Data Science Foundations

Review and master the foundational principles of data science, moving from theoretical underpinnings to practical application and ethical considerations, in preparation for a comprehensive assessment.

Course Outline

The mathematical & statistical core

Lecture 1. Probability & Inference: the language of uncertainty
Lecture 2. Statistical distributions
Lecture 3. Hypothesis testing
Lecture 4. Essential linear algebra & calculus

Working with data

Lecture 5. Wrangling with Pandas and NumPy
Lecture 6. Structured Query Language (SQL)
Lecture 7. Exploratory Data Analysis (EDA)
Lecture 8. Visual Storytelling

Predictive modelling

Lecture 9. Regression models
Lecture 10. Classification models
Lecture 11. Unsupervised Learning
Lecture 12. Model evaluation
Lecture 13. Feature Engineering

Advanced topics

Lecture 14. Experimentation (A/B testing)
Lecture 15. Decision making