• GA Seattle DSI
  • Introduction
  • i. Students
    • a: Onboarding
      • 1: Installfest
      • 2: Technical Guide
      • 3: Precourse Onboarding Tasks
    • b: Resources
  • ii. Projects
    • a: Weekly Projects
      • 1: SAT Scores + Summary Statistics
      • 2: Billboard Hits + Data Munging
      • 3: Liquor Sales + Linear Regression
      • 4: Web Scraping + Logistic Regression
      • 5: Disaster Relief + Classification
      • 6: IMDB API + Random Forests
      • 7: Airport Delays + Cluster Analysis
    • b: Capstone Project
      • 1: Capstone Topic + Dataset Validation
      • 2: Problem Statement + EDA
      • 3: Progress Report + Preliminary Findings
      • 4: Report Writeup + Technical Analysis
      • 5: Presentation + Recommendations
  • 1. Python
    • 1.1a Data Science Overview
    • 1.1b Command Line
    • 1.2 Git
    • 1.3 Python pt.1
    • 1.4 Python pt.2
    • 2.1 Python pt.3
    • 2.2 Lab
    • 2.3 Python pt.4
    • 2.4 Lab
    • 3.1 Programming Fundamentals
    • 3.2 iPython, Data Values, and CSV
    • 3.3 Intro to NumPy
    • 3.4 Lab
    • 4.1 Math Primer pt. 2
    • 4.2 Lab
    • 4.3 Lab
    • 4.4 Data Viz Principles
    • 5.1 Plotting Tools
    • 5.2 Lab
    • 5.3 Python List Comprehensions
  • 2. Pandas / Data Analysis
    • 1.1 Pandas pt.1
    • 1.2 Study Design
    • 1.3 Stats 101
    • 1.4 Lab
    • 2.1 Pandas pt.2
    • 2.2 Lab
    • 2.3 Data Cleaning
    • 2.4 Lab
    • 3.1 Pandas pt.3
    • 3.2 Lab
    • 3.3 Categorical & Dummy Variables
    • 3.4 Lab
    • 4.1 SciPy
    • 4.2 Lab
    • 4.3 Joins and Pandas
    • 4.4 Lab
    • 5.1 Plotting and Pandas
    • 5.2 Lab
  • 3. Linear Regression / Stats Models
    • 1.1 Modeling Linear Regression
    • 1.2 Lab
    • 1.3 Stats Models & SKLearn
    • 1.4 Lab
    • 2.1 Bias Variance Tradeoff
    • 2.2 Lab
    • 2.3 Regularization and Overfitting
    • 2.4 Lab
    • 3.1 Regression Metrics & Loss Functions
    • 3.2 Train-Test Split
    • 3.3 Lab
    • 3.4 Lab
    • 4.1 Gradient Descent
    • 4.2 Feature Scaling
    • 4.3 Study Design
    • 5.1 Stakeholder Analysis
    • 5.2 Lab
  • 4. Logistic Regression
    • 1.1 Classification
    • 1.2 Web Scraping
    • 1.3 Lab
    • 1.4 Lab
    • 2.1 Logistic Regression
    • 2.2 Lab
    • 2.3 Model Fit
    • 2.4 Lab
    • 3.1 Visualizing Classification Models
    • 3.2 Lab
    • 4.1 Advanced Model Evaluation
    • 4.2 Lab
    • 4.3 Lab
    • 5.1 Communicating Results
    • 5.2 Lab
  • 5. Classification / Databases
    • 1.1 Different Databases
    • 1.2 SQL pt.1
    • 1.3 Lab
    • 1.4 Lab
    • 2.1 Classification Case Studies
    • 2.2 Pipelines and Custom Transformers in SKLearn
    • 2.3 Lab
    • 2.4 Lab
    • 3.1 SQL pt.2
    • 3.2 Lab
    • 3.3 Lab
    • 4.1 SQL Feature Selection
    • 4.2 Lab
    • 5.1 Support Vector Machines
    • 5.2 Lab
  • 6. Trees & Ensemble Methods
    • 1.1 Classification and Regression Trees (CARTs)
