DAT SF 12

Homework is to be submitted by posting it to your own github repo. Then post the URL and folder where the homework lives at here.

Tentative Course Outline

1. Intro to Data Science, Relational Databases & SQL
2. Getting started with IPython & Git
   • How to Use Git and GitHub
3. APIs and semi-structured data
   • Mining Twitter
4. IPython.parallel & StarCluster
   • Parallel Computing with IPython
5. Hadoop Distributed File System and Spark
   • AMP Camp 5 Exercises
6. Intro to ML: k-Nearest Neighbor Classification
   • Nearest Neighbor Methods (PDF Slides)
   • k-Nearest Neighbor Classification Algorithm (YouTube Video)
   • K Nearest Neighbors (Coursera Video)
   • KNN for Humans
   • Intuitive Classification using KNN and Python
   • Nearest Neighbors Classification (scikit-learn documentation)
   • Should I normalize/standardize/rescale the data?
7. Clustering: Hierarchical and K-Means
   • A Tutorial on Clustering Algorithms (web tutorial)
   • K-means (scikit-learn documentation)
   • Clustering: k-means (PDF Slides)
   • Clustering: Hierarchical Clustering (PDF Slides)
8. Probability, A/B Tests & Statistical Significance
   • Probability and Statistics (Khan Academy Course)
   • Visualizing Distributions of Data
9. Multiple Linear Regression and ANOVA
   • Plotting Linear Models using Seaborn
   • Why ANOVA and Linear Regression are the Same Analysis
   • Linear Regresion and ANOVA (Examples in R)
10. Logistic Regression and Generlized Linear Models
    • Logistic Regression in Python
    • Explaining Odds Ratios
    • GLM for Poisson Data
11. Project Elevator Pitches
    • See Student Project Repos below
12. Naïve Bayes, Cross Validation, ROC, AUC & Midterm Review - Part I
    • Receiver operating characteristic (wikipedia article)
    • Receiver Operating Characteristic (ROC) (scikit-learn documentation)
13. Naïve Bayes, Cross Validation, ROC, AUC - Part II
14. Principal Components Analysis
    • PCA and other Decomposition Examples in scikit-learn
15. Decision Trees and Forests
    • Decision Tree Learning (Wikipedia article)
    • Decision Trees
    • How to construct a tree
    • Information gain
16. Support Vector Machines
17. Bringing it Together
18. Final Project Working Session
19. Final Project Working Session
20. Final Project Presentations (12 min. each)
21. Final Project Presentations (12 min. each)
22. Future Directions

Project Schedule

Student Project Repos:

| Student | Repo | | Ajay Anand | sryballin/GeneralAssembly-DS | | Zachary Cousens | zfcousens/DAT_SF_12/tree/gh-pages/Project | | Carmen Diaz Echauri | cde/? | | Deepthi Duddempudi | DeepthiGA/Project | | Vijay Duraipalam | coolcalguy/DAT_SF_12/tree/gh-pages/Project | | Cheong-tseng Eng | ctteng/GA-Proj-GPSAnomalyDetection.git | | David Feng | selwyth/neighborhood | | Isabel Friedman | isabitz/whales | | Dave Halvorson | git-halvorson/DAT_SF_12/tree/gh-pages/FinalProject | | Alison Harmon | alharmon13/DAT_SF_12/tree/gh-pages/project | | Markus Huber | mbhuber/USconsumers | | Ryan Hughes | cryhughes/AVS-Kaggle | | Tania Ibanez | positiveepsilon/GA_Project | | Roxana Ordonez | rockyroxana/bike-share-forecast.git | | Justin Peterson | justinrpeterson/? | | April Song | khsong92/ga_ds | | India Swearingen | iswearingen/DAT_SF_12/blob/gh-pages/Homework/Project-IS-load-data.ipynb | | Bing Wang | bingbingboo/DAT_SF_12/blob/gh-pages/Homework/2014flightdatalab.ipynb | | Jaime Williams | jawilliams3000/OaklandCrime | | David Yerrington | dyerrington/Rapstats | | Matt Jones | jonesmatt415/NCAA-Prediction-Project- |