HarvardX: Data Science: Inference and Modeling course Syllabus

Overview: This course provides a rigorous introduction to statistical inference and modeling, foundational skills for data science. Through a concept-driven approach, learners explore probability, hypothesis testing, and statistical models used to make data-driven decisions. The curriculum spans approximately 8–10 weeks with a recommended 6–8 hours per week, combining theory with real-world applications. Emphasis is placed on statistical thinking, interpretation, and understanding uncertainty in data.

Module 1: Foundations of Statistical Inference

Estimated time: 10 hours

• Role of statistical inference in data science
• Differentiating populations and samples
• Understanding sampling variability
• Core probability concepts for inference

Module 2: Probability Models and Random Variables

Estimated time: 14 hours

• Common probability distributions in data analysis
• Expected value and variance of random variables
• Modeling real-world phenomena with probability
• Understanding randomness and distributional assumptions

Module 3: Hypothesis Testing and Confidence Intervals

Estimated time: 16 hours

• Principles of hypothesis testing
• Constructing and interpreting confidence intervals
• Understanding p-values and statistical significance
• Identifying common misinterpretations and pitfalls

Module 4: Statistical Modeling and Interpretation

Estimated time: 16 hours

• Building models for explanation and prediction
• Interpreting model parameters
• Evaluating model assumptions
• Understanding uncertainty and limitations in models

Module 5: Variability, Bias, and Modeling Trade-offs

Estimated time: 12 hours

• Sources of variability in data
• Identifying and addressing bias
• Trade-offs in model selection
• Strengthening analytical reasoning for evidence-based conclusions

Module 6: Final Project

Estimated time: 20 hours

• Analyze a real-world dataset using inference methods
• Construct confidence intervals and perform hypothesis tests
• Interpret modeling results and communicate findings

Prerequisites

• Basic understanding of descriptive statistics
• Familiarity with fundamental probability concepts
• Prior exposure to data analysis concepts recommended

What You'll Be Able to Do After

• Apply statistical inference to real-world data problems
• Quantify uncertainty using probability and sampling distributions
• Perform hypothesis testing and interpret p-values correctly
• Build and evaluate statistical models for decision-making
• Understand variability, bias, and trade-offs in modeling