Course Description
Managing, manipulating, and analyzing structured/unstructured data to understand relationships and generate useful insights. Principles such as programming for analytics, data visualization, statistical modeling, database design, high performance computing are discussed.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- Write clear, structured Python programs using functions and control structures.
- Manipulate data using lists, dictionaries, sets, and tuples.
- Clean and analyze datasets using NumPy and Pandas.
- Create visualizations using Matplotlib and Seaborn.
- Apply basic machine learning and automation techniques.
- Connect to SQL databases and run queries from Python.
- Manage code with Git and work in reproducible notebooks.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Weekly Quizzes: Brief checks of course concepts | 15-20% |
| Coding Assignments: Python programming and data-analysis exercises | 30-35% |
| Midterm Project: Applied data-analysis project | 20% |
| Final Project: End-to-end analytics project | 25-30% |
| Labs and Participation: Lab work and course engagement | 5-10% |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
No course materials are required.
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.