    • 1.2 Lab
    • 1.3 Servers, JSON, & APIs
    • 1.4 Lab
    • 2.1 SQL Joins
    • 2.2 Lab
    • 2.3 Ensemble Methods - Decision Trees and Bagging
    • 2.4 Lab
    • 3.1 Ensemble Methods - Random Forests and Boosting
    • 3.2 Lab
    • 3.3 Model Evaluation & Feature Importance
    • 3.4 Lab
    • 4.1 Feature Extraction from Text
    • 4.2 Lab
    • 5.2 Communicating results
  • 7. Unsupervised Learning
    • 1.1 Intro to Clustering
    • 1.2 Lab
    • 1.3 Tuning Clusters
    • 1.4 Advanced Database Skills
    • 2.1 Dimensionality Reduction
    • 2.2 Intro to Principal Component Analysis
    • 2.3 Lab
    • 2.4 Lab
    • 3.1 Hierarchical Clustering
    • 3.2 Hierarchical Clustering
    • 3.3 Lab
    • 3.4 Unsupervised Learning Trends
    • 4.2 DBSCAN
    • 4.3 Lab
    • 5.1 Unsupervised Learning Case Study
  • 8. Bayesian Inference
    • 1.1 Bayes
    • 1.2 Lab
    • 1.3 Bayes Deep Dive
    • 1.4 Lab
    • 2.1 Lab
    • 2.2 Lab
    • 2.3 Lab
    • 2.4 Lab
    • 3.1 API Review
    • 3.2 Lab
    • 3.3 LDA
    • 3.4 Lab
    • 4.1 Bayesian Stat Testing
    • 4.2 Lab
    • 4.3 Naive Bayes
    • 4.4 Lab
    • 5.3 Communicating Bayesian Results
  • 9. Time Series Data
    • 1.1 Github for Teams
    • 1.2 Lab
    • 1.3 Pandas & Time Series Data
    • 1.4 Lab
    • 2.1 Lab
    • 2.2 Autocorrelation & Time Series Data
    • 2.3 Lab
    • 3.1 ARIMA Model pt.1
    • 3.2 Lab
    • 4.1 ARIMA Model pt.2
    • 4.2 Lab
    • 5.1 Time Series Analysis Recap
    • 5.2 Lab
  • 10. Intro to Big Data
    • 1.1 Intro to Big Data
    • 1.2 Lab
    • 1.3 Lab
    • 1.4 Lab
    • 2.1 AWS Amazon Web Services
    • 2.2 AWS Elastic Map Reduce
    • 2.3 Lab
    • 2.4 Big Data Case Studies
    • 3.1 Spark Overview
    • 3.2 Lab
    • 3.3 Lab
    • 3.4 Spark Case Studies
    • 4.1 Database Design Case Study
    • 5.1 Big Data Review Case Study
  • 11. Advanced Topics & Interviews
    • 1.1 Intro to A/B Testing
    • 1.2 Lab
    • 3.1 Introductory Career Tips
    • 3.2 Lab
    • 4.1 Interview Prep
    • 4.2 Lab
    • 5.1 Data Science Portfolios
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6. Trees & Ensemble Methods

DSI - Week 6

This is a summary of the materials provided for Week 6 of the Data Science Immersive.

Week 6: Trees & Ensemble Methods

Session Time Day 1 Day 2 Day 3 Day 4 Day 5
9-10 (Project Review) Morning Exercise (Outcomes) Morning Exercise (Reflection)
10-11:30 Intro to CARTS SQL Joins Random Forests and Boosting Intro to NLP Capstone Pt 1: Presentations
11:30-1 CARTS Lab Join API Data Lab Practice Methods & Visualize Results NLTK Lab Communicating Models
2-3:30 Servers, JSON, & APIs Decision Trees and Bagging Model Evaluation & Feature Importance Project 6: Workshop Project 6: Workshop
3:30-5 APIs & Classification Tree Lab Practice Methods With Sklearn Model Comparison Lab +Flex: Workshop Project 6: Workshop

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