jfulghum/dat5

General Assembly's Data Science course in Washington, DC

DAT5 Course Repository

Course materials for General Assembly's Data Science course in Washington, DC (3/18/15 - 6/3/15).

Instructors: Kevin Markham and Brandon Burroughs

Monday	Wednesday
3/18: Introduction and Python
3/23: Git and Command Line	3/25: Exploratory Data Analysis
3/30: Visualization and APIs	4/1: Machine Learning and KNN
4/6: Bias-Variance and Train/Test Split	4/8: Kaggle Titanic (Part 1)
4/13: Web Scraping, Tidy Data, Reproducibility	4/15: Linear Regression
4/20: Logistic Regression and Confusion Matrix	4/22: ROC and Cross-Validation
4/27: Project Presentation #1	4/29: Kaggle Titanic (Part 2)
5/4: Naive Bayes	5/6: Natural Language Processing
5/11: Decision Trees	5/13: Ensembles
5/18: Clustering and Regularization	5/20: Advanced scikit-learn
5/25: No Class	5/27: Databases and SQL
6/1: Course Review	6/3: Project Presentation #2

Key Project Dates

• 3/30: Deadline for discussing your project idea(s) with an instructor
• 4/6: Project question and dataset (write-up)
• 4/27: Project presentation #1 (slides, code, visualizations)
• 5/18: First draft due (draft of project paper, code, visualizations)
• 5/25: Peer review due
• 6/3: Project presentation #2 (project paper, slides, code, visualizations, data, data dictionary)

Key Project Links

• Course project requirements
• Public data sources
• Kaggle competitions
• Examples of student projects
• Peer review guidelines

Logistics

• Office hours will take place every Saturday and Sunday.
• Homework will be assigned every Wednesday and due on Monday, and you'll receive feedback by Wednesday.
• Our primary tool for out-of-class communication will be a private chat room through Slack.

Submission Forms

• Homework submission form (also for project submissions)
  • Gist is an easy way to put your homework online
• Feedback submission form (at the end of every class)

Before the Course Begins

• Install the Anaconda distribution of Python 2.7x.
• Install Git and create a GitHub account.
• Once you receive an email invitation from Slack, join our "DAT5 team" and add your photo.
• Choose a Python workshop to attend, depending upon your current skill level:
  • Beginner: Saturday 3/7 10am-2pm or Thursday 3/12 6:30pm-9pm
  • Intermediate: Saturday 3/14 10am-2pm
• Practice your Python using the resources below.

Python Resources

• Codecademy's Python course: Good beginner material, including tons of in-browser exercises.
• DataQuest: Similar interface to Codecademy, but focused on teaching Python in the context of data science.
• Google's Python Class: Slightly more advanced, including hours of useful lecture videos and downloadable exercises (with solutions).
• A Crash Course in Python for Scientists: Read through the Overview section for a quick introduction to Python.
• Python for Informatics: A very beginner-oriented book, with associated slides and videos.
• Code from our beginner and intermediate workshops: Useful for review and reference.

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Class 1: Introduction and Python

• Introduction to General Assembly
• Course overview (slides)
• Brief tour of Slack
• Checking the setup of your laptop
• Python lesson with airline safety data (code)

Homework:

• Python exercises with Chipotle order data (listed at bottom of code file)
• Work through GA's excellent introductory command line tutorial and then take this brief quiz.
• Read through the course project requirements and start thinking about your own project!

Optional:

• If we discovered any setup issues with your laptop, please resolve them before Monday.
• If you're not feeling comfortable in Python, keep practicing using the resources above!

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Class 2: Git and Command Line

• Any questions about the course project?
• Command line (slides)
• Git and GitHub (slides)

Homework:

• Command line exercises with SMS Spam Data (listed at the bottom of Introduction to the Command Line)
• Note: This homework is not due until Monday. You might want to create a GitHub repo for your homework instead of using Gist!

Optional:

• Browse through some example student projects to stimulate your thinking and give you a sense of project scope.

Resources:

• This Command Line Primer goes a bit more into command line scripting.
• Read the first two chapters of Pro Git to gain a much deeper understanding of version control and basic Git commands.
• Watch Introduction to Git and GitHub (36 minutes) for a quick review of a lot of today's material.
• GitRef is an excellent reference guide for Git commands, and Git quick reference for beginners is a shorter guide with commands grouped by workflow.
• The Markdown Cheatsheet covers standard Markdown and a bit of "GitHub Flavored Markdown."