| layout |
faimg |
title |
page |
calendar |
Lectures |
This page lists the class lectures and recitations, plus additional material (slides, notes, video) associated with each lecture.
| Date |
Topic |
Slides |
Notes |
Video |
Quiz |
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Data collection and management |
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| 1/17 |
Introduction |
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| 1/22 |
Data collection and scraping |
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| 1/24 |
Jupyer notebook lab |
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| 1/29 |
Relational data |
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| 1/31 |
Visualization and data exploration |
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| 2/5 |
Vectors, matrices, and linear algebra |
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| 2/7 |
Graph and network processing |
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| 2/12 |
Free text and natural language processing |
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| 2/14 |
Geographic information systems and spatial data |
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| 2/19 |
Overflow lecture |
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Statistical modeling and machine learning |
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| 2/21 |
Linear regression |
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| 2/26 |
Linear classification |
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| 2/28 |
Nonlinear modeling, cross-validation |
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| 3/5 |
Evaluating machine learning models |
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| 3/7 |
Basics of probability |
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| 3/12 |
Spring break |
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| 3/14 |
Spring break |
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| 3/19 |
Hypothesis testing and experimental design |
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| 3/21 |
Decision trees, interpretable models |
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Advanced modeling techniques |
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| 3/26 |
Clustering and dimensionality reduction |
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| 3/28 |
Anomaly detection |
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| 4/2 |
Recommender systems |
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| 4/4 |
Deep learning |
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| 4/9 |
Overflow lecture |
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Additional topics |
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| 4/11 |
High dimensional visualization |
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| 4/16 |
Probabilistic modeling |
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| 4/18 |
Big data and MapReduce methods |
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| 4/23 |
Debugging data science |
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| 4/25 |
A data science walkthrough |
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| 4/30 |
Data science jobs |
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| 5/2 |
The future of data science and Q&A |
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