Data Science

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Data Science for Economists Seminar

By Grant McDermott at the University of Oregon

What is this?
Excerpt from site: This is a graduate economics seminar taught by Grant McDermott at the University of Oregon.

Please read the syllabus before you go through any of the lectures. This will detail software requirements and installation, and give you a better sense of the aims and scope of the course. I also have an “FAQ” section at the end that covers frequently asked questions (or, at least, potentially asked questions). Speaking of which, here follow answers to some questions that are more specifically related to this repo.

  1. Link to lectures repo:

Data Science for Psychologists: A Refreshed Exploratory & Graphical Data Analysis in R

By S. Mason Garrison

Added Fri Apr 28th, 2023

What is this?
Excerpt from site: This website is designed to accompany Mason Garrison’s Data Science for Psychologists (DS4P). DS4P is a graduate-level quantitative methods course at Wake Forest University. This class assumes zero knowledge of programming, computer science, linear algebra, probability, or really anything fancy. I encourage anyone who is quant-curious to work their way through these course notes. The course notes include lectures, worked examples, readings, activities, and labs.

  1. Link to website:

Data Science in a Box

By Mine Çetinkaya-Rundel

What is it?
Excerpt from site: The core content of the course focuses on data acquisition and wrangling, exploratory data analysis, data visualization, inference, modelling, and effective communication of results. Time permitting, the course also introduces additional concepts and tools like interactive visualization and reporting, text analysis, and Bayesian inference.

  1. Link to site:
  2. Link to repo:

Data Science with R

By Danielle Navarro

What is this?
Data Science course by the wonderful Danielle Navarro.

  1. Link to e-course here:

Data Skill for Reproducible science

What is it?
Excerpt from site: 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.

  1. Course here:

Data Wrangling in the Tidyverse

By Nick Huntington-Klein

Date added Fri AprMay 5th, 2023

What is this?
Excerpt from the site: This video series covers the basics of data wrangling using the tidyverse, aimed at my data communications class. I assume some very, very basic R background, but really not much.

This is a simplified and adapted version of my data wrangling workshops, which are available in full on my channel for tidyverse (R), data.table (R), and pandas (Python).

  1. Link to videos are here:
  2. Slides to follow along with the videos are here:
  3. Code instructor works on in the videos can be found here:

Introduction to Data Science Fall 2019

By University of Edinburgh

What is this
Excerpt from site: Gain experience in data collection, wrangling, and visualization, exploratory data analysis, predictive modeling, and effective communication of results while working on problems and case studies inspired by and based on real-world questions. The course will focus on the R statistical computing language.

  1. Link to e-course here:

Reproducible science workshop: A one-day workshop with R and RStudio

By Olivier Gimenez

Added Fri Oct 8th, 2021

What is this

One day Workshop that includes the following topics: Motivations, Manipulating data in the tidyverse, Visualising data in the tidyverse, Writing dynamic and reproducible documents with R Markdown, Versioning with Git and GitHub in RStudio, and Take-home messages.

  1. Link to materials and videos here: