COP 4283 – Data Science Programming
Course Description
Programming is an essential skill for data scientists. This course introduces practical programming for data processing, exploration, and visualization using Python. Students learn to read, clean, transform, analyze, and communicate with data. Visualization covers Matplotlib/Seaborn and Vega-Lite for web-based charts. Assignments and a small team presentation are based on analytical tasks common in data science.
Note:
The course no longer teaches R/Julia; the focus is Python + visualization tools.
- Python for Data Analysis: language basics, file handling, functions, control flow, and core data structures.
- NumPy & Pandas: arrays and tabular data, cleaning, joins, reshaping, EDA, and descriptive statistics.
- Visualization: Matplotlib & Seaborn fundamentals; web charts with Vega-Lite/Altair.
- Problem-Solving: structuring analyses, communicating results, and practicing teamwork.
Course Outcomes
- Apply Python and data science libraries using best practices in software development.
- Function effectively in teams to accomplish a common analytical goal.
- Use current techniques, skills, and tools necessary for computing practice.
- Demonstrate knowledge of, and ability to apply, programming fundamentals to solve data-driven problems.
Materials
Assignments & Grading
All submissions are electronic via Canvas unless otherwise noted. Deadlines are 11:59 PM Eastern Time unless otherwise stated.
Late submissions incur 10% per day up to 5 days; after 5 days, work is not accepted. Exceptions apply only to documented emergencies. Each student has two late days for the semester (each late day allows up to 24 hours without penalty).
| Category | Percentage | Notes |
|---|---|---|
| Attendance | 10% | Taken through in-class activities and quizzes |
| Quizzes | 10% | Bi-weekly short quizzes on Canvas |
| Midterm Exam | 20% | Week 9, covers Python and data wrangling |
| Scripting Assignments | 30% | Individual Python assignments |
| Final Project | 30% | Team-based (≈5 students). Proposal (5%), Check-in (5%), Final Report & Code (15%), Presentation (5%). |
- Proposal (5%) – Topic, dataset, team roles.
- Check-in (5%) – Initial dataset and cleaning plan.
- Final Report & Code (15%) – Cleaned dataset, analysis, visualizations, reproducible notebook/data dashboard.
- Presentation (5%) – In-class talk with polished visuals and findings.
Combination of multiple-choice and free-response questions.
| Letter Grade | Percentage |
|---|---|
| A | 94–100% |
| A- | 90–93% |
| B+ | 87–89% |
| B | 84–86% |
| B- | 80–83% |
| C+ | 77–79% |
| C | 74–76% |
| C- | 70–73% |
| D+ | 67–69% |
| D | 64–66% |
| D- | 61–63% |
| F | 0–60% |
Academic Integrity & Student Conduct
Please read and understand student rights and responsibilities in UCF’s Golden Rule: goldenrule.sdes.ucf.edu/2e_Rules.html.
All instructors must document academic activity at the start of the course. Complete the required activity by the end of week 1 (or ASAP after adding) to avoid delays in financial aid disbursement.
Artificial Intelligence (AI) Use Policy
Students may use Generative AI tools (e.g., Copilot, ChatGPT) for explanation, guidance, and brainstorming. Direct copy-pasting of AI-generated code or text is not permitted. If AI tools are used, briefly note how they were applied (e.g., “Used ChatGPT to clarify how list comprehensions work”). Students are ultimately responsible for understanding and verifying all submitted work.
Instructor & Contact
Other Notes
- Please email yu.fu@ucf.edu with subject
[COP4283]for faster resolution of course issues. - For office visits, let the instructor know in advance if possible.
- The syllabus and calendar are subject to change; updates on Webcourses.
- Be mindful of the Grade-Due-to-the-College deadline; resolve issues before this date.