syllabus of this class can be downloaded here: IntroIR. Please right click on the link and download or open it in a new tab. I will post the slides of each week … Read more →

This is a graduate-level seminar on US foreign policy. I started teaching this course at the University of Salzburg in 2020. The intention of maintaining this website is to facilitate my teaching and to keep track of the resources. Feel free to use this website for education purposes. If you see any errors or have suggestions, please do let me know. The syllabus of this class can be downloaded here: US foreign policy. Please right click on the link and download or open it in a new tab. Here you can find the slides for each … Read more →

Online version of Design and Analysis of Experiments and Observational Studies using R […] This is the free website for Design and Analysis of Experiments and Observational Studies using R. A hardcopy of the book can be purchased from Routledge. This book grew out of course notes for a twelve-week course (one term) on the Design of Experiments and Observational Studies in the Department of Statistical Sciences at the University of Toronto. Students are senior undergraduates and applied Masters students who have completed courses in probability, mathematical statistics, and regression … Read more →

Cross-Platform Journalism - B.A. Seminar at LMU Munich, Fall 2022/2023 […] This online tutorial will accompany the seminar “Cross-Platform Journalism: News use, content, and effects”. It is part of the undergraduate course “Communication Studies” (LMU, Department of Media and Communication). At the moment, the course is expected to take place via in-person meetings every Tuesday 08:00-10:00 am in room B109, Edmund-Rumpler-Strasse 13. Please note that this may change in light of COVID-19 developments. You will find all necessary information on the seminar’s structure, important dates and … Read more →

This book is supposed to introduce the reader (i.e., you) into some fundamental techniques for computational social science research: acquiring online data, agent-based modeling, and text mining. […] Dear student, if you read this script, you are either participating in one of my courses on digital methods for the social sciences, or at least interested in this topic. If you have any questions or remarks regarding this script, hit me up at felix.lennert@ensae.fr. This script will introduce you to three techniques I regard as elementary for any aspiring (computational) social scientist: the … Read more →

Guide for the Course ECON371- Political Economy of Development […] The objective of this compendium is to provide a study guide for the course: ECON371- Political Economy of Development. This guide does not replace any of the books used in the course nor the lecture notes. However, it will help you to go through it, so you can understand and critically think about the material that we will cover in the course. The best way to contact me is through email. I am usually very fast at responding to my students, but plan for a 24-hour delay in my response during the weekdays. I usually do not … Read more →

This is the course website for the fall 2022 PS 811 Class […] This is the class website for PS-811 for the 2022 fall semester. PS 811 is a one-credit hour, pass/fail course taught to incoming Ph.D. students in the political science department. This course will be taught remotely; however, RM 3218 in the Social Sciences building has been reserved if you would like to listen to lecture and work on the problem sets together in the room during the class time window. In modern political science, you will be required to perform or understand quantitative research. Often this research is conducted … Read more →

This section of the OMICS course will cover NGS sequencing from FASTQ reads to differential RNA analysis. The general workflow is shown above. We will use a dataset from Saccharomyces cereviseae. The bookdown version of these pages is published at this site … Read more →

This is the textbook for applied biostats in fall of 2022 taught by yaniv brandvain […] In the summer of 2020, the world was on fire – COVID was raging, we – especially in Minnesota – were processing the murder of George Floyd and the subsequent uprising etc, the future was unclear. At that point teaching was likely to be entirely online, and I decided to write a digital book for my course see the first edition of my book here. I didn’t really know what I was doing or what my vision was (and to some extent I still do not). There were hiccups: some strangeness in rendering etc, typos, last … Read more →

This a brief overview of how to use biology for inform decision making for environmental grand challenges we face globally. […] Welcome to biology for environmental management. The goal of this course is to examine global grand challenges that we face through the lens of solutions from science. Science can be magical because it provides us with incredible outcomes in so many domains of the human endeavour. Here, we work to find the magic spells from science for the environment to promote the well-being of people and the planet. There is an opportunity for us to mobilize science to do social … Read more →

A guide to the tidyverse for POL345 Students. […] POL345 is often Princeton students’ first foray into the programming language R. Through POL345, students gain an introductory overview of R, and programming generally, to conduct basic data analysis on their own. However, many further courses (SML201, SOC306, POL346), along with industry users of R, use the tidyverse instead, a “language” within R to conduct clean, readable data analysis. This book seeks to bridge that gap, revisiting each of the POL345 handouts using the tidyverse to introduce students to this “language within a language”. … Read more →

This book is the companion to the MA course Data Visualization at IBEI. […] In an experiment conducted by researchers from University College London (Mcmanus and Gesiak 2014), 277 participants were asked to look at several pairs of paintings: one of the pairs was an original by abstract painter Piet Mondrian, and the other was fake version that closely resembled it.1 The participants where asked: When looking at the pictures you should decide overall which you thinks looks better, in that it looks nicer, it looks better organised, or it looks better balanced. The results suggested that … Read more →

MUED 540 […] Generally, research is systematic inquiry through chosen epistemologies and methodologies. Quantitative methods can include experimental design, which is noted by random assignment (e.g., effectiveness of a drug in a double-blind study), quasi-experimental design, which is noted by previously attributed treatments or groups (e.g. effectiveness of a teaching method across sections of a course, or include observation or description (e.g., number of students who took their instruments home on a given day/cars that stopped at a stop sign and turned right). Quantitative research can … Read more →

Materials for the three day camera trap data management and analysis course […] The number of projects employing camera traps to understand ecological phenomena is growing rapidly – as are the number of statistical tools to analyze the resultant data. Consequently, the management and analysis of camera trap data can seem complex and overwhelming. This course aims to guide participants in effective ways to store, manipulate and analyze camera trap data within the R statistical environment. The idea for this course was born out of the realization that many of the analytical frameworks we … Read more →

Notes for the R Crash Course for the students of the Economics and Finance degree […] You are reading the lecture notes of the R Crash Course. This introducton to R software is for the students from the Master in Economics and Finance (EF) at University of Bergamo (academic year 2022/23). Enjoy the journey! In the following lecture notes, this font (with grey background) represents R code. The following is an example of R code with the corresponding output. These notes are written in RStudio by using bookdown (https://bookdown.org/). For errors, typos, suggestions, … write to … Read more →

Statistics 1 - exercises 2022 […] This book is being prepared for the students of Statistics course at Gdańsk University of Technology. Dear students. For the purposes of our class I am testing bookdown (http://bookdown.org). We will see how it … Read more →

STAT101 Tutorials […] Weekly tutorial exercises (starting in Week 1) are assigned from the required course textbook, Lock, Lock, Lock Morgan, Lock and Lock, Statistics: Unlocking the Power of Data, 2nd Edition, (2017) Wiley. You can find details on where to source the textbook in the Course Information section. You do not need to get the textbook exercises finished or correct during the tutorial, we just want you to give them your best attempt. However, mastering these concepts is strongly recommended in preparation for the final exam. Your attempts at the textbook exercises will not be … Read more →

This is the workbook you will use for the Quantitative Methods in the Social Sciences lab sessions. […] Welcome to the Quantitative Methods in the Social Sciences lab! This workbook is targeted to University of Glasgow students enrolled in the Undergraduate Quantitative Research Methods course of the School of Social & Political Sciences. The activities are designed for RStudio Cloud. The book was written using R bookdown package based on the GitHub repository: https://github.com/rstudio/bookdown-demo. The online version of this book is licensed under the Creative Commons … Read more →

Course notes for Biometry. […] You belong in this course and in the field of data science! We are excited to learn with each and every one of you. We are here to support your success. We have no doubt that you will do great things with the data science skills you learn in this course because of who you are as a person and the values you bring with you from your culture, family, and life experiences. We want to invite you to bring your whole self into our data science learning community. Each of you brings cultural assets and personal perspectives that will allow you to make unique … Read more →

An accessible introduction to R that doesn’t assume programming experience […] This course has been adapted from ex-Matilda Marius Mather’s R for Academics course. This session is designed to get people started with a programming approach to data management and analysis. We’ll be using R, but a lot of the concepts in R will transfer to other software. … Read more →

This is the companion book of the course ED 101 Making Sense of Data offered in the Department of Education at John Carroll University. […] Step 1: Log in to your JCU (google) email account Step 2: Click here or type the short URL: https://colab.to/r to open a new R notebook in Colab. To check if this is really an R notebook (instead of python), click on the “Runtime” tab, then click on “change runtime type.” And you should see this: Now hit “Cancel” and go back to the notebook. Try typing a line of code in the cell, such as: To run this line of code, either hit the play button: Or … Read more →

