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This Week

Exam Week

Final Exam on Tuesday, Jun 08

Discussion 8

Was due Saturday, Jun 12 at 23:59 PM

Week 10

Conclusion

Lecture 18 — Model Evaluation and Fairness

  1. Sensitivity, Specificity, Precision, Recall
  2. Model Evaluation and Individuals
  3. Fairness and Bias in ML Models
  4. Parity Measures
  5. Parity Example: Loans and Age

Week 9

Model Selection

Lecture 17 — Examples

  1. Grid Search Cross-Validation
  2. Examples: Multicollinearity
  3. Example: Text Data

Lecture 16 — Model Validation

  1. Model Validation
  2. Example: Polynomial Regression
  3. Example: Decision Trees

Discussion 7

Was due Saturday, May 29 at 23:59 PM

Lab 9

Was due Tuesday, Jun 01 at 23:59 PM

Project 5

Was due Monday, Jun 07 at 23:59 PM

Week 8

Prediction and sklearn

Lecture 15 — Model Pipelines

  1. sklearn Transformers and Models
  2. Pipelines
  3. Intro to Bias and Variance

Lecture 14 — Features

  1. Features
  2. Feature Engineering
  3. Features and Models
  4. Example: Predicting Tips
  5. Example: Adding Features to Predict Tips

Lab 8

Was due Tuesday, May 25 at 23:59 PM

Project 4

Was due Thursday, May 27 at 23:59 PM

Week 7

Regex and NLP

Lecture 13 — Extracting Information from Text

  1. Cleaning the SD City Salary Job Titles
  2. Bag of Words
  3. Tf-Idf

Lecture 12 — Text Data

  1. Canonicalization
  2. Intro to Regular Expressions
  3. Regex Building Blocks
  4. Extended Regex

Discussion 7

Was due Saturday, May 15 at 23:59 PM

Lab 7

Was due Tuesday, May 18 at 23:59 PM

Project 4 Checkpoint

Was due Thursday, May 20 at 23:59 PM

Week 6

HTTP and HTML

Midterm on Thursday, May 06

Lecture 11 — Parsing Nested Data

  1. HTML
  2. Parsing HTML
  3. Example: Scraping Quotes
  4. Nested vs. Flat Data Structures
  5. API Requests

Discussion 6

  • Friday May 7 at 13:00 PM via Zoom
  • Assignment is Extra Credit
Was due Saturday, May 08 at 23:59 PM

Lab 6

Was due Tuesday, May 11 at 23:59 PM

Week 5

Missingness

Lecture 10 — Collecting Data

  1. Collecting Data
  2. HTTP Requests and JSON
  3. APIs vs Scraping

Lecture 9 — Imputation

  1. Introduction to Data Imputation
  2. What to do with missing data
  3. Mean imputation
  4. Probabilistic imputation

Discussion 5

Was due Saturday, May 01 at 23:59 PM

Lab 5

Was due Tuesday, May 04 at 23:59 PM

Project 3

Was due Thursday, May 13 at 23:59 PM

Week 4

Permutation Tests

Lecture 8 — Missingness

  1. Missingness Mechanisms
  2. Formal Definitions of Missingness
  3. Assessing Missingness Quantitatively
  4. Kolmogorov-Smirnov Test Statistic
  5. Summary and Examples

Lecture 7 — Permutation Tests

  1. Permutation Tests
  2. TVD as a Test Statistic
  3. Speeding up Permutation Tests

Discussion 4

Was due Saturday, Apr 24 at 23:59 PM

Lab 4

Was due Tuesday, Apr 27 at 23:59 PM

Project 2

Was due Thursday, Apr 29 at 23:59 PM

Week 3

Data Granularity

Lecture 6 — Combining Data

  1. Concatenating Vertically
  2. Working with Times
  3. Concatenating Horizontally
  4. Joining with pd.merge
  5. Many-to-* Joins, and a Demo

Lecture 5 — Data Granularity

  1. Data Granularity and groupby
  2. Pivot Tables
  3. Simpson's Paradox

Discussion 3

Was due Saturday, Apr 17 at 23:59 PM

Lab 3

Was due Tuesday, Apr 20 at 23:59 PM

Project 2 Checkpoint

Was due Thursday, Apr 22 at 23:59 PM

Week 2

Messy Data and Testing

Lecture 4 — Hypothesis Testing

  1. Hypothesis Testing and Coin Flips
  2. Jury Panels and the TVD
  3. P-Values

Lecture 3 — Messy Data

  1. Introduction to data cleaning
  2. Cleaning up data types
  3. Unfaithful data
  4. Missing values

Discussion 2

Was due Saturday, Apr 10 at 23:59 PM

Lab 2

Was due Tuesday, Apr 13 at 23:59 PM

Project 1

Was due Thursday, Apr 15 at 23:59 PM

Week 1

Introduction

Welcome to DSC 80!

Here's how to get started:

  • read the syllabus
  • join our campuswire and gradescope with the email invitations you received earlier this week
  • watch the intro lecture linked below when it is posted on Tuesday

See you in lecture.

Lecture 2 — Tabular Data

  1. Tabular Data in Pandas
  2. Pandas and Numpy
  3. Useful Pandas Methods

Lecture 1 — Introduction

  1. Introduction
  2. Course Structure
  3. The Data Science Lifecycle

Discussion 1

Was due Saturday, Apr 03 at 23:59 PM

Lab 1

Was due Tuesday, Apr 06 at 23:59 PM

Project 1 Checkpoint

Was due Thursday, Apr 08 at 23:59 PM