BAN218
INTRO TO PYTHON PROGRAMMING
- Department
- Business Analytics
- Terms offered
- 1
- Data refreshed
- Jun 27, 2026
Description
Analytics Programming, designed as a practical introduction to data analytics in a business context. Aimed at students with minimal programming experience, this course focuses on Python programming and its Pandas library as primary tools for data manipulation and analysis. The course is not merely a technical tutorial but an application-oriented guide. You'll learn how to define business problems clearly, ask insightful questions, and then use Python and Pandas to uncover answers. We will start with Python fundamentals and move on to more complex data manipulation and visualization techniques using Pandas. Throughout the course, you'll engage in hands-on mini-projects that simulate real-world business scenarios, culminating in a team-based final project. This final project serves as both a capstone experience and a portfolio piece, demonstrating your newfound analytical skills in a real-world context. By the end of this course, you'll have both the technical skills and the strategic mindset to apply data analytics effectively in any business environment.
Syllabi
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Offering history & forecast 2026
2026
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: not enough history to forecast. | 2030 Spring: not enough history to forecast. | 2030 Summer: not enough history to forecast. | 2030 Intersession: not enough history to forecast. |
| 2029 | 2029 Fall: not enough history to forecast. | 2029 Spring: not enough history to forecast. | 2029 Summer: not enough history to forecast. | 2029 Intersession: not enough history to forecast. |
| 2028 | 2028 Fall: not enough history to forecast. | 2028 Spring: not enough history to forecast. | 2028 Summer: not enough history to forecast. | 2028 Intersession: not enough history to forecast. |
| 2027 | 2027 Fall: not enough history to forecast. | 2027 Spring: not enough history to forecast. | 2027 Summer: not enough history to forecast. | 2027 Intersession: not enough history to forecast. |
| 2026 | 1 section | Not offered | Not offered | No data for this term |
Future estimates combine recent offering frequency with detected multi-year cycles. Hover a cell for confidence and evidence; low-confidence estimates are marked with ~.
Meeting times
Most recent first- Tue, 6:00 PM–9:00 PM JC218 2026
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Fall 2026 | Kyunghee Yoon | Lecture | 1 | 1 of 5 enrolled | 24883 |