This content is from the fall 2016 version of this course. Please go here for the most recent version.

cm003 - October 3, 2016

Overview

Identify computer programming as a form of problem solving
Practice decomposing an analytical goal into a set of discrete, computational tasks
Identify the verbs for a language of data manipulation
Clarify confusing aspects of data transformation from R for Data Science
Define exploratory data analysis (EDA) and types of pattern exploration
Demonstrate types of graphs useful for EDA and precautions when interpreting them
Practice transforming and exploring data using Department of Education College Scorecard data

Register your GitHub account for the class - all remaining homework assignments will be in private repositories. Private repos can only be seen and edited by members of our course organization. Once you register your GitHub account, I will invite you to join the course organization. If you don’t register your account, you won’t have access to any of the homework assignments.
Submit homework 1
Chapters 9-13 from R for Data Science
Lohr. 2014. For Big-Data Scientists, “Janitor Work” Is Key Hurdle to Insights. New York Times.
Start thinking about your topic for your final project. If you want to do a group project, start identifying project partners or post on the discussion board to find classmates with similar interests.