DATA 301 Introduction to Data Science

Notes

• Intro
  • Introduction to Notebooks and Google Colab
• Quiz 1
  • Tabular Data
  • Rows and Observational Units
  • Columns and Variables
  • Lecture 1: Introduction to Tabular Data
• Quiz 2
  • Summarizing One Categorical Variable
  • Summarizing Two Categorical Variables
  • Lecture 2: Categorical Variables
• Quiz 3
  • Multi Way Tables and Simpson's Paradox
  • Summarizing a Quantitative Variable
  • Lecture 3A: Multi-way Tables and Simpson's Paradox
  • Lecture 3B: Summarizing a Quantitative Variable
• Quiz 4
  • Transforming Variables
  • Summarizing Two Quantitative Variables
  • Lecture 4: Quantitative Variables
• Quiz 5
  • Split-Apply-Combine
  • Moving Beyond Two Variables
  • Lecture 5: Split-Apply-Combine and Summarizing Categorical and Quantitative Variables
• Quiz 6
  • The Grammar of Graphics
  • Lecture 6: Data Visualization
• Quiz 7
  • Distances between Observations
  • Lecture 7: Distances Between Observations
• Quiz 8
  • Hierarchical Data and JSON
  • Lecture 8: Hierarchical Data and JSON Data Format
• Quiz 9
  • The XML Data Format
  • HTML and Web Scraping
  • Lecture 09: XML, HTML, and Web Scraping
• Quiz 10
  • Bag of Words and N-Grams
  • The Vector Space Model
  • Lecture 10: Intro to Text Data
• Quiz 11
  • Concatenating and Merging Data
  • Types of Joins
  • Lecture 11: Concatenating and Merging (Joining) Data
    • Solutions
• Quiz 12
  • Linear Regression
  • Categorical Features in Regression Models
  • Lecture 12: Linear Regression
• Quiz 13
  • K-Nearest Neighbors for Regression
  • Lecture 13: K-Nearest Neighbors for Regression
• Quiz 14
  • Training and Test Errors
  • Estimating the Test Error
  • Lecture 14: Training and Test Errors
• Quiz 15
  • Model Selection and Hyperparameter Tuning
  • Lecture 15: Model Selection and Hyperparameter Tuning
• Quiz 16
  • Ensemble Models for Regression
  • Lecture 16: Ensemble Models for Regression
• Quiz 17
  • K-Nearest Neighbors for Classification
  • Evaluating Classification Models
  • Lecture 17: K-Nearest Neighbors for Classification
• Quiz 18
  • Estimating Test Metrics for Classification Models
  • Lecture 18: Estimating Test Metrics for Classification
• Quiz 19
  • Time Series Data
  • Time Series Modeling and Forecasting
  • Lecture 19: Time Series
• Quiz 20
  • K Means Clustering
  • Lecture 20: Unsupervised Learning - K Means Clustering
• Quiz 21
  • Hierarchical Clustering
  • Lecture 21: Hierarchical Clustering

Assignments

• Assignment 1
  • PDF
• Assignment 2A
  • PDF
• Assignment 2B
  • PDF
• Assignment 3
  • PDF
• Assignment 4
  • PDF
• Assignment 5A
  • PDF
• Assignment 5B
  • PDF
• Assignment 6
  • Day 16 PDF
  • Day 18 PDF

Project

• Phase 2
  • Collect and Clean Notebook
  • Visualizations
  • Executive Summary Notebook