| Date of Class | Topics | Chapter |
|---|---|---|
| Week 1 (Jan 6-10) | What is data science? + Getting started with R (part 1) | 1 + 2 (up to section 2.9 inclusively) |
| Week 2 (Jan 13-17) | Getting started with R (part 2) | 2 (from section 2.10) |
| Week 3 (Jan 20-24) | Importing and tidying data (part 1) | 3 (up to section 3.4 inclusively) |
| Week 4 (Jan 27-31) | Importing and tidying data (part 2) | 3 (from section 3.5) |
| Week 5 (Feb 3-7) | Working with strings | 4 |
| Week 6 (Feb 10-14) | Publishing in R + Summarizing data | 5 + 6 |
| Reading week (Feb 17-21) | ||
| Week 7 (Feb 24-28) | Visualizing data | 7 |
| Week 8 (Mar 3-7) | Topic modelling | 8 |
| Week 9 (Mar 10-14) | Logistic regression | 9 |
| Week 10 (Mar 17-21) | Linear regression | 10 |
| Week 11 (Mar 24-28) | Interactive dashboards (part 1) | 11 |
| Week 12 (Mar 31-Apr 4) | 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.
Note
This book can be accessed directly from your browser (without going through Brightspace) using the following URL: https://pmongeon.github.io/info6270/
Schedule
Assignments
Important
Important
Use the course’s Brightspace to:
Access the syllabus (under the content - overview tab).
Access assignment instructions and due dates.
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).