This is a minimal example of using the bookdown package to write a book. set in the _output.yml file. The HTML output format for this example is bookdown::bs4_book, [...] This book is designed to help students develop an intuition for intermediate microeconomic concepts using interactive plots and animations. By no means is this a replacement for your course textbook; it should serve as a supplement that builds confidence towards more complex topics. This book is still a work in progress, so there is a chance you will find typos or inconsistencies. Please let me know if you find any at ... Read more →

Matured Big Data Analytics provides new product, consumer, market, and competitor insights in a real-time fashion. […] This is an early draft for my Marketing Research course (MKTG4000) at CSUB. The free mannual (outline) is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. “I do not care which tool or language you use for assignments, as long as you do your own work.” You can access the script on the course website. You don’t need to actually know to code in R for this course although you will be able to pick up this skill slowly when we … Read more →

This is a graduate-level seminar on interstate conflict. I started teaching this course at the University of Salzburg in 2022. The intention of maintaining this website is to facilitate my teaching and to keep track of the resources. Feel free to use this website for education purposes. If you see any errors or have suggestions, please do let me know (contact me). The syllabus of this class can be downloaded here: Conflict. Please right click on the link and download or open it in a new … Read more →

This book serves as an accompanying script for the R sessions of the 2022 Summer Institute for Computational Social Science (SICSS), taking place at the Institut Polytechnique de Paris. […] Dear student, if you read this script, you are either participating in the SICSS itself or came across it while browsing for resources for your studies. In any case, if you find inconsistencies or mistakes, please do not hesitate to point them out by shooting an email to felix.lennert@ensae.fr. This script will introduce you to the automated acquisition and subsequent quantitative analysis of text data … Read more →

Course content for ISTA 321 - Last updated for Summer 2022 […] Welcome to ISTA 321 - Data Mining! The goal of this class is to teach you how to use R to make informed inferences and predictions from large datasets using a variety of methods. This requires a mixture of many skills including programming, data exploration and visualizations, statistics, algorithms, machine learning, model validation, and general data wrangling. We don’t do these things in isolation, but instead do them with a goal of answering a question, thus being able to apply this knowledge to make a data-driven decision … Read more →

Arthur Small This course introduces tools and concepts of literate programming. Literate programming is an approach to creating documents that smoothly integrates data, code, and narrative writing. All analysts need to present their results in multiple formats: articles, slide decks, web sites, and so on. Traditional workflows for creating and publishing documents rely heavily on manual workflows, e.g., copy-and-paste. Traditional workflows are poorly suited to data-intensive analytic projects. This course will provide an introduction to an entirely different and better approach to scientific … Read more →

Our work for the course “Project in Data Analytics for Decision Making” is to predict the credit risk linked to customers for our client, a German bank. To do so, we used the CRISP-DM method (CRoss Industry Standard Process for Data Mining): Business understanding: Credit risk is defined as the risk of loss resulting from the failure by a borrower to repay the principal and interest owed to the lender. By performing a credit risk analysis, the lender determines the borrower’s ability to meet debt obligations in order to cushion itself from losses. It is therefore important to efficiently … Read more →

Script for the seminar ‘Big Data and Social Science’ at the University of Bern. […] The present document serves both as slides and script for the workshop/seminar Computational Social Science. This seminar is taught by Paul C. Bauer at the University of Mannheim (Spring Semester 2022). The material was developed by Paul C. Bauer and heavily draws on material developed by other people (see script). Any original material and examples is licensed under a Creative Commons Attribution 4.0 International License. For potential future versions of the course see my website: www.paulcbauer.eu. If you … Read more →

Notes for the R labs of the MLFE course @ Unibg […] You are reading the lecture notes of the R labs for the Machine learning for Economics (MLFE) course at University of Bergamo (academic year 2021/22). The MLFE course is the second module of the Coding for Data Science course. The MLFE R labs are designed for students who already have some experience with R programming thanks to the first module of the Coding and Machine Learning course. Click here and here to access the R lab notes of the first module regarding introduction to R language and the tidyverse package. Enjoy the journey! … Read more →

The book covers material taught in the Johns Hopkins Biostatistics Advanced Statistical Computing course. I taught this course off and on from 2003–2016 to upper level PhD students in Biostatistics. The course ran for 8 weeks each year, which is a fairly compressed schedule for material of this nature. Because of the short time frame, I felt the need to present material in a manner that assumed that students would often be using others’ software to implement these algorithms but that they would need to know what was going on underneath. In particular, should something go wrong with one of … Read more →

This website contains lessons and labs to help you code categorical regression models in either Stata or R. […] This website houses all the information you need learn the basics of coding a number of different categorical and count models in Stata and R. It will not contain all the information taught in class, but will allow you to bridge that knowledge into running these models on your own. The Stata labs on this website were adapted from materials by Ewurama Okai. This course will contain 8 labs and an optional review lab at the end of the course. Our lab sessions will alternate between … Read more →

Notes for the R labs of the AIMLFF course @ Unibg […] You are reading the lecture notes of the R lectures for the AI and Machine Learning for Finance (AIMLFF) course at University of Bergamo (academic year 2021/22). See here for more details. In this notes R programming language for data science will be introduced (with respect to data manipulation, data visualization and communication and implementation of machine learning methods). For this part I suggest the following on-line book: Enjoy the journey! In the following lecture notes, this font (with grey background) represents R code. The … Read more →

A Short Course on Nonparametric Curve Estimation. MSc in Applied Mathematics. EAFIT University (Colombia). [...] This course is intended to provide an introduction to nonparametric estimation of the density and regression functions from, mostly, the perspective of kernel smoothing. The emphasis is placed in building intuition behind the methods, gaining insights into their asymptotic properties, and showing their application through the use of statistical software. The software employed in the course is the statistical language R and its most common IDE (Integrated Development ... Read more →

This module will teach the basics of data analytics using R. […] Welcome to the University of Minnesota’s Data Analytics with R module - presented by Optum. In this module, you will perform a case study designed to replicate a real-world analytics process that you may encounter in your future career as an actuary or data analyst. The module will be broken down into 7 chapters that you will complete over the course of 4 class periods (roughly 2 weeks time). As you work through the chapters, you will learn how to import data, summarize and visual data, create a predictive model, and interpret … Read more →

A gitbook-style website authored for the course HE9091: Principles of Economics. […] This is a gitbook-style website authored for the SBS course BS0005: One … Read more →

This manual is generated using Bookdown for internal use only […] This course is designed as a Computational Statistics Laboratory (CSL) comprised of 29 experiments selected from the Statistical Courses in INMCA Programme. Details of experiments and the instructions regarding creation & submission of laboratory reports are explained in this introductory chapter. Familiarization of environments in R. Perform simple arithmetics using R. Perform basic R functions. Use various graphical techniques in EDA. Create different charts for visualization of given set of data. Draw a Pareto chart to … Read more →

ANU Morange Class […] This is course overview for FINM2002/7041 ANU MOrange Class. Please check the course schedule … Read more →

This website contains lessons and labs to help you code linear regression in either Stata or R. […] Welcome to your guide to learning linear regression in Stata and R. This website houses all the information you need learn the basics of coding linear regression in Stata and R. It will not contain all the information taught in class, but will allow you to bridge that knowledge into running linear regressions on your own. The Stata labs on this website were adapted from materials by Ewurama Okai. This is a 10-week course with 9 labs. Each lab will focus on some topic related to coding linear … Read more →

Hello. This is your course reader for DATA 3320 Data Science Methodology and Applications. Use the chapters of the reader to guide your work through each project. You will find the associated R Markdown file for each project on … Read more →

How to do comparative methods for evolution and ecology […] This book was created as part of my PhyloMeth class, which focuses on sensibly using and developing comparative methods. It will be actively developed over the course of Spring 2017, so if you don’t like this version (see date above), check back soon! The book is available here but you can fork it, add issues, and look at raw source code at https://github.com/bomeara/ComparativeMethodsInR. [Note I’ll be changing the name of the repo eventually; the course is largely in R (not entirely) but of course many key methods appear in other … Read more →

Add description […] Outline of preface (preliminary and incomplete). June 21, 2016. I started teaching the course Introduction to Financial Econometrics at UW in 1998. Motivation was to teach more statistics and quantitative methods to economics majors. I found that combining statistics topics with finance applications was very effective and popular. Early classes used Microsoft Excel as the main software tool (R was not around then). Experience with Excel was, and still is, in high demand by employers in the finance industry. However, Excel is not a good tool for doing statistics. In early … Read more →

