Course information
This course covers the basic elements of the data science life cycle, including statistics for data science, data exploration, data preprocessing, visualization, data analysis and inference. We will explore key areas including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. Specifically, we will focus on transforming, querying and analyzing data; basic algorithms for data analysis including regression, classification and clustering; principles behind creating informative data visualizations; and statistical concepts of measurement error and prediction.
Instructor and time
Reference books
- Computational and Inferential Thinking: The Foundations of Data ScienceAni Adhikari and John DeNero, 2021
- Principles and Techniques of Data ScienceSam Lau, Joey Gonzalez and Deb Nolan, 2021
- The Art of Data ScienceRoger D. Peng and Elizabeth Matsui, 2017
- Python Data Science HandbookJake VanderPlas
Grading
| Component | Weight |
|---|---|
| Midterm 1 | 20% |
| Midterm 2 | 25% |
| Project | 15% |
| Final exam | 40% |
Announcements
All announcements and communication will be carried out via Piazza. The enrolment link and lecture notes will be shared there at the start of the semester.
Schedule
Tentative. Dates and lecture notes will be added during the semester.
| Week | Topic | Notes |
|---|---|---|
| 1 | Introduction to data science | – |
| 2 | Causality, experiments, tables in Python | – |
| 3 | Rectangular data and basic SQL | – |
| 4 | SQL continued | – |
| 5 | Data cleaning, reduction and transformation | – |
| 6 | Data visualization | – |
| 7 | Midterm 1 | – |
| 8 | Introduction to statistical inference and regression | – |
| 9 | Linear models and regression (continued) | – |
| 10 | Classification and logistic regression | – |
| 11 | Midterm 2 | – |
| 12 | Classification trees | – |
| 13 | Clustering | – |
| 14 | Text, graphs and other data types | – |