Course Description
Problem-based learning introduction to Data Science, including programming with data; data mining, munging, and wrangling; statistics, analytics, and visualization, towards scientific, social, and environmental challenges. Prerequisite: Permission of instructor (prior programming and statistics experience is required).
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- Understand the complete data science workflow and utilize modern tools for data analysis and software development.
- Apply Python programming, data manipulation, and statistical techniques to preprocess, analyze, and interpret real-world datasets.
- Create effective data visualizations and communicate analytical findings through appropriate graphical representations.
- Develop and evaluate predictive models using fundamental machine learning techniques.
- Build AI-assisted data science applications by integrating Streamlit, LangChain, and large language models into the data analysis workflow.
- Design, implement, and present an end-to-end data science project while critically evaluating AI-generated results and demonstrating responsible use of AI in data science.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Assignments | 26 points |
| In-class quizzes | 24 points |
| Project | 40 points |
| Research Report | 10 points |
Grade | Point/Percentage Total
| Percentage | Letter Grade |
|---|---|
| 92% - 100% | A |
| 89% - 92% | A- |
| 86% - 89% | B+ |
| 83% - 86% | B |
| 80% - 83% | B- |
| 77% - 80% | C+ |
| 74% - 77% | C |
| 70% - 74% | C- |
| 67% - 70% | D+ |
| 64% - 67% | D |
| 60% - 64% | D- |
| <60% | F |
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.