Course Materials […] Hello and Welcome to this introductory Lecture in Probability! These Course Notes are a complement to the Lecture Probability I. The Lecture is divided in the following Chapters, and each Chapter contains several themes. The Lectures will take place over Zoom on Thursdays from 12h to 14h. Lectures will consist on a presentation of the contents of the class. During the class, there will be some exercises. You are invited to download the app Wooclap Q&A sessions on the exercises will take place on Thursdays from 16 to 18 over Zoom. We will soon be making available a … Read more →

This is a graduate-level seminar on economic statecraft. I started teaching this course at the University of Salzburg in 2020. The intention of maintaining this website is to facilitate my teaching and to keep track of the resources I use. Feel free to use this website for education purposes. If you see any errors or have suggestions, please do let me know. The syllabus of this class can be downloaded here: EconomicStatecraft. Please right click on the link and download or open it in a new … Read more →

This is a graduate-level seminar on Chinese foreign policy. I started teaching this course at the University of Salzburg in 2021. The intention of maintaining this website is to facilitate my teaching and to keep track of the resources. Feel free to use this website for education purposes. If you see any errors or have suggestions, please do let me know. The syllabus of this class can be downloaded here: CFP. Please right click on the link and download or open it in a new … Read more →

Notes for the R labs of the PSBF course @Unibg […] You are reading the lecture notes of the R labs for the Probability and Statistics for Business and Finance (PBSF) course at University of Bergamo (academic year 2021/22). R is a great programming language especially designed for statistical analysis and data visualisation. The PSBF R labs are designed for those who don’t have any programming background. It will be a step-by-step path; at the end you will have the basic R knowledge for analysing financial time series. Enjoy the journey! In the following lecture notes, this font (with grey … Read more →

Lab materials, examples, and other data for use in Dr. Circo’s CJST 4557 course. […] Welcome to CJST 4557 - Crime Mapping and Analysis! Before we get started, let’s address a few common questions about this course, as well as some more general questions about crime mapping in general. In general I have a few major goals for you in this course: This is intended to be a general course covering common methods in crime analysis, some of the tools that go along with it, and a theory-based discussion of how to best implement strategies for crime prevention. This will be a mix of both lab-based … Read more →

This book is suitable for an introductory course of data analytics to help students understand some main statistical learning models, such as linear regression, logistic regression, tree models and random forests, ensemble learning, sparse learning, principal component analysis, kernel methods including the support vector machine and kernel regression, etc. Data science practice is a process that should be told as a story, rather than a one-time implementation of one single model. This process is a main focus of this book, with many course materials about exploratory data analysis, residual analysis, and flowcharts to develop and validate models and data pipelines. Read more →

Text as Data Methods in R - M.A. Seminar at IKMZ, HS 2021 […] This online tutorial will accompany the seminar “Text as Data Methods in R - Applications for Automated Analyses of News Content”. It is part of the M.A. “Internet & Society” (IKMZ, University of Zurich, HS2021). At the moment, the course is expected to take place via in-person meetings every Tuesday 10:15-12 am in room BIN-1-D.25. Please note that this may change in light of new COVID-19 developments. You will find all necessary information on the seminar’s structure, important dates and assessments/evaluations via OLAT. Please … Read more →

A gitbook-style website authored for the NTU course BS1005 / CM1051: Biochemistry I […] This is a gitbook-style website authored for the AY 2020 - 2021 edition of BS1005 / CM1051: Biochemistry I - a 13-week core module worth 3 AUs for SBS students. More information will be shown on the following sections of this chapter. Nonetheless, to navigate this website, note the following: The “f” key on your keyboard can be used to bring up a search bar on the top of the sliding menu to the left of your computer’s screen. This may come in handy if you are looking for a specific term under a specific … Read more →

Notes for the R labs of the C4DS course @ Unibg […] You are reading the lecture notes of the R lectures for the Coding for Data Science (C4DS) course at University of Bergamo (academic year 2021/22). C4DS is the first module of the course named Coding and Machine Learning (see here for more details). The C4DS R lectures are designed for students who already have a programming background thanks to the first part of the C4DS course dedicated to Python. In this part of the module we will introduce R programming language for data science (including data manipulation, data visualization and … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] This is a website I (Kevin) made using the R package bookdown for the NTU SBS course BS2002: Microbiology. More information about this site will be shown in the following sections. This website was authored for the semester 1, NTU academic year 2021 - 2022 edition of BS2002. At the time of your visit, weeks, months, or even years may have passed (hence rendering the website’s content outdated if no edits are made). ... Read more →

This a website built for studying BS2002: Physiology […] This is a bookdown site generated for the NTU SBS course BS2001: Physiology (a core module for BS students). More information about this website will be shown in the following sections: At the time of your visit, weeks, months, or even years may have passed. Hence, the material presented on this website may no longer be up-to-date for future iterations of this course. Furthermore, this bookdown site is not a substitute for skipping lectures and / or tutorials. The curator encourages current and prospective students to be responsible … Read more →

This is a bookdown site authored for the NTU SBS course BS2003: Biochemistry II […] This is a bookdown site made for the NTU SBS course Biochemistry II. BS2003: Biochemistry II is a core module in the SBS curriculum typically taken by year 2 undergraduates during the first semester. The course is jointly taught by two SBS faculty members: [Gerhard Grueber] and [Ardina Grueber]. While the author has made a considerable effort to ensure that the contents of this bookdown site are as faithful to the course’s content, errors may still arise in the site’s nonetheless. Hence, should you … Read more →

R @ Ewha (Sunbok Lee) […] Hi everyone, welcome to the course. This is the introduction to R course at Ewha Womans University. R is a great programming language for statistical analysis and data science. I hope you enjoy R in this course and find many useful applications for your own field. This course is designed for students who don’t have any programming background in social science. In this lecture note, this font represents R commands, variable names, and package names. In order to maximize your learning in this semester, you should read the weekly reading assignment in our … Read more →

This is the full book written in preparation for my course on “Survey data in the field of economy and finance” given at the University of Luxembourg (Master 2 ‘Economy and Finance’). […] Counting a population through censuses has been a long established practice which dates back to thousand years BC. Babylonians, Egyptians, Romans etc. used to resort to population censuses to support important economic decisions in terms of taxation, labour force scaling, food distribution etc. Censuses are usually regarded as error-free data sources leading to statistics with highest accuracy. On the … Read more →

Introduction to R for Econometrics […] This is a short introduction to R to go with the first year econometrics courses at the Tinbergen Institute. It is aimed at people who are relatively new to R, or programming in general.1 The goal is to give you enough of knowledge of the fundamentals of R to write and adapt code to fit econometric models to data, and to simulate your own data, working alone or with others. You will be able to: read data from csv files, plot it, manipulate it into the form you want, use sets of functions others have built (packages), write your own functions to compute … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::gitbook, set in the _output.yml file. [...] Hi everyone, welcome to the course.This is the introduction to R course at Ewha Womans University. R is a great programming language for statistical analysis and data science. I hope you enjoy R in this course and find many useful applications for your own field. This course is designed for students who don’t have any programming background in social science. In this lecture note, this font represents R commands, ... Read more →

This course provides an overview of skills needed for reproducible research and open science using the statistical programming language R. Students will learn about data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations, general linear models, and reproducible workflows. Learning is reinforced through weekly assignments that involve working with different types of data. Read more →

R Training Manual […] This course aims to teach some of the key concepts to help learners use R as a tool to inform data-driven decision-making for epidemiological analysis. Many of the examples in this course are from the Epidemiologist R handbook, a resource developed by epidemiologists working across the world. The handbook is in invaluable resource for all levels of R users but the focus for this course will be on the early learning concepts most frequently used by epidemiologists. The mapping content has been provided by the afrimapr project. The course has been designed in … Read more →

PSY317L & PSY120R Guidebook […] This book is written to help students enrolled in the University of Texas at Austin Introduction to Statistics for the Behavioral Sciences (PSY317L) course or R Programming for Behavioral Sciences (PSY120R) led by Professor James Curley. We hope that the book will be a useful resource to help you learn both R and statistics. If you have any suggestions for improvements, please get in touch with Professor Curley. This is in between a textbook and a study guide. We’re trying to build materials that will enable students to quickly find what they’re looking for … Read more →

