haruom / dat5_bos

General Assembly's Data Science course in Boston, MA (Winter-Spring 2015)

DAT5 Course Repository

Course materials for General Assembly's Data Science course in Boston, MA (20 January 2015 - 07 April 2015). View student work in the student repository.

Instructor: Bryan Balin. Teaching Assistant: Harish Krishnamurthy.

Office hours: Wednesday 5-6pm; Friday 5-6:30pm, @Boston Public Library; as needed Tuesdays and Thursdas @GA.

Course Project information

Tuesday	Thursday
1/20: Introduction	1/22: Python & Pandas
1/27: Git and GitHub	1/29: Getting Data
2/3: Advanced Pandas Milestone: Question and Data Set	2/5: Numpy, Machine Learning, KNN
2/10: scikit-learn, Model Evaluation Procedures	2/12: Linear Regression
2/17: Logistic Regression, Preview of Other Models	2/19: Model Evaluation Metrics Milestone: Data Exploration and Analysis Plan
2/24: Working a Data Problem	2/26: Clustering and Visualization Milestone: Deadline for Topic Changes
3/3: Naive Bayes	3/5: Natural Language Processing
3/10: Decision Trees and Ensembles Milestone: First Draft	3/12: Advanced scikit-learn
3/17: No Class	3/19: Databases and MapReduce
3/24: Recommenders	3/26: Course Review, Companion Tools Milestone: Second Draft (Optional)
3/31: TBD	4/2: Project Presentations
4/7: Project Presentations

Installation and Setup

• 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 "datbos05 team" and add your photo!

Class 1: Introduction

• Introduction to General Assembly
• Course overview: our philosophy and expectations (slides)
• Data science overview (slides)
• Tools: check for proper setup of Anaconda, overview of Slack

Homework:

• Resolve any installation issues before next class.

Optional:

• Review the code for a recap of some Python basics.
• Read Analyzing the Analyzers for a useful look at the different types of data scientists.
• Check out the PyData Boston Meetup page to become acquainted with the local data community.

Class 2: Python & Pandas

slides

• Brief overview of Python
• Brief overview of Python environments: Python scripting, IPython interpreter, Spyder
• Working with data in Pandas
  • Loading and viewing data
  • Indexing and selecting data
  • Assigning, reassigning, and splitting data
  • Describing and summarizing data
  • Plotting data

Homework:

• Do the class homework by Tuesday.
• Read through the project page in detail.
• Review a few projects from past Data Science courses to get a sense of the variety and scope of student projects.

Optional:

• If you need more practice with Python, review the "Python Overview" section of A Crash Course in Python, work through some of Codecademy's Python course, or work through Google's Python Class and its exercises.
• For more project inspiration, browse the student projects from Andrew Ng's Machine Learning course at Stanford.

Resources:

• Online Python Tutor is useful for visualizing (and debugging) your code.

Class 3: Git and GitHub

• Check for proper setup of Git by running git clone https://github.com/bbalin12/DAT-project-examples.git

Class 4: Getting Data

Class 5: Advanced Pandas

Class 6: Numpy, Machine Learning, KNN

Class 7: scikit-learn, Model Evaluation Procedures

Class 8: Linear Regression

Class 9: Logistic Regression, Preview of Other Models

Class 10: Model Evaluation Metrics

Class 11: Working a Data Problem

Class 12: Clustering and Visualization

Class 13: Naive Bayes

Class 14: Natural Language Processing

Class 15: Decision Trees and Ensembles

Class 16: Advanced scikit-learn

Class 17: Databases and MapReduce

Class 18: Recommenders

Class 19: Course Review, Companion Tools

Class 20: TBD

Class 21: Project Presentations

Class 22: Project Presentations