| 1/16 |
Course Overview and Review of Python and Probability
screencast slides notebook |
chapter 1 |
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| 1/18 |
Data Design and Sources of Bias
screencast slides notebook |
chapter 2 |
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| 1/23 |
Data Manipulation using Pandas
screencast slides notebook |
chapter 3 |
Lab 1: Setup |
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HW 1: Prereqs and Image Classification |
| 1/25 |
Data Manipulation using Pandas
screencast slides notebook |
chapter 3 |
Disc 1: Probability and Sampling
(Solutions) |
vitamin 1 |
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| 1/30 |
Data Cleaning and EDA
screencast slides notebook |
chapter 4 |
Lab 2: Pandas |
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HW 2: Food Safety |
| 2/01 |
EDA and Visualization
screencast slides notebook |
chapter 5 |
Disc 2: Bayes’ Rule and Data Visualization (Solutions) |
vitamin 2 |
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| 2/06 |
Visualization and Data Transformations
screencast notebook |
chapter 6 |
Lab 3: Plotting |
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HW 3: Bike Sharing |
| 2/08 |
Visualization and Data Transformations
screencast slides notebook |
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Disc 3: Data Visualization and Log Transforms (Solutions) |
vitamin 3 |
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| 2/13 |
Web Technologies
screencast slides notebook |
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Lab 4: Plotting, Smoothing, Transformation |
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Project 1: Twitter Analysis |
| 2/15 |
Working With Text
screencast slides notebook |
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Disc 4: Regular Expressions (Solutions) |
vitamin 4 |
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| 2/20 |
REST & Relational Databases and SQL
screencast slides notebook |
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No Lab: Holiday (President’s Day) |
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| 2/22 |
More Advanced SQL
screencast slides notebook |
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Disc 5: SQL (Solutions) |
vitamin 5 |
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| 2/27 |
Modeling and Estimation
slides notebook 1 notebook 2 screencast |
chapter 10 |
Lab 6: Regular Expressions, SQL |
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HW 4: SQL |
| 3/1 |
Gradient Descent for Model Estimation
screencast slides notebook |
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Disc 6: Loss Functions & Gradient Descent (Solutions) |
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| 3/6 |
Midterm Review
screencast slides |
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| 3/8 |
Midterm
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| 3/13 |
Generalization and Empirical Risk Minimization
screencast slides |
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Lab 8: Modeling and Estimation |
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Homework 5: Modeling |
| 3/15 |
The Bias Variance Tradeoff and Regularization
slides screencast notebook |
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Disc 7: Bias Variance Tradeoff & Regularization (Solutions) |
vitamin 6 |
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| 3/20 |
Linear Regression and Feature Engineering
screencast slides |
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Lab 9: Bootstrap |
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Homework 6: Feature Engineering & Linear Models |
| 3/22 |
Cross-Validation, Regularization, and Begin Classification
screencast slides notebook |
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Disc 8: Regularization, Cross Validation, Geometric Interpretation (Solutions) |
vitamin 7 |
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| 4/3 |
Classification and Logistic Regression
screencast slides notebook 1 notebook 2 |
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Lab 10: Feature Engineering and Cross-Validation |
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| 4/5 |
Classification and Logistic Regression (Part 2)
screencast slides notebook |
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Disc 9: Logistic Regression & Bootstrap (Solutions) |
vitamin 8 |
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| 4/10 |
Probability theory, Monte Carlo Simulation, and Bootstraping
screencast slides |
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Lab 11: Logistic Regression |
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| 4/12 |
Hypothesis Testing
slides screencast notebook |
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Disc 10: Hypothesis Testing & Bootstrap (Solutions) |
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| 4/17 |
P-values, Probability, Priors, Rabbits, Quantifauxcation, and Cargo-Cult Statistics
screencast slides |
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Lab 12: Hypothesis Testing, Baby Weights |
vitamin 9 |
Project 2: Spam vs. Ham Classification |
| 4/19 |
Big Data
screencast slides notebook |
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Disc 11: Hypothesis Testing (Solutions) |
vitamin 10 |
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| 4/24 |
[See Syllabus] |
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| 4/26 |
[See Syllabus] |
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Disc 12: Final Review (Solutions | Slides) |
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