This is an introduction to Game Theory. The project started when I sat in on Tobias Heinrich’s class (POLI 725: International Conflict) in Fall 2019. I was given the opportunity to provide an introduction to basic game theory concepts and methods. Thank again to Toby for the trust and the opportunity. My intention is to build upon the short introduction and potentially expand it into course, with a heavy focus on models used in International Relations. If you have suggestions or find any errors, please do shoot me an … Read more →

This book contains all the content for, and information relevant to, the Data Visualization for Conservation course (7 July - 20 July 2021). […] Welcome to the Data Visualization for Conservation course! In this course we are going to be talking about data visualisation or ‘dataviz.’ We will spend time talking about the importance of data visualisation, learning how to produce custom visualisations using the ggplot2 package, as well as an important theory of data management and storage. The path of learning is never-ending, and we do not want you to imagine that this course can teach you … Read more →

Data Visualization Workshop […] “The greatest value of a picture is when it forces us to notice what we never expected to see.” — John W. Tukey This bookdown was created for a 2-hour minicourse on data visualization for students in the 2021 UCSF Science and Health Education Partnership Highschool Internship Program (SEP HIP). Interns – welcome! You can use this website and all of its resources during and after the workshop. Anyone else – welcome, also! Feel free to use what you find here for educational purposes. If you do, please cite this book and/or include the URL where you use these … Read more →

This is a course handbook written by Bodong Chen for his SNA course at UMN. […] This site is built for a course titled CI 8371 - Applied Social Network Analysis in Education, taught by Prof. Bodong Chen at the University of Minnesota. Content on this site will be actively built and refined throughout the Spring 2021 semester. While the course is titled Social Network Analysis in Education, this course is not limited to social networks or to education. We will broadly examine social, information, and artificial networks in a variety of learning contexts including schools, workplace, and … Read more →

This is a website with tutorials for the Social Life of Neighborhoods offered in SOC 176/276. […] The following is a series of tutorials specifically designed for The Social Life of Neighborhoods (SOC 176/SOC 276/AFRICAAM 76B/CSRE 176B/URBANST 179) course. The course assignments and final story map require collecting and analyzing information about neighborhoods and other urban spaces. In the tutorials, you will be introduced to tools that will allow you to gather, process, and visualize data so that you can complete the assignments and create your own story map. No prior experience or … Read more →

Script for the seminar ‘Big Data and Social Science’ at the University of Bern. […] The present document serves both as slides and script for the workshop/seminar Computational Social Science: Theory & Applications. This seminar is taught by Paul C. Bauer at the University of Mannheim (Spring Semester 2021). The material was developed by Paul C. Bauer and heavily draws on material developed by other people (see script). Any original material and examples is licensed under a Creative Commons Attribution 4.0 International License. For potential future versions of the course see my website: … Read more →

Introduction to R - tidyverse […] This document contains the material covered in the Introduction to R (tidyverse) course taught at the Walter and Eliza Hall Institute of Medical Research. The course is taught to biomedical scientists, but the material and the teaching examples are very broad. Skills taught in this workshop can be applied to many disciplines in academia and industry. There is no assumed knowledge of R or other computer languages - we start from scratch. Chapters 1 through 5 make use of popular (non-biological) teaching data sets available through R. Chapters 6 onwards … Read more →

This is a short workbook designed to help students in the MSSE program feel better about how to talk about the data generated from their capstone projects. […] This short book encapsulates everything that we used to cover in an in-person short course, the entire point of which was to collaboratively improve our ability to talk about, describe, and display data. We began the course as a deep-end-of-the-pool method of trying to take away some of the concern and fear that people have when it comes to writing about their Capstone data. Our goals are very simple: The only requirements for this … Read more →

Course materials for the Data Science Boot Camp, Weldon Cooper Center for Public Service, University of Virginia June 8-10, 2021 […] This boot camp is designed to help research assistants rapidly to become productive doing data science as a member of the Cooper Center team. The mini-course offers introductory training in how to do data science as a member of a team. It also provides an orientation to the projects, resources, and house styles that are specific to the Cooper Center. R4DS: R for Data Science by Hadley Wickham and Garrett Grolemund. An excellent introduction, available for free … Read more →

A Crash Course in Geographic Information Systems (GIS) using R […] There is an assumption of some previous experience in R with this tutorial. If you have not used R before I would start with Chapter 1 of the free, and excellent textbook R for Data Science. The GIS operations in R from the sf package are designed to integrate well with the tidyverse suite of R packages. We will make use of some basic functionality from the dplyr package and will be using pipes (%>%) to sequence multiple operations. If you are unfamiliar with dplyr and pipes I would go through the base vignette before … Read more →

Notes for the R labs of the MLFE course @ Unibg […] You are reading the lecture notes of the R labs for the Machine learning for Economics (MLFE) course at University of Bergamo (academic year 2020/21). The MLFE course is the second module of the Coding and Machine Learning course. The MLFE R labs are designed for students who already have some experience with R programming thanks to the first module of the Coding and Machine Learning course. Click here to access the R lab notes of the first module regarding introduction to R language and the tidyverse package. Enjoy the journey! … Read more →

This is a bookdown in which the second part of the project of the subject advanced regression and prediction of the Master’s Degree in Statistics for Data Science has been carried out […] Currently Machine Learning (ML) techniques are applied in an infinity of fields to obtain knowledge from data. Among these fields today we can highlight the appearance and effect of the coronavirus disease (COVID-19) in all aspects of society. That is why, by completing this second part of the advanced regression and prediction course, it is intended to use the techniques learned during the practical and … Read more →

Business Statistics […] The course purpose is to introduce a formal framework for analyzing real life business problems with actual data, so that students can improve their understanding of the circumstances in which statistical techniques should be used and how to apply statistics to practical business situations. The entire course is supported with many case studies and worked-out examples. In particular, statistical techniques are grouped in sections covering applications in the field of decision making, business forecasting, quality control, and commonly used descriptive and inferential … Read more →

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] The goal of this course is to introduce students to the structure and functions of international political and economic organizations. Particular attention to the United Nations and its specialized agencies, and to emerging regional communities. We look in depth at the United Nations, the World Trade Organization, international courts, and the international financial institutions, as well as other international organizations in comparative terms. Students … Read more →

Notes for the R labs of the R4CDS course @ Unibg […] You are reading the lecture notes of the R lectures for the Coding for Data Science (C4DS) course at University of Bergamo (academic year 2020/21). R is a great programming language especially designed for statistical analysis and data visualisation. The C4DS R lectures are designed for students who already have a programming background thanks to the first part of the C4DS course dedicated to Python. In the 5 lectures dedicated to R I will present you the basics of R for data manipulation, analysis and plotting. Enjoy the journey! In the … Read more →

An introduction to quantitative research in science, engineering and health (including research design, hypothesis testing and confidence intervals in common situations) […] This book is an introduction to quantitative research in the scientific and health disciplines. The whole research process is introduced, from asking a research question to analysis and reporting of the data. The focus, however, is on the analysis of data. This book was originally prepared for use with the course SCI110 Science Research Methods1 at the University of the Sunshine Coast (USC). This name is grammatically … Read more →

This course is intended for educational puposes & preparing Data analysts for Exam DA-100: Analyzing Data with Microsoft Power BI : Creating reports […] This course is intended for educational purposes & preparing Data analysts for Exam DA-100: Analyzing Data with Microsoft Power BI : Creating reports All the needed resources to follow along can be found here https://github.com/Mkassem16/NycTaxiPBI. Creating basic PowerBI reports knowledge is prerequisite for this course. By the end of this course Data Analysts should be able to: + Customize Report pages. + Decide for appropriate … Read more →

Course notes for applied Biostats. […] Key links: Canvas, Detailed schedule in progress, Rough schedule. I begin this writing in early Sept 2020, and am thinking about you, my students for the fall 2021 term. Currently much is uncertain — we are beginning a term in the wake of George Floyd’s murder [among many others], which has resulted in protests, uprisings, rioting and armed confrontation between protesters, armed citizens and police. We are in the midst of an uncertain economy, and a vicious presidential election. Most of us feel isolated as COVID has pushed most of us home, some of us … Read more →

Introductory text to a couple of commonly used Machine Learning techniques and how they are performed in R […] This book is intended to be a reference for frequently used Machine Learning techniques and how they can be performed in R. Writing this work started in summer 2020 and new chapters are and will be added over time. It is written with a view to students pursuing a Machine Learning course in a pre-graduate or graduate program. It is not intended to be an exhaustive textbook on all possible Machine Learning … Read more →

STA 444/5 - Introductory Data Science using R […] This book is intended to provide students with a resource for learning R while using it during an introductory statistics course. The Introduction section covers common issues that students in a typical statistics course will encounter and provides a simple examples and does not attempt to be exhaustive. The Deeper Details section addresses issues that commonly arise in many data wrangling situations and is intended to give students a deep enough understanding of R that they will be able to use it as their primary computing resource … Read more →

This e-book accompanies the course HE-802 in the MS in HPEd program at MGHIHP (http://mghihp.edu/mshped). HE-802 is a statistics course that equips students to analyze healthcare and/or behavioral data in R. […] This online e-book is the main resource to guide you through the course HE-802 in the MS in HPEd program at MGHIHP in the Spring 2021 semester. Each chapter contains reading (or links to reading) that you should do as well as an assignment that you should complete and submit by the deadline in the course calendar. My name is Anshul Kumar and I am the author/preparer of this e-book. … Read more →

This book contains the texts to be annotated fo the course Introduction to Korean Literature at Sogang University, 2021. […] The texts on this website are formatted in HTML and they will adapt to your screen size. So, you can easily read them on a desktop computer, tablet, or mobile phone. Use the menu icon on the left to access the table of contents and the < icon on the right to open the annotation sidebar. In order to annotate the texts with Hypothesis, you need to sign up at this link: https://hypothes.is/signup Once you have signed up and confirmed your email, log in using the sidebar … Read more →

Lecture notes for Calculus and Applications produced in bookdown […] These are lecture notes for the second part of Calculus and Applications first year module at the Department of Mathematics, Imperial College London. The notes are split into three parts on Fourier Transform, Ordinary Differential Equations and Introduction to Multivariate Calculus. Please refer to course Blackboard for additional materials recommended text books for further reading. These lecture notes are adobted from existing courses in our department. Part I of the course is based on the old M2AA2 course (Andrew … Read more →

Schedule, readings, and labs for GESC 258, Winter 2021. […] This is will provide week-to-week scheduling of course activities in … Read more →

This is a data science project practice book. It was initially written for my Big Data course to help students to run a quick data analytical project and to understand 1. the data analytical process, the typical tasks and the methods, techniques and the algorithms need to accomplish these tasks. During convid19, the unicersity has adopted on-line teaching. So the students can not access to the university labs and HPC facilities. Gaining an experience of doing a data science project becomes individual students self-learning in isolation. This book aimed to help them to read through it and follow instructions to complete the sample propject by themslef. However, it is required by many other students who want to know about data analytics, machine learning and particularly practical issues, to gain experience and confidence of doing data analysis. So it is aimed for beginners and have no much knowledge of data Science. the format for this book is bookdown::gitbook. Read more →

This is a working version of MMES Quant. Methods materials book. The output format for this example is bookdown::gitbook. […] You will find contained in this online document all the materials you need for this course. The book sets out for each week the readings that we will be discussing for each of the four weekly seminars. It also contains a set of take-home exercises. You can read more about the structure of the course in the next Introduction section. Note: this is a “live” document, meaning that I will continue to tweak some things. But fear not, the main course materials and assigned … Read more →

Notes for Basic Statistics I […] These are my notes for the Basic Stats I course at NYU (taught by Prof. Hassad). They will guide our lab sessions. The notes provide: Questions Note: Short-cut to the attendance file, … Read more →

R for Everyone (Advanced Analytics and Graphics) and LaTeX […] This is an introduction to R and Latex. In compiling this documents, several sources have been consulted, including Jared P. Lander’s R for Everyone, Hadley Wickham and Garrett Grolemund’s R for Data Science, Dr. Yuleng Zeng’s website, Dr. Timothy M. Peterson’s website, Havard’s Math Prefresher, the course offered by DataCamp. Chapter 3 and Chapter 4 are mostly borrowed from Dr. Yuleng Zeng’s website resources. Install the following applications: Finally, this document is to be used in-class only. As I (will) mention several … Read more →

An applied textbook on generalized linear models and multilevel models for advanced undergraduates, featuring many real, unique data sets. It is intended to be accessible to undergraduate students who have successfully completed a regression course. Even though there is no mathematical prerequisite, we still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. We believe strongly in case studies featuring real data and real research questions; thus, most of the data in the textbook arises from collaborative research conducted by the authors and their students, or from student projects. Our goal is that, after working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. Read more →

This is a textbook for use in undergraduate critical thinking courses. […] This is a textbook written primarily for my students in PHIL 1502: Critical Thinking, at Oklahoma Baptist University in Shawnee, Oklahoma. There are many good textbooks for critical thinking on the market today, so why write another one? First, all of those books were written for someone else’s course. None cover all of the topics that I would like to cover in class. Second, as I’m sure any student can attest to, textbooks are remarkably expensive, to the point that most of the world’s population cannot afford access … Read more →

This is a tutorial book with R Markdown for CASA0005 final coursework containing code and instruction of the whole analyzing process. Course name: CASA0005 Geographic Information Systems and Science Program: MSc Spatial Data Science and Visualisation Department: Centre for Advanced Spatial Analysis GitHub repository: https://github.com/LingruFeng/GIS_assessment Rpubs link: https://rpubs.com/Lingru/GIS_assessment … Read more →

This project was completed as part of our course Project in Data Analytics for Decision making. […] This work was completed as part of our course “Project in Data Analytics for Decision Making.” We produced a detailed analysis of the german database. The ultimate goal was to predict whether the customer represents a risk for the bank or not by following the CRISP-DM method. Business Understanding: A company that lends money takes risks. This assessment has always been made by humans. However, it is important to provide reliable tools for decision-making. Indeed, the consequences of a large … Read more →

Introduction to Statistical Methodology, Second Edition […] The problem with most introductory statistics courses is that they don’t prepare the student for the use of advanced statistics. Rote hand calculation is easy to test, easy to grade, and easy for students to learn to do, but is useless for actually understanding how to apply statistics. Since students pursuing a Ph.D. will likely be using statistics for the rest of their professional careers, we feel that this sort of course should attempt to steer away from a “cookbook” undergraduate pedagogy, and give the student enough … Read more →

The second semester of an Intro Stats course designed for graduate students in Biology, Forestry, Ecology, etc. […] These notes are intended to be used in the second semester of a two-semester sequence of Statistical Methodology. We assume that students have seen t-tests, Simple Regression, and ANOVA. The second semester emphasizes the uniform matrix notation (y = X\beta + \epsilon) and the interpretation of the coefficients. We cover model diagnostics, transformation, model selection, interactions of continuous and categorical predictors as well as introduce random effects in the … Read more →

These are the complete set of lecture notes in online bookform for the course 1014SCG Statistics at Griffith University, 2020. […] ©Griffith University 2019. Subject to the provisions of the Copyright Act, no part of this publication may be reproduced in any form or by any means (whether mechanical, electronic, microcopying, photocopying, recording, or otherwise), stored in a retrieval system or transmitted without prior … Read more →

Class notes for the BGU course - Introduction to Data Science. […] This book accompanies the course I give at Ben-Gurion University, named “Introduction to Data Science”. This is an introductory-level, hands-on focused course, designed for students with basic background in statistics and econometrics, and without programming experience. It introduces students to different tools needed for building a data science pipeline, including data processing, analysis, visualization and modeling. The course is taught in R environment. Many of the contents in this book are taken from BGU’s “R” course, … Read more →

Course notes for Applied Spatio-temporal Statistics (STAT 764) at Kansas State University […] This document contains the course notes for Applied Spatio-temporal Statistics at Kansas State University (STAT 764). During the semester we will cover construction and analysis of spatial, time series, and spatio-temporal data sets. Topics include data generation using geographic information systems, exploratory data analysis and visualization, and descriptive and dynamic spatio-temporal statistical … Read more →

This is based on Yihui Xie’s a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] These notes were written in 2020, during the ecological modelling classes (Modelação Ecológica, ME in short). While it all started as just a way to teach the course, it soon became obvious that with a bit of extra effort put into it these notes might become material that would be useful to others beyond the ME students. This is being written as a bookdown project. Maybe one day it will become a book, for now, these are essentially notes I … Read more →

