wittedhaddock / dat5_bos Goto Github PK

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

• 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!

• 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.

slides. Python refresher code. Python code. Pandas code.

• 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.

• slides
• GitHub for Windows
• GitHub for Mac
• Student directory for this class

Homework:

• Check for proper setup of Git by forking the data science project examples and pulling the fork to your local hard drive.
• Download the following for Class 4:
  • SQLite. Please make sure to download the precompiled binaries for your OS, NOT the source code.
  • Sublime Text Editor.
  • DB Visualizer. Please download the free version.
  • Baseball archive for SQLite.

slides

Overview of the baseball archive

• Installation of SQLite, Sublime, DB Visualizer, and our dataset
• The SELECT statement
• The WHERE clause
• ORDER BY
• LEFT JOIN and INNER JOIN
• GROUP BY
• DISTINCT
• CASE statements
• Subqueries and IS NOT NULL
• CREATE TABLE
• Using Pandas and SQL Seamlessly

Homework:

• Complete the in-class excercises, if you haven't already:

  • Find the player with the most at-bats in a single season.
  • Find the name of the the player with the most at-bats in baseball history.
  • Find the average number of at_bats of players in their rookie season.
  • Find the average number of at_bats of players in their final season for all players born after 1980.
  • Find the average number of at_bats of Yankees players who began their second season at or after 1980.
  • Pass the SQL in the previous bullet into a pandas DataFrame and write it back to SQLite.

• Create full, working queries to answer at least four novel questions you have about the dataset using the following concepts:

  • The WHERE clause
  • ORDER BY
  • LEFT JOIN and INNER JOIN
  • GROUP BY
  • SELECT DISTINCT
  • CASE statements
  • Subqueries and IS NOT NULL

• Using Pandas, (1) query the Baseball dataset, (2) transform the data in some way, and (3) write a new table back to the databse.

• Commit and Sync your SQL and Pandas files to your GitHub fork and issue a pull request.

Resources: * SQLite homepage * SQLite Syntax

SQL Tutorials: * Note: These tutorials are for all flavors of SQL, not just SQLite, so some of the functions may behave differently in SQLite. * SQL tutorial * SQLZoo