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GRATIS
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Duke University vía Coursera
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Data Analysis and Statistical Inference

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Week 1: Unit 1 - Introduction to data
  • Part 1 – Designing studies
  • Part 2 – Exploratory data analysis
  • Part 3 – Introduction to inference via simulation
Week 2: Unit 2 - Probability and distributions
  • Part 1 – Defining probability
  • Part 2 – Conditional probability
  • Part 3 – Normal distribution
  • Part 4 – Binomial distribution
Week 3: Unit 3 - Foundations for inference
  • Part 1 – Variability in estimates and the Central Limit Theorem
  • Part 2 – Confidence intervals
  • Part 3 – Hypothesis tests
Week 4: Finish up Unit 3 + Midterm
  • Part 4 – Inference for other estimators
  • Part 5 - Decision errors, significance, and confidence
Week 5: Unit 4 - Inference for numerical variables
  • Part 1 – t-inference
  • Part 2 – Power
  • Part 3 – Comparing three or more means (ANOVA)
  • Part 4 – Simulation based inference for means
Week 6: Unit 5 - Inference for categorical variables
  • Part 1 – Single proportion
  • Part 2 – Comparing two proportions
  • Part 3 – Inference for proportions via simulation
  • Part 4 – Comparing three or more proportions (Chi-square)
Week 7: Unit 6 - Introduction to linear regression
  • Part 1 – Relationship between two numerical variables
  • Part 2 – Linear regression with a single predictor
  • Part 3 – Outliers in linear regression
  • Part 4 – Inference for linear regression
Week 8: Unit 7 - Multiple linear regression
  • Part 1 – Regression with multiple predictors
  • Part 2 – Inference for multiple linear regression
  • Part 3 – Model selection
  • Part 4 – Model diagnostics
Week 9: Review / catch-up week
  • Bayesian vs. frequentist inference
Week 10: Final exam