Instructor: Prof. Data
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. |