Introduction to Data Science

Lectures | Summary | About | Credits

Lectures

0. R and the tidyverse [.html | .pdf | .Rmd]

1. What is data science? [.html | .pdf | .Rmd]

2. Programming I: Project management, coding etiquette, functions [.html | .pdf | .Rmd]

3. Programming II: Iteration, automation, scheduling [.html | .pdf | .Rmd]

4. Web data and technologies [.html | .pdf | .Rmd]

5. Web scraping and APIs [.html | .pdf | .Rmd]

6. Relational databases and SQL [.html | .pdf | .Rmd]

7. Modeling [.html | .pdf | .Rmd]

8. Visualization [.html | .pdf | .Rmd]

9. Monitoring and communication [.html | .pdf | .Rmd]

10. Data science ethics [.html | .pdf | .Rmd]

11. Towards open data science [.html | .pdf | .Rmd]

12. Hackathon [.html | .pdf | .Rmd]

13. [BONUS] Working at the command line [.html | .pdf | .Rmd]

14. [BONUS] Version control [.html | .pdf | .Rmd]

Summary

This is a course taught by Simon Munzert at the Hertie School, Berlin.

Course contents

This course will introduce you to the modern data science workflow with R. In recent years, data analysis skills have become essential for those pursuing careers in policy advocacy and evaluation, business consulting and management, or academic research in the fields of education, health, and social science. We will cover topics like functional programming, data collection, wrangling, storage, and visualization, model fitting, data science ethics, open data science practice, and the responsible use of AI in data science practice. The course is intended for students with some experience in working with R.

Main learning objectives

The goals are to (1) equip you with conceptual knowledge about the data science pipeline and coding workflow, data structures, and data wrangling, (2) enable you to apply this knowledge with statistical software, and (3) prepare you for our other methods electives and the master’s thesis.

Credits

Many of the materials build on Grant McDermott's excellent course Data Science for Economists.