PSY317L Guidebook […] This books is still in progress !!! This is a draft. Several sections are still incomplete or unedited. This book is written to help students enrolled in the University of Texas at Austin Introduction to Statistics for the Behavioral Sciences (PSY317L) course led by Professor James Curley. We hope that the book will be a useful resource to help you learn both R and statistics. If you have any suggestions for improvements, please get in touch with Professor Curley. This is in between a textbook and a study guide. We’re trying to build materials that will enable students … Read more →

This is a companion book for students taking the BER 642: Advanced Regression Method at the University of Alabama, Fall 2020 […] This book is still in progress !!! This is a draft. Several sections are still incomplete or unedited. This book is written to help students enrolled in the University of Alabama, Advanced Regression Method (BER 642) course led by Professor Dr.Youn-Jeng (Joy) Choi. We hope that the book will be a useful resource to help you learn both R and statistics. If you have any questions concerning your homework in R, please contact your TA: Qingzhou, at: … Read more →

This is an R guide for statistics course at NSC. […] This guide’s primary focus is on Basic R. When graphics are involved, command functions in both Basic R and a package called ggplot2 will be shown. Graph enhancements will be kept to a minimum. Although there are R packages that may do the same or better job than Basic R, this tutorial will not delve into those packages. Exploring other packages will be left for the student to look into, if the student so wishes. Datasets will be taken mostly from those built into R. Since this is mainly a tutorial on the R commands necessary to do … Read more →

This is a course manual of Bodong Chen’s learning analytics course. […] This is the course website of CI 5371 - Learning Analytics: Theory and Practice offered in Fall ’20 at the University of Minnesota. This course is fully online. All content published on this website is open to the public. Instructor: Bodong Chen, Associate Professor in Learning Technologies and Huebner Endowed Chair in Education & … Read more →

Using R bookdown to present all the assignments in the class. Assignments will be divided into chapters. […] This bookdown document combines many R Markdown files, and is intended to showcase the utility of R Markdown in reporting data. Each chapter in this document contains a brief sample report of each week’s assignment using data provided in-class, as well as an extention of a topic covered that week. Chapter … Read more →

This book contains the results of the document analysis that I did over the summer. I reviewed all available documents on the PCLN and SOM-Y1 courses’ Sakai pages. […] Please read this section before you click through the information in this document. Things I talk about here will help contextualize what you read and explain some of the features of this document. I first highlight some important information about the document analysis. I then provide a table of the courses whose docs were included in this document analysis. The information in this document is the result of a “document … Read more →

A guide to computationa genomics using R. The book covers fundemental topics with practical examples for an interdisciplinery audience […] The aim of this book is to provide the fundamentals for data analysis for genomics. We developed this book based on the computational genomics courses we are giving every year. We have had invariably an interdisciplinary audience with backgrounds from physics, biology, medicine, math, computer science or other quantitative fields. We want this book to be a starting point for computational genomics students and a guide for further data analysis in more … Read more →

ZU course, Fall Semester 2020 […] Welcome to Inferential Statistics and Complex Surveys.This is a course about making inferences with surveys. What does it mean to make an inference? The simplest way to put it is saying that we will use things we know (data) to learn about things we do not know (parameters). This course aims: The objective of these materials is not to replace the readings, but to provide a more concise and, especially, applied summary of the course contents. Part I is about getting the tools ready for the course (R and RStudio) and learning their basics. Part II presents a … Read more →

Experimental Design and Process Optimization with R […] The present document is a short and elementary course on the Design of Experiments (DoE) and empirical process optimization with the open-source Software R. The course is self-contained and does not assume any preknowledge in statistics or mathematics beyond high school level. Statistical concepts will be introduced on an elementary level and made tangible with R-code and R-graphics based on simulated and real world data. So, then, what is DoE and why should the reader become familiar with the concepts of DoE? Very briefly, DoE is the … Read more →

This book contains all materials for my TA STAT 7: Statistical Methods for the Biological,Environmental, and Health Sciences at UCSC, Winter 2020. […] Course Title: Statistical Methods for the Biological, Environmental, and Health Sciences Instructor: Dr. Rajarshi Guhaniyogi TA: Jizhou ‘Joe’ Kang Bio: I’m a first year Ph.D. student at our statsitics department. This is my second time serving as TA for STAT 7, and fourth time as TA. Contact Info: Email: jkang37@ucsc.edu; Office: E2 516 (by appointment only). Office Hour: Thursday 5:00 pm - 6:00 pm at BE 118 Discussion Section: Section A: … Read more →

This book accompanies of the R package oSCR with a specific focus the design of spatial capture-recapture studies and details of the oSCR function scrdesignGA(). […] Why we did this The main function in oSCR does likelihood analysis of several classes of spatial capture-recapture (SCR) models. THere are also a suite of helper fnctions for formatting and processing data objects. Here are a few of the things that motivated our development of the package: So, using this book of course requires that the oSCR package is loaded: But you will also need a few others: If you have any issues or … Read more →

This is a short guide to learning the basic concepts of time series while also implementing these procedures in R. […] This book is created with an objective to clearly explain the basics of time series analysis. The inspiration came from taking a time series course and constantly getting confused by the theory. Often, time series can be a tricky subject; therefore, this book will try to explain the essentials of time series using R. Time series is an immense subject with so much to it; therefore, we won’t be able to cover all of it in this book. We will solely focus on what we believe is … Read more →

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] This course introduces you R by working through common tasks in data science: importing, manipulating, and visualizing data. R is a statistical and programming computer language widely used for a variety of applications. Before proceeding with these training materials, please ensure you have an RStudio.cloud account and can see the workspace. This is a searchable website that serves as a reference for the Ready for R course. This gitbook is not meant to be a … Read more →

Lectures notes for an introductory course on Computational Information Science. […] This is a set of lecture notes for Principles of Computational Information Science written in Markdown and intended for students in Northeastern University’s IS2000 course. m.schedlbauer@neu.edu↩ … Read more →

Property Class Spring 2020 […] [IMPORTANT: If you have any TECHNOLOGICAL issues with this website or the assignments, please contact YOUR PROFESSOR NOT Elon IT.] [Note: If a word appears blue on the website that means that you can click on it and it will take you to a website or it will allow you to download something.] VIEW: This welcome video. READ: Read the course syllabus. DO: Let’s play a game to see how closely you read the syllabus! Click here. Make sure to type your name so I know that you played it! You get credit just for trying. After you have completed the challenge above, give … Read more →

An introduction to R for political scientists. […] This is an introduction to R and Latex. In compiling this documents, several sources have been consulted, including Tim Peterson’s website, Havard’s Math Prefresher, and the course offered by DataCamp. Make sure that you have a laptop throughout this introduction. Install the following applications, if you haven’t done so. Finally, this document is to be used in-class only. As I (will) mention several times, it borrows and merges a lot of resources online. Also, if you see any mistakes or have suggestions, please do shoot me an … Read more →

The book covers material taught in the Johns Hopkins Biostatistics Time Series Analysis course. […] This book will cover the use of time series methods in biomedical and public health applications. And maybe rockets? We will use the following … Read more →

An hands-on introduction to machine learning with R. […] This course material is aimed at people who are already familiar with the R language and syntax, and who would like to get a hands-on introduction to machine learning. This material is currently under development and is likely to change in the future. A set of packages that are used, either directly or indirectly are provided in the first chapter. A complete session information with all packages used to compile this document is available at the end. The source code for this document is available on GitHub at https://github.com/lgatto/IntroMac … Read more →

These are the lecture notes for the course Project Management […] This document contains the lecture notes for the course Project Management (3897) taught at Hasselt University. Each chapter of this document serves to support the lecture presentations and contains a summary in bullet-point style. We advise students to go throught these lecture notes immediately after the lecture and to add your own notes to this … Read more →

經濟資料視覺化 […] This course is designed to develop the skill of efficient graphic language, where efficiency is defined as the data information delivery that is self-contained, concise, and non-distorting. The programming language is mainly based on R, with a little bit of Javascript toward the end. Though there is no computer programming knowledge required, basic R knowledge will help (the ebook, R for Data Science, would be a good start). By the end of the course, students who learn well should be able to design professional … Read more →

This is a collection of my course handouts for PSYC 621 class in the 2019 Spring semester. Please contact me [mailto:hokchiol@usc.edu] for any errors (as I’m sure there are plenty of them). […] This is a collection of my course handouts for PSYC 621 class. The materials are based on the book by McElreath (2016), the brms package (Bürkner 2017), and the STAN language. Please contact me for any errors (as I’m sure there are plenty of them). Bürkner, Paul-Christian. 2017. “brms: An R Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software 80 (1): 1–28. … Read more →

