HarvardX: Data Science: Probability course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction to Probability

Estimated time: 6 hours

• Define probability and its role in data science
• Understand random experiments, outcomes, and sample spaces
• Identify events and their complements
• Build intuition using real-world examples of randomness

Module 2: Probability Rules and Distributions

Estimated time: 10 hours

• Apply basic rules of probability
• Use combinatorics to count outcomes
• Understand discrete probability distributions
• Model random processes using probability distributions

Module 3: Conditional Probability and Bayes’ Theorem

Estimated time: 10 hours

• Define conditional probability
• Assess independence of events
• Apply Bayes’ theorem to update beliefs
• Solve real-world problems using probabilistic reasoning

Module 4: Random Variables and Expectation

Estimated time: 10 hours

• Define random variables and their types
• Compute expected value and variance
• Interpret expectation in decision-making contexts
• Analyze variability in data science scenarios

Module 5: Foundations for Inference and Modeling

Estimated time: 8 hours

• Connect probability to statistical inference
• Understand uncertainty and risk in modeling
• Prepare for machine learning and advanced statistics

Module 6: Final Project

Estimated time: 6 hours

• Apply probability concepts to a real-world dataset
• Calculate probabilities and interpret results
• Present findings with clear probabilistic reasoning

Prerequisites

• Familiarity with basic algebra
• Basic understanding of data analysis concepts
• High school-level mathematical maturity

What You'll Be Able to Do After

• Explain core probability concepts in data science
• Apply probability rules to real-world problems
• Compute and interpret conditional probabilities and Bayes’ theorem
• Understand and use random variables and expectation
• Build a strong foundation for advanced statistics and machine learning