Data Science Fundamentals
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: 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. |