• Course Code: ST101
  • Instructor: Sebastian Thrun (CEO)
  • Course Developer: Adam Sherwin

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Course Notes

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Week 1: Visualizing Relationships in Data

Teaser, Looking at Data, Scatter Plots, Bar Charts, Pie Charts, Programming Charts (Optional), Admissions Case Study, Problem Set 1.

Week 2: Probability

Probability, Conditional Probability, Bayes Rule, Programming Bayes Rule (Optional), Probability Distributions, Correlation vs. Causation, Problem Set 2.

Week 3: Estimation

Estimation, Averages, Variance, Programming Estimators (Optional), Problem Set 3.

Week 4: Outliers and Normal Distribution

Outliers, Binomial Distribution, Central Limit Theorem, Central Limit Theorem Programming (Optional), The Normal Distribution, Manipulating Normals, Most Better than Average, Problem Set 4.

Week 5: Inference

Sebastian's Weight and Proofs (Optional), Confidence Intervals, Normal Quantiles, Hypothesis Test, Hypothesis Test 2, Programming Tests and Intervals (Optional), Problem Set 5.

Week 6: Regression

Regression, Correlation, Monty Hall Problem (Optional), Weight Case Studies, Flash Crash Example, Challenger Example, Problem Set 6.

Week 7: Final Exam

Test your understanding of the material.