KOUT 2095 - Data Science with R

This workshop is about accessing, manipulating visualizing, and analyzing data with a statistical software called R and its RStudio interface. Working with different kinds of data, you will learn some essential statistical concepts along the way, building up from exploratory data analysis to statistical modeling. You must know that you will be using your computer most of the time throughout the course. The goal is for you to turn to a data scientist 2 hours of each week!.

GROUP 1 with Professor Briatte will be an advanced level class - please refer to the Course Outline for Group 1 for a list of contents

Learning Outcomes
1. Proficiency in exploratory data analysis
2. Knowledge of statistical inference and modeling
3. Knowledge of the R programming language
4. Knowledge of the RStudio software
5. Exposition to current data science trends

Professional Skills
Quantitative methods, R and RStudio software, data science skills. After this course, the students will be more able to interact with scientific professions such as data scientists.

François BRIATTE,Kim ANTUNEZ

Séminaire

English

- Attendance: 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: 2 hours a week / 24 hours a semester

(1) A laptop running a recent version of Windows, MacOS or Linux, with full admin privileges. The laptop must have installed with the latest versions of R (r-project.org) and RStudio Desktop (rstudio.com). (2) Minimal computing skills, e.g. unzipping files, and the ability of installing new libraries using the internet. (3) Some prior exposure to introductory statistics, e.g. descriptive stats and association tests.

Autumn and Spring 2023-2024

There will be exercises to be completed in between workshop sessions, and possibly group projects to be elaborated throughout the semester.

All classes are structured around a slides-based presentation and a ‘demo' session on the statistical software, followed by a ‘debrief' email that includes readings and other homework, with feedbacks during the next class.