| Date of Class | Topics | Chapter |
|---|---|---|
| Week 1 (Jan 8-12) | What is data science? + Getting started with R (part 1) | 1 + 2 (up to section 2.9 inclusively) |
| Week 2 (Jan 15-19) | Getting started with R (part 2) | 2 (from section 2.10) |
| Week 3 (Jan 22-26) | Importing and tidying data (part 1) | 3 (up to section 3.4 inclusively) |
| Week 4 (Jan 29-Feb 2) | Importing and tidying data (part 2) | 3 (from section 3.5) |
| Week 5 (Feb 5-9) | Working with strings | 4 |
| Week 6 (Feb 12-16) | Publishing in R + Summarizing data | 5 + 6 |
| Reading week (Feb 19-23) | ||
| Week 7 (Feb 26-Mar 1) | Visualizing data | 7 |
| Week 8 (Mar 4-8) | Topic modelling | 8 |
| Week 9 (Mar 11-15) | Logistic regression | 9 |
| Week 10 (Mar 18-22) | Linear regression | 10 |
| Week 11 (Mar 25-29) | Interactive dashboards (part 1) | 11 |
| Week 12 (Apr 1-5) | Interactive dashboards (part 2) | 12 |
Introduction to Data Science
Course overview
Introduction to Data Science is a hands-on course for students with no or minimal coding experience. We will learn to use R to collect, manipulate, analyze, and visualize data.
Schedule
For the Winter 2024 semester, the course will be online. You can book online assistance with me throughout the semester using this link. For the month of January only, there will be optional in-person support available in ROWE 3080 on Tuesdays from 11:30 am to 2:30 pm.
Assignments
Important
Important
Use the course’s Brightspace to:
Access the detailed instructions and due dates for the assignments.
Submit your assignments.
Bibliography
The course website will contain everything you need, including references to resources that can be valuable to further develop your understanding and skills, most of which are all available online for free. They are all available in this Zotero library.
Useful resources
Teams channel (only accessible to students currently registered in the course).
Course syllabus (in case of discrepancy, the course website and BrightSpace prevail).