Intro to Data Science

What is Data Science?

• Relatively new field intersecting CS, Statistics and Business
• Determining consumer trends, business opportunities, and user behavior
• Drawing valuable insight from data - data is "unbiased"
• Some areas that overlap:
  • Computational Statistics
  • Big Data and Data Mining
  • Machine Learning

How is Data Science used?

A/B testing - web applications with different features

Consumer recommendations - Amazon, Netflix, etc.

Consumer trends - How people use certain products

Revenue opportunities - ad targeting, investor/stock forecasting

Accessibility

Technologies to "do" Data Science

• Programming Languages - Python, R (statistical computing)
• Big Data Frameworks - Hadoop, Spark, Pig
• Concepts - Map-Reduce, Aggregation, Regression, Distributions
• Visualization Frameworks - Matplotlib, D3, Processing

Map/filter/reduce in a tweet:

map([🌽, 🐮, 🐔], cook)
=> [🍿, 🍔, 🍳]

filter([🍿, 🍔, 🍳], isVegetarian)
=> [🍿, 🍳]

reduce([🍿, 🍳], eat)
=> 💩

— Steven Luscher (@steveluscher) June 10, 2016

Tutorial

• Skeleton: python-start
• Complete: coming soon
• illinoiswcs.org/techteam

Brainstorm

• What would you like to build?
• What features? Sketches.
• What technologies do you think you'll need?
• What is your plan for developing?

Potential Projects

• Your Own Personal Website
• Data Analysis on a Dataset
• Web or Mobile App
• WCS Display for our TV
• Chrome Extension
• Slackbot

Submit your Projects!