Instructor: Prof. Sagong
Term: Spring
Location: 한빛관 213호
Time: Mondays and Thursday
Prerequisites
- Basic programming knowledge (preferably in Python)
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. |