Course Description
This course covers research methods in data science. The emphasis of this course is on developing the habits of sound critical thinking.
Weekly Projects
1. Guide to Evaluate Your Own Research Question
Evaluated research topics and refined goals and questions.
2. Design an Experiment
Developed methodology addressing confounding variables.
3. Creating a Sample Survey
Survey assessing student interest in sports programs.
4. Simulating With Distributions
R project simulating data distributions with visual and statistical summaries.
5. Cleaning Data
Cleaned GDP and population data, handling missing values.
6. Exploring Data
Cleaned GDP and population data for analysis.
7. Distribution Project: Quality Control Process
Analyzed widget defects and size distribution.
8. Hypothesis Testing Project: Apps
Quality control analysis of manufacturing standards.
9. t-Test Project: Customer Satisfaction
Analyzed customer satisfaction across membership types.
10. Linear Regression: Ice Cream Sales & Glucose
Two-part analysis on temperature effects and glucose levels.
11. Can We Predict Education Levels Based On Livability?
Explores factors influencing higher education pursuit.
12. Bad Santa Regression Project
Examines potential gender bias in gift-giving patterns.
Final Project
Chess's Impact on Memory
Explored whether chess mastery requires innate intelligence or develops through memory enhancement, examining whether regular chess engagement improves memory in young adults using simulated fall-semester data from The Islands platform.