CS109 Data Science

Week 1 (Mon Feb 19 - Fri Feb 23)

• Lecture 1: Course Overview (Feb 25)

Week 2 (Mon Feb 26 - Fri Mar 2)

• Lab 1: Pandas, Python, and Github (Feb 26)
• Lecture 2: Web Scraping. Regular Expressions. Data Reshaping. Data Cleanup. Pandas. (Feb 27)
• Lecture 3: Exploratory Data Analysis (Feb 28)

Week 3 (Mon Mar 5 - Fri Mar 9)

• Lab 2: Scraping, Pandas, Python, and viz (Mar 2)
• Lecture 4: Pandas, SQL, and the Grammar of Data (Mar 6)
• Lecture 5: Statistical Models (Mar 10)

Week 4 (Mon Mar 12 - Fri Mar 16)

• Lab 3: Probability, Distributions, and Frequentist Statistics (Mar 14)
• Lecture 6: Story Telling and Effective Communication (Mar 17)
• Lecture 7: Bias and Regression (Mar 17)

Week 5 (Mon Mar 19 - Mar 23)

• Lab 4: Regression, Logistic Regression: in sklearn and statsmodels(Mar 22)
• Lecture 8: More Regression
• Lecture 9: Classification. kNN. Cross Validation. Dimensionality Reduction. PCA. MDS.

Week 6 (Mon Apr 2 - Fri Apr 6)

• Lab 5: Machine Learning
• Lecture 10: SVM, Evaluation
• Lecture 11: Decision Trees and Random Forests

Week 7 (Mon Apr 9 - Fri Apr 13)

• Lab 6: Machine Learning 2
• Lecture 12: Ensemble Methods.
• Lecture 13: Best Practices

Week 8 (Mon Apr 16 - Fri Apr 20)

• Lab 7: Ensembles
• Lecture 14: Best Practices, Recommendations and MapReduce.
• Lecture 15: MapReduce Combiners and Spark

Week 9 (Mon Apr 23 - Fri Apr 27)

Week 10 (Mon Apr 25 - Fri Apr 29)

Week 11 (Mon Apr 30 - Fri May 4)

Week 12 (Mon May 7 - Fri May 11)

Week 13 (Mon May 14 - Fri May 18)