A handbook on teaching and learning with Jupyter notebooks. […] Lorena A. Barba, Lecia J. Barker, Douglas S. Blank, Jed Brown, Allen B. Downey, Timothy George, Lindsey J. Heagy, Kyle T. Mandli, Jason K. Moore, David Lippert, Kyle E. Niemeyer, Ryan R. Watkins, Richard H. West, Elizabeth Wickes, Carol Willing, and Michael Zingale This handbook is for any educator teaching a topic that includes data analysis or computation in order to support learning. It is not just for educators teaching courses in engineering or science, but also data journalism, business and quantitative economics, data-based … Read more →

This is the page that contains the course labs materials […] Data Analysis for Psychology in R 1 (dapR1) is your first step on the road to being a data, programming and applied statistics guru! This course provides a introduction to data, R and statistics. It is designed to work slowly through conceptual content that form the basis of understanding and working with data to perform statistical testing. At the same time, we will be introducing you to basic programming in R, covering the fundamentals of working with data, visualization and simple statistical tests. The overall aim of the … Read more →

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] This book is the personnal transcription of the course provided by Siraj Raval. A drama related to content copyright stained for Raval during this course, and many students including me were disturbed. This event swelled a lot and the motivation to progress in this course was affected. So I decided to propose a version with more explanations and details. I will provide a list of tutorials to follow in order to complete this course. This is a book written in … Read more →

This is a book that is build on lectures from a course of the same name. […] Welcome to Bayesian Hierarchical Models in Ecology. This is an ebook that is also serving as the course materials for a graduate class of the same name. There will be numerous and on-going changes to this book, so please check back. And don’t hesistate to email me if you have questions, comments, or for anything else. To start, let’s calrify the title of this text—it should be Hierarchical Models in Ecology Using Bayesian Inference. A Bayesian Hierarchical Model is more a term of convenience than accuracy, as … Read more →

ECON 41 Labs […] This book is an R-based statistical programming companion for ECON 41 - Statistics For Economists, an undergraduate course for Economics majors offered at the University of California, Los Angeles. More specifically, it has been created to augment the version of this course that is offered at Jinling High School in Nanjing, Jiangsu, China as part of the Global Classroom program that is part of the UCLA International Institute. More basic information about our program and about me is available at the links below. Go Bruins! 金陵中学中美班，加油！ https://www.international.ucla … Read more →

經濟資料視覺化 […] This course is designed to develop the skill of efficient graphic language, where efficiency is defined as the data information delivery that is self-contained, concise, and non-distorting. The programming language is mainly based on R, with a little bit of Javascript toward the end. Though there is no computer programming knowledge required, basic R knowledge will help (the ebook, R for Data Science, would be a good start). By the end of the course, students who learn well should be able to design professional … Read more →

R for MSc DH/RSHR/Epi […] Welcome to the two day Introduction to R for MSc Epi and MSc RSHR. Before you attend the course, you will need to ensure you have your own R setup on your computer. The getting started page will instruct you how to do that. Please do this as early as possible before the course to ensure that you can get help if you encounter any issues. How to interact with this course: Course materials come in three flavours. First, there is the Bookdown file you are reading right now in your web browser - this contains all the code, output, exercise solutions (after the course is … Read more →

The course reader for INFO 201: Technical Foundations of Informatics. […] Announcement: Starting in 2019, readings for the INFO 201 course will come from the textbook Programming Skills for Data Science, which is available to UW students for free via SafariBooksOnline or in print. Unless specifically directed to a section of this online text, you should refer to the Programming Skills for Data Science textbook. This book covers the foundation skills necessary to start writing computer programs to work with data using modern and reproducible techniques. It requires no technical background. … Read more →

Scripts from the online course on Time Series and Forecasting in R. […] Selecting the model. Due to seasonality involved, simple models will not be able to capture it. We therefore use the seasonal ARIMA and exponential smoothing models. Exponential smoothing models have seasonality built in it by construction. Complex models like mixed models and neural nets will be an overkill. … Read more →

This is a book that provides the foundations for good project structure and organisation. It guides you in what reproducible research is, and how we can implement it. If you would like to contribute to, and expand upon, sections, please submit a pull request on the GitHub Repo. Equally, please submit pull requests if you spot a typo or a mistake! […] This book’s focus is on how to produce reproducible research, and should serve as an introduction to data management and project organisation. Through the course of this document, we explain techniques that can be employed easily to help add … Read more →

This is the main page of the course and contains the materials to help you going with R […] This course is designed for those who will be joining a third year Research Methods and Statistics (RMS) course and covers a number of introductions to topics which are core to statistical analysis in psychology and beyond. You will find here an introduction to R as a tool to analyse data, visualize it and to use it for a very very basic analysis of the relationships in your data. It will further revise some of the most commonly used statistical tests and provide you with a guidance how to set up and … Read more →

A workflow for doing machine learning in the R language, using Agile principles. […] Not even too long ago, when I was starting my career as a data scientist, I did not really have a workflow. Freshly graduated from an applied statistics master I entered the arena of Dutch business, employed by a small consulting firm. Neither the company I was with, nor the clients I was working for, nor myself had an understanding of what it meant to implement a statistical model or a machine learning method in the real world. Everybody was of course interested in this “Big Data” thing, so it did not take … Read more →

A detailed introduction to coding in R and the process of data analytics. Version 1.0.0 […] Welcome to Introduction to Data Exploration and Analysis in R (IDEAr)! This book is designed as a crash course in coding with R and data analysis, built for people trying to teach themselves the skills needed for most analyst jobs today. You won’t need any past experience with R or data analytics - the aim of the book is to work as a primer for people of all backgrounds. This book is currently being continuously deployed to bookdown.org and GitHub while editing continues. This is so that I can get … Read more →

Data Analysis and Processing with R based on IBIS data […] Over the course of my time working with the Carolina Insitute for Developmental Disabilities (CIDD) and the Infant Brain Imaging Study (IBIS) network, I have seen a great interest in learning how to do basic statistical analyses and data processing among the trainees. Specially, there is an interest in learning how to use R, due to its popularity across the sciences and its zero financial cost. As a statistican in training, I feel it is a great benefit for scientists to learn R. It is vital for scientists to understand the … Read more →

This is the main page of the course and contains a course overview, schedule and learning outcomes. […] During this intensive workshop we will cover a number of introductions to topics which are core to statistical analysis in applied research. This will include introduction to R as a tool to analyse data, visualize it and to use it for a very very basic analysis of the relationships in your data. We will further revise some of the most commonly used statistical tests and provide you with a guidance how to set up and interpret them in R. We will introduce you to simple linear model and … Read more →

經濟資料視覺化處理 […] This course is designed to develop the skill of efficient graphic language, where efficiency is defined as the data information delivery that is self-contained, concise, and non-distorting. The programming language is mainly based on R, with a little bit of Javascript toward the end. Though there is no computer programming knowledge required, basic R knowledge will help (the ebook, R for Data Science, would be a good start). By the end of the course, students who learn well should be able to … Read more →

Lab Guide to Quantitative Research Methods in Political Science, Public Policy & Public Administration. […] This book is a companion to Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R): 4th Edition, an open-source text book that is available here. It grew from our experiences teaching introductory and intermediate quantitative methods classes for graduate students in Political Science and Public Policy at the University of Oklahoma. We teach these courses using a format that pairs seminars on theory and statistics with … Read more →

This online textbook contains learning material for the UQ (The University of Queensland) course BIOL3360: Analysis and Communication of Biological Data. This book is organised with each chapter corresponding to lectures from the Mathematical Modelling component of the course. This book contains many code chunks that can be copied and pasted into an R console to create Shiny apps of the models being discussed. Content and figures were created by Jan Engelstädter. Online version including Shiny apps were created by Nicole Fortuna. Read more →

This is the main page of the course and contains a course overview, schedule and learning outcomes. […] During this intensive workshop we will cover a number of introductions to topics which are core to statistical analysis in applied research. This will include introduction to R as a tool to analyse data, visualize it and to use it for a very very basic analysis of the relationships in your data. We will further revise some of the most commonly used statistical tests and provide you with a guidance how to set up and interpret them in R. Lastly, we will introduce you to simple linear model … Read more →

