From data to impact - a practical guide to inference for infectious disease dynamics
This course covers the foundational aspects of data science, including data collection, cleaning, analysis, and visualization. Students will learn practical skills for working with real-world datasets.
Instructor: Monica Golumbeanu, Christian Selinger
Term: Spring
Location: Science Building, Room 202
Time: Mondays and Wednesdays, 2:00-3:30 PM
Course Overview
This course provides a comprehensive introduction to data science principles and practices. Students will:
- Learn the end-to-end data science workflow
- Gain practical experience with data manipulation tools
- Develop skills in data visualization and communication
- Apply statistical methods to derive insights from data
Prerequisites
- Basic programming knowledge (preferably in Python)
- Introductory statistics
- Comfort with basic algebra
Textbooks
- “Python for Data Analysis” by Wes McKinney
- “Data Science from Scratch” by Joel Grus
Grading
- Assignments: 50%
- Project: 40%
- Participation: 10%
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Feb 5 | Introduction to Data Science Overview of the data science workflow and key concepts. | |
| 2 | Feb 12 | Data Collection and APIs Methods for collecting data through APIs, web scraping, and databases. | |
| 3 | Feb 19 | Data Cleaning and Preprocessing Techniques for handling missing values, outliers, and data transformation. | |
| 4 | Feb 26 | Exploratory Data Analysis Descriptive statistics, visualization, and pattern discovery. | |
| 5 | Mar 4 | Statistical Analysis Hypothesis testing, confidence intervals, and statistical inference. | |
| 6 | Mar 11 | Data Visualization Principles and tools for effective data visualization. |