# Course

# Teaching and Learning with Jupyter

## by 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

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, … Read more →

# edav.info/

## by Zach Bogart, Joyce Robbins

This resource is a collaborative collection of resources designed to help students succeed in GR5702 Exploratory Data Analysis and Visualization, a course offered at Columbia University. While the course lectures and textbook focus on theoretical issues, this resource, in contrast, provides coding tips and examples to assist students as they create their own analyses and visualizations. It is our hope that students will contribute to edav.info and it will grow with the course. Read more →

# ntpu-programming-for-data-science.utf8.md

## by tpemartin

資料科學程式設計（一） […] This course is to build the foundation for being a data scientist–who masters both data analysis and data engineering. There are two programming languages that will be taught through the course: R and Javascript. R will serve as the data analysis backend, while Javascipt will serve as the communication tool interacting with cloud services–such as Google G Suite. After taking the course, students will be able to create their own data services that can automate routine works and enhance their productivities. … Read more →

# Notes for Predictive Modeling

## by Eduardo García Portugués

Notes for Predictive Modeling. MSc in Big Data Analytics. Carlos III University of Madrid. […] Welcome to the notes for Predictive Modeling for the course 2018⁄2019. The subject is part of the MSc in Big Data Analytics from Carlos III University of Madrid. The course is designed to have, roughly, one lesson per each main topic in the syllabus. The schedule is tight due to time constraints, which will inevitably make the treatment of certain methods a little superficial compared with what it would be the optimal. Nevertheless, the course will hopefully give you a respectable panoramic … Read more →

# ntpu-data-visualization.utf8.md

## by tpemartin

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 →

# An Introduction to R, LaTeX, and Statistical Inference

## by Yuleng Zeng

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 →

# Introduction to Data Exploration and Analysis with R

## by Michael Mahoney

This is a course reader for a class that will never be taught. Hopefully it helps you nonetheless. […] This is a course reader for a hypothetical 3-credit undergraduate class, focusing on getting those with no prior exposure to R up to speed in coding and data analysis procedures. This reader is currently being continuously deployed to bookdown.org and GitHub, particularly as new sections are completed or old ones restructured. This is so that I can get feedback from the small group of people who are using this book to learn R themselves, so I can adjust and adapt the text as needed. If … Read more →

# Big data and Social Science

## by Paul C. Bauer

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 →

# A First Course on Statistical Inference

## by Isabel Molina Peralta and Eduardo García Portugués

Notes for Statistical Inference. MSc in Statistics for Data Science. Carlos III University of Madrid. […] Definition 1.1 (Random experiment) A random experiment is an experiment with the following properties: The following concepts are associated with a random experiment: Example 1.1 The next experiments are random experiments: A probability function is defined as a mapping of subsets (events) of the sample space (\Omega) to elements in ([0,1]). Therefore, it is convenient to count on a “good” structure for these subsets, which will provide “good” properties to the probability … Read more →

# Notes for ST463/ST683 Linear Models 1

## by Katarina Domijan, Catherine Hurley

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 →

# STAT160 R/RStudio Companion

## by Statistics/Data Science at St. John Fisher College

Companion document to Introduction to Statistical Investigations using R/RStudio. […] This companion is for use in STAT160 (Introduction to Data Science). 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 →

# Technical Foundations of Informatics

## by Michael Freeman and Joel Ross

The course reader for INFO 201: Technical Foundations of Informatics. […] 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. These materials were developed for the INFO 201: Technical Foundations of Informatics course taught at the University of Washington Information School; however they have been structured to be an online resource for anyone hoping to learn to work with information using programmatic approaches. This book is licensed under a Creative Commons … Read more →

# An Introduction to R and LaTeX

## by Yuleng Zeng

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 →

# An Introduction to Statistical and Data Sciences via R

## by Chester Ismay and Albert Y. Kim

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 0.4.0 of ModernDive published on July 21, 2018. For previous versions of ModernDive, see Section 1.5. This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding … Read more →

# A short course on Survival Analysis applied to the Financial Industry

## by Marta Sestelo

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 →

# Advanced Statistical Computing

## by Roger D. Peng

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 →

# 網頁外掛功能：GA,Share,Comments

## by 國立臺北大學經濟學系-經濟時事與多媒體出版

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 →

# R for Social Scientists

## by Paul C. Bauer, Rudolf Farys

Script for a an R course at the European University Institute. … Read more →

# ggplot2 介紹

## by 林茂廷老師

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 →

# The Queens College Guide to Life

## by The Queens College Collective Consciousness

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 collaboratively written guide for courses at Queens College that focus on the biology of tiny things, which … Read more →

# The Queens College Stats Guide

## by The Queens College Collective Consciousness

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 guide to learning statistics at Queens College. Currently we support the following courses directly with … Read more →

# The Queens College Guide to Ecosystems

## by The Queens College Collective Consciousness

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 collaboratively written guide for courses at queens college that focus on the biology of big things, like communities and cats, which … Read more →

# The Queens College Alchemy Guide

## by The Queens College Collective Consciousness

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 collaboratively written guide for chemistry courses at Queens College, … Read more →

# Numerical Analysis: Notes

## by Brynjólfur Gauti Jónsson

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 →

# A short course on nonparametric curve estimation

## by Eduardo García-Portugués

A short course on nonparametric curve estimation […] 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 reason is because they are hosted at https websites with auto-signed SSL … Read more →

# Comparative Methods

## by Brian O’Meara

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 →

# APS 135: Introduction to Exploratory Data Analysis with R

## by Dylan Z. Childs

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) module. You can view this book in any modern desktop browser, as well as on your phone or tablet device. The site is self-contained—it contains all the material you are expected to learn this year. Bethan Hindle is running the course this year. Please email her if you spot any problems. You will be introduced to the R ecosystem. R … Read more →

# Broadening Your Statistical Horizons

## by J. Legler and P. Roback

Test. […] Broadening Your Statistical Horizons (BYSH): Generalized Linear Models and Multilevel Models is intended to be accessible to undergraduate students who have successfully completed a regression course through, for example, a textbook like Stat2. We started teaching this course at St. Olaf in 2003 so students would be able to deal with the non-normal, correlated world we live in. It has been offered at St. Olaf every year since; in fact, it is required for all statistics concentrators. Even though there is no mathematical prerequisite, we still introduce fairly sophisticated topics … Read more →

# Course Notes for IS 6489, Statistics and Predictive Analytics

## by Jeff Webb

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 →

# ModernDive

## by Chester Ismay and Albert Y. Kim STARRING FRANK MCGRADE

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

## by Jonathan Wong

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 AML100

## by Jordan T. Bates

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 →

# Social Network Analysis in Education

## by chen

This is a course handbook written by Bodong Chen for his SNA course at UMN. […] This site is the course portal of CI5330 - Social Network Analysis in Education, taught by Prof. Bodong Chen at the University of Minnesota in Spring ’17. Content on this site is actively built and refined throughout the semester. This site or book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Last update: 2017-04-17 … Read more →

# Multivariate Analysis with Optimal Scaling

## by Jan de Leeuw, Patrick Mair, Patrick Groenen

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 →

# Econ 215 Notes

## by Salfo Bikienga

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 →

# Odds & Ends

## by Jonathan Weisberg

A textbook introducing philosophy students to probability, decision theory, and the philosophical foundations of statistics […] 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 … Read more →