Course book for Introduction to Exploratory Data Analysis with R (APS 135) in the Department of Animal and Plant Sciences, University of Sheffield. […] This is the online course book for the Introduction to Exploratory Data Analysis with R component of APS 135, a module taught by the Department and Animal and Plant Sciences at the University of Sheffield. You can view this book in any modern desktop browser, as well as on your phone or tablet device. Dylan Childs is running the course this year. Please email him if you spot any problems with the course book. You will be introduced to the R … Read more →

Gijón Air Pollution - An exercise of visualization and forecasting […] My name is Sergio Berdiales and I am a Data Analyst with more than ten years experience in Customer Experience and Quality areas. If you want to know more about me or contact me you can visit my Linkedin profile or my Twitter account. This is my final project for the Kschool Master on Data Science (8th edition). The main objective of this project is to show I can apply the acquired knowledge during the master’s course in a practical way . The Master on Data Science of Kschool is a 230-hour course which includes Python … Read more →

Script for the seminar ‘Big Data and Social Science’ at the University of Bern. […] The present document serves both as slides and script for the workshop/seminar Big Data and Social Science. This seminar is taught by Paul C. Bauer at the University of Bern (Fall Semester 2018). The material was developed by Paul C. Bauer and heavily draws on material developed by Pablo Barberà in courses such as Social Media & Big Data Research, Big Data Analysis in the Social Sciences and Automated Collection of Web and Social Data. Any original material and examples is licensed under a Creative Commons … Read more →

These are the notes for ST463/ST683 Linear Models 1 course offered by the Mathematics and Statistics Department at Maynooth University. This module is offered at as a part of of MSc in Data Science and Data Analytics. It is an introductory course for students who have basic background in Statistics, Data analysis, R Programming and linear algebra (matrices). […] There are many good resources, e.g. Weisberg (2005), Fox (2005), Fox (2016), Ramsey and Schafer (2002), Draper and Smith (1966). We will use Minitab and R (R Core Team 2017). To create this document, I am using the bookdown package … Read more →

An introduction to R for political scientists. […] This is an introduction to R and Latex. In compiling this documents, several sources have been consulted, including Tim Peterson’s website, Havard’s Math Prefresher, and the course offered by DataCamp. Make sure that you have a laptop throughout this introduction. Install the following applications, if you haven’t done so. Finally, this document is to be used in-class only. As I (will) mention several times, it borrows and merges a lot of resources online. Also, if you see any mistakes or have suggestions, please do shoot me an … Read more →

This is a short course on survival analysis applied to the financial field. […] This book is designed to provide a guide for a short course on survival analysis. It is mainly focussed on applying the stastical tecnquines developed in the survival field to the financial industry. The emphasis is placed in understanding the methods, building intuition about when aplying each of them and showing their application through the use of statistical … Read more →

迷你課程 […] 電子書網址：https://bookdown.org/tpemartin/minicourse-webplugins/ 首先你必需： 在Atom: 點privacypolicy.html 將以下兩個訊息換成你的訊息 https://your_website_url your_email … Read more →

ggplot2 介紹 […] hypothes.is: https://hypothes.is/groups/eBBqEGde/minicourse-ggplot2 要在hypothes.is貼上程式碼時，請依下例張貼： ggplot2 cheatsheet Computing for the Social Sciences, U.Chicago. ggplot2part of the … Read more →

This is a minimal example of using the bookdown package to write a book. The output format for this example is bookdown::gitbook. […] This is a collection of my notes and algorithms from a course on Numerical Analysis at the University of Iceland. The book used in the course was Numerical Analysis by Timothy … Read more →

Course notes for IS 6489. […] These are the course notes for IS 6489, Statistics and Predictive Analytics, offered through the Information Systems (IS) department in the University of Utah’s David Eccles School of Business. This is an exciting time for data analysis! The field has undergone a revolution in the last 15 years with increases in computing power and the availability of “big data” from web-based systems of data collection. “Data science” is the umbrella term that describes the result of this revolution—a new discipline at the intersection of many traditional fields such as … Read more →

An open-source and fully-reproducible electronic textbook bridging the gap between traditional introductory statistics and data science courses. […] Help! I’m new to R and RStudio and I need to learn about them! However, I’m completely new to coding! What do I do? If you’re asking yourself this question, then you’ve come to the right place! Start with our Introduction for Students. This is version 0.2.0 of ModernDive published on August 02, 2017. For previous versions of ModernDive, see Section 1.4. This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding … Read more →

Data Science and Visualizations with R […] This is a course on the use of tidyverse packages tidyverse provides a complete suite of modern data-handling tools. It is an essential toolbox for any data scientist using R. The tidyverse package is designed to be easy to install. This course will dive into using tidyverse. It will assume you have already installed r and rstudio and how some familiarity on how to use the rstudio. This book will use the nycflights13 dataset This package contains information about all flights that departed from NYC in 2013: 336,776 flights with 16 variables. To … Read more →

Notes on R for the course AML100 at Arizona State University. […] These notes introduce the basics of the programming language R as needed for the course AML100. Notes on RStudio and R Markdown are included in … Read more →

In 1980 members of the Department of Data Theory at the University of Leiden taught a post-doctoral course in Nonlinear Multivariate Analysis. The course content was sort-of-published, in Dutch, as Gifi (1980). The course was repeated in 1981, and this time the sort-of-published version (Gifi (1981)) was in English. The preface gives some details about the author. The text is the joint product of the members of the Department of Data Theory of the Faculty of Social Sciences, University of Leiden. ‘Albert Gifi’ is their ‘nom de plume’. The portrait, however, of Albert Gifi shown here, is that … Read more →

Lecture notes for my introduction to statistics class at University of Nebraska-Lincoln. […] This is supposed to be your first course in statistics. So the goal is to give you an overview of what statistics is, why it is a powerful thing to know, how you can use it to make informed decision or understand “numbers speak” people throw around in the news. At the end of this class, I hope: 1- You understand the importance of statistics; 2- You can better appreciate the numbers you get from the news; 3- You can perform your own analysis to inform yourself, and your collaborators. The explosion … Read more →

This book aims to provide a practical extension of introductory statistics typically taught in psychology into the general linear model (GLM) using R. […] Typically, introductory univariate statistics courses in psychology cover the following inferential analyses (plus or minus a few more analyses): These conventions may be useful for quickly talking about a particular statistical analysis with others; however, thinking of these analyses as derivatives (or special cases) of the GLM (i.e., ordinary least squares [OLS] regression) lends itself to understanding more advanced statistical … Read more →

Course notes for 94692 Data Science Practice at the University of Technology, Sydney. […] This website forms the course notes for 94692 Data Science Practice which is an elective subject developed as part of the Master of Data Science and Innovation program at the University of Technology, Sydney. For more information about this subject see the Subject Information. For more information about the MDSI program see the MDSI Prospectus. Whilst these course materials have been produced specifically for MDSI students, they have been made available under a permissive license for the benefit of the wider … Read more →

Joyce Robbins This is edav.info version 2.0! The first version of edav.info is still available, but will no longer be updated. With this resource, we try to give you a curated collection of tools and references that will make it easier to learn how to work with data and create visualizations in R. This resource is specifically tailored to the graduate courses I teach at Columbia University. However, we hope that anyone interested in working with data in R will benefit from these pages. Happy coding! (Note. edav.info 2.0 is still under construction, and we will try our best to update new … Read more →

Vignette for the R package mixOmics […] If you are following our online course, the following vignette will be useful as a complementary learning tool. This vignette also covers the essential use cases of various methods in this package for the general mixOmcis user. The below methods will be covered: As outlined in 1.3, this is not an exhaustive list of all the methods found within mixOmics. More information can be found at our website and you can ask questions via our discourse … Read more →

An open access textbook for introductory philosophy courses on probability and inductive logic. […] This textbook is for introductory philosophy courses on probability and inductive logic. It is based on a typical such course I teach at the University of Toronto, where we offer “Probability & Inductive Logic” in the second year, alongside the usual deductive logic intro.(,) The book assumes no deductive logic. The early chapters introduce the little that’s used. In fact almost no formal background is presumed, only very simple high school algebra. Several well known predecessors inspired … Read more →

The Epi R Handbook is an R reference manual for applied epidemiology and public health. […] Usage: This handbook has been used over 3 million times by 850,000 people around the world. Objective: Serve as a quick R code reference manual (online and offline) with task-centered examples that address common epidemiological problems. Are you just starting with R? Try our free interactive tutorials or synchronous, virtual intro course used by US CDC, WHO, and 400+ other health agencies and Field Epi Training Programs worldwide. Languages: French (Français), Spanish (Español), Vietnamese (Tiếng … Read more →