CS 533 Fall 2022
  • CS 533 Homepage

Course Info

  • Syllabus
    • BSU COVID-19 Statement
    • Course & Instructor
    • Resources and Readings
    • Course Structure
    • Course Policies
  • Schedule
  • Glossary

Content

  • Week 0 — Pre-Class Welcome
  • Week 1 — Questions (8/22–26)
    • Demo Notebook
    • Data Types and Operations
  • Week 2 — Description (8/29–9/2)
    • Introducing Pandas
    • Aggregates and Groups
    • Describing Distributions
  • Week 3 — Presentation (9/5–9)
  • Week 4 — Inference (9/12–16)
  • Week 5 — Filling In (9/19–23)
  • Week 6 — Two Variables (9/26–30)
  • Week 7 — Getting Data (10/3–7)
  • Week 8 — Regression (10/10–14)
  • Week 9 — Models & Prediction (10/17–21)
  • Week 10 — Classification (10/24–28)
  • Week 11 — More Modeling (10/31–11/4)
  • Week 12 — Text (11/7–11)
  • Week 13 — Unsupervised (11/14–18)
  • Week 14 — Workflow (11/28–12/2)
  • Week 15 — What Next? (12/5–9)

Assignments

  • Rubric
  • Assignment 0
    • CS 533 Assignment 0
  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Assignment 4
  • Assignment 5
  • Assignment 6
  • Assignment 7

Resources

  • Resource Overview
  • Software and Installation
  • Documentation & Reading
  • Data Sets
  • Notebook Checklist & Guide
  • Notes on Probability
  • Common Problems
  • Remotely Using Onyx
  • Git Resources
  • Software Environments
  • Tutorials
    • Advanced Pipeline Example
    • Tricks with Boolean Series
    • Building Data
    • Finishing Touches
    • Drawing Charts
    • Drawing Charts
    • Clustering Example
    • Confidence
    • Correlation and Basic Linear Model
    • Charting Movie Scores
    • Distributions
    • Empirical Probabilities
    • Bibliography Fetch
    • Fun with Numbers
    • Writing Functions
    • Indexing
    • Logistic Regression Demo
    • MovieLens Time Series
    • Magic Numbers
    • Rebuilding Regression
    • Missing Data
    • Movie Matrix Decomposition
    • One Sample
    • Overfitting Simulation
    • PCA Demo
    • Sampling and Testing the Penguins
    • Random Numbers
    • Why does Random Search Work?
    • Regressions
    • Reshaping Data
    • Sampling Distributions
    • SciKit Logistic Regression Demo
    • SciKit Pipeline and Transform Demo
    • SciKit-Learn Linear Regression Demo
    • SciKit Transformations
    • Selecting Data
    • Sessionization
    • Spam Detector Example
    • Tuning Example
    • Data Types and Operations
    • Using Census Data

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    • Fall 2021
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Resource Overview

Resource Overview#

  • Documentation & Readings

  • Data Sets

  • Notebook Checklist

  • Common Problems

  • Remotely Using Onyx

  • Notes on Probability

  • Git Resources

  • Software

  • Tutorials

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Software and Installation

By Michael D. Ekstrand
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