Due date Topic Readings Assignment Project Slides
8/22 Intro 50 years of DS (to 8.3)(*) Piazza response & intro CATME survey slides
8/24 What is Data Science? ISLR 1,2 HW0.ipynb
HW0.pdf
8/31 Scientific Method 1) End of Theory
2) Big Data Science (*)
project descriptions slides
9/5 Data Management Best Practices (*) group project bid due (email) slides (ppt)
9/7 Stats 101 notes
9/12 Frequentist vs Bayes Why isn't Everyone Bayesian (*) HW1.ipynb
HW1.pdf
slides
9/14 Model evaluation ISLR 5-5.2 Tech. Lecture Topic due slides
9/19 Intro to ML Two Cultures (to 215) (*) Data Management plan slides
9/21 Linear Regression ISLR 3-3.3,6.2 slides
9/26 Common ML algorithms ISLR 4.4, 8.1, 9.1, 10.2 Naive Bayes HW2.ipynb
HW2.pdf
slides
9/28 Blackbox ML ISLR 4.4, 8.1, 9.1, 10.2 Neural Nets slides
10/3 Hypothesis Testing Team Eval due slides
10/5 Optimization slides
10/10 Reproducibility Nature
OpenScience(*)
HW1ans.ipynb
HW1ans.pdf
10/12 Tech lecture Time Series notes powerpoint
10/17 Fall Break
10/19 Tech lecture Data Visualization notes slides
10/24 Tech lecture Natural Language Processing notes slides
10/26 Tech lecture GIS notes slides
10/31 Network Tech talk slides
11/2 Uber guest lecture inputDigits.csv
labelDigits.csv
HW3.ipynb
HW3.pdf
11/7 How to write a research paper
11/9 Guest Lecture Summary of project, outline of paper
11/14 Data Anonymization and Aggregation
11/16 Biases in Algorithms podcast
ACM
538 (*)
11/21 How to give a reserch talk
11/23 Thanksgiving
11/28 Paper workshopping Homework 3 due
11/30 Paper workshopping
12/5 Presentation
12/7 Presentation
12/13 1:30-4:00pm Presentations Final Paper due by 1:30pm
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