KOUT 2095 - Data Science with R

This course introduces the foundations of data science using R within the RStudio software. Students will progress from descriptive statistics and hypothesis testing through regression models to an introduction to machine learning. Using R, they will apply these methods to heterogeneous datasets drawn from various research fields (political science, sociology, biology, etc.). They will master the art of loading, describing, summarizing, and visualizing data. Most importantly, they will learn how to make sense of a dataset and how to question it. No prior programming experience is required.

LEARNING OUTCOMES

• Proficiency in exploratory data analysis
• Knowledge of statistical inference and modeling
• Knowledge of the R programming language
• Knowledge of the RStudio software

PROFESSIONAL SKILLS

Quantitative methods, R and RStudio software, familiarity with data science

• A laptop running a recent version of Windows, MacOS or Linux, with full admin privileges, in order to support the installation of the latest versions of R (r-project.org) and RStudio Desktop (posit.co/products/open-source/rstudio).
• Minimal computing skills, e.g. unzipping files, using keyboard shortcuts, connecting to the Internet.
• Some prior exposure to introductory statistics, e.g. descriptive statistics and association tests.

Feedback will be provided in class as answers to the exercises.

First graded group exercice (After module 2): Data loading, descriptive statistics, data visualisation, hypothesis testing (33%)

Individual formal exam (After module 3): Data analysis project to explain and comment (Student have to look at a data science analysis with R code, tables and figures. We ask them questions about the code, statistics, and the result of the analysis) (33%)

Second graded group exercice (After the end of the course): Data wrangling, inferential statistics, visualisation, and modeling (33%)

• In Class Presence: 2 hours a week / 24 hours a semester
• Online learning activities: 12 hours a week / 24 hours a semester
• Reading and Preparation for Class: 1 hour a week / 12 hours a semester
• Research and Preparation for Group Work: 1 hour a week / 12 hours a semester
• Research and Writing for Individual Assessments: 1 hour a week / 12 hours a semester