vijaynitrr / dat_sf_12

DAT SF 12

Instructor:	Alessandro Gagliardi
EiRs:	Ramesh Sampath
	Otto Stegmaier
	Alex Chao
Classes:	6:30pm-9:30pm, Tuesday and Thursdays
	January 15 – March 31
Office Hours:	Alex Chao, 5:30 - 6:30 before class at GA
	Otto Stegmaier, 9:30 - 10:00 after class at GA
	Ramesh Sampath, 4:00 - 6:00 Saturdays remote
	Can also set by appointment

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.

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Tentative Course Outline

1. Intro to Data Science, Relational Databases & SQL
   • Awesome Public Datasets
2. Getting started with IPython & Git
   • How to Use Git and GitHub
   • SourceTree
   • What’s new in IPython
3. APIs and semi-structured data
   • Mining Twitter
4. IPython.parallel & StarCluster
   • Parallel Computing with IPython
   • AWS Provisining
5. Hadoop Distributed File System and Spark
   • AMP Camp 5 Exercises
   • Announcing Spark 1.3
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)
   • Hierarchical Clustering in Action
   • 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)
   • What’s a good value for R-squared?
   • 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)
   • Multiple (Linear) Regression (Examples in R)
10. Logistic Regression and Generlized Linear Models
    • Logistic Regression in Python
    • Explaining Odds Ratios
    • GLM for Poisson Data
    • Logistic Regression (scikit-learn documentation)
    • sklearn logistic regression with unbalanced classes
11. Project Elevator Pitches
    • See Student Project Repos below
12. Naïve Bayes, Cross Validation, ROC, AUC & Midterm Review - Part I
    • Bayes' Theorem with Lego
    • Probabilistic Programming and Bayesian Methods for Hackers
    • Doing Naive Bayes Classification
    • 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
    • Principal Component Analysis Explained Visually
    • Should you apply PCA to your data?
15. Decision Trees and Forests
    • Decision Tree Learning (Wikipedia article)
    • Decision Trees
    • How to construct a tree
    • Information gain
16. Support Vector Machines
    • Support Vector Machines (scikit-learn documentation)
    • A User's Guide to Support Vector Machines
17. Scaling Out
    • numpy.memmap
    • Feature Hashing
    • HashingVectorizer
    • Vectorizing a large text corpus with the hashing trick
    • Pyrallel - Parallel Data Analytics in Python
    • Online Passive-Aggressive Algorithms
18. Recommendation Systems
    • Machine Learning & Recommender Systems @ Netflix Scale
19. Visualization
    • Chart Suggestions—A Thought-Starter
    • Seaborn: statistical data visualization
    • Welcome to Bokeh
    • ggplot for python
    • plotly
20. Final Project Presentations (12 min. each)
21. Final Project Presentations (12 min. each)
22. Future Directions

Project Schedule

Date	Due	Returned
1/22	Preliminary Project Proposals Due (3-4 sentences)
1/27	Homework 1
1/29		EiR Feedback on Project Proposals
2/3		EiR Feedback on Homework 1
2/5	Formal Proposals (including data and methods chosen)
2/10	Homework 2 Assigned
2/12		EiR Feedback on Formal Proposals
2/17	Homework 2 Due
2/19	Project Elevator Pitch in class (4 minutes each)	Project Live on Github
2/24		Homework 3 Assigned
2/26	Peer Feedback of Projects	Peer Feedback on Project
3/3		Midterm Assessment Posted
3/12	Midterm Assessment Due
3/17	At least one working model
3/24-26	Final Presentations (12 minutes each)	Midterm Graded

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- |