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General Assembly Data Science Course 14 - Fall 2014

This repo includes materials for the General Assembly Data Science course in NYC. Navigate the directory structure to find what you're looking for. The README.md files are often the most central in a directory, and will display by default when you navigate on github.

Upcoming Activity:

  • 12/8/2014 - Review
  • 12/10/2014 - Final Presenations - Dry Run
  • 12/15/2014 - Final Final Presenations

Office Hours

Rob will be available by appointment, [email protected]

Jarret and David will hold regular office hours

Jarret: Sun - 4:00-6:00 PM [email protected]

David: M,W - 5:30-6:30 [email protected]

Syllabus

| Date | Location | Topic | Assignment Due |:----------|:--------|:------|:------|:------ | 9/24/2014 | GA East (902 Broadway, 4th Floor) |Intro to Intro to Data Science | Submit first pull request (lab01) | 9/29/2014 || Intro to Machine Learning | Chicago housing price predictor (dataexplor01) | 10/1/2014 | | Linear Regression | lab02 iPython notebooks | 10/6/2014 | GA West (10 E. 21st St, 4th Floor) | Data Visualization and EDA | | 10/8/2014 | | Model Selection | dataexplor02 | 10/13/2014| | Columbus Day (Holiday/ No class) | | | 10/15/2014 | | Regularization, Dimensionality Reduction | dataexplor03 | 10/20/2014 | | Classification, Logistic Regression | dataexplor04 | 10/22/2014 | | Probability and Bayes Theorem, Naive Bayes Classifiers | | 10/27/2014 | | Evaluating Classifier Models | dataexplor05 | 10/29/2014 | | Project Lightning Talks | Project Update | | 11/3/2014 | | Time Series Analysis | dataexplor06 | 11/5/2014 | | Guest Speaker, Time Series Workshop | | 11/10/2014 | | Clustering, k-means | dataexplor07 | 11/12/2014 | | Recommendation Systems, Collaborative Filtering | | 11/17/2014 | | Natural Language Processing | dataexplor08 | 11/19/2014 | GA East (902 Broadway, 4th Floor) | Bayesian A/B Testing | | 11/24/2014 | | Decision Trees and Ensemble Methods | | 11/26/2014 | | (Holiday/ No class) | | 12/1/2014 | | Guest Speaker, Geospatial Problems | | 12/3/2014 | | Data Engineering: Distributed Computing, Hadoop + Guest Speaker | | 12/8/2014 | | Review | | 12/10/2014 | | Presentation Workshop | Presentation Slides/Outline | 12/15/2014 | |Final Presentations | Final Project Due

Getting Help

Github Issues

For general or specific course help, students can get the fastest response by posting an issue, to the issues page for this repository

  • Jarret or David will review each issue
  • Students and other instructors following the repository will also be able to address the issue, improving response time.

Submitting Assignments

We will be using the Fork and Pull git model for submitting labs and some assignments.

For the first assignment:

  1. Fork the assignments repo for the class:

  2. clone the repo to your newly created Data Science Toolbox

vagrant@data-science-toolbox:~$git clone [email protected]:<yourgithubusername>/fall_2014_assignments.git

in <yourgithubusername>/fall_2014_assignments/lab01, make a directory with your first initial/full last name.

vagrant@data-science-toolbox:~$export DIR=<"flastname">
vagrant@data-science-toolbox:~$mkdir $DIR; cd <yourgithubusername>/fall_2014_assignments/lab01/$DIR;

With a text editor, create and save a markdown file with the following content:

  • Your name and what you do
  • One liner about your coding and math background
  • Any social web you use and don't mind sharing (e.g. linkedin, twitter)
  • A data blog post you read recently for sharing with the class create a branch of the repository with a unique name, and then commit to that repo
vagrant@data-science-toolbox:~$git checkout -b my_name_class_1
vagrant@data-science-toolbox:~$git add .
vagrant@data-science-toolbox:~$git commit -m 'my first git commit!'
vagrant@data-science-toolbox:~$git push origin my_name_class_1

Add a pull request. This is the actual submission of your work. You can do this on github by finding your branch and clicking "Create pull request." Developers, feel free to use some command line tool for this if you prefer it.

Again, a link to github documentation on the Fork and Pull git model.