509 7th Street NW, 3rd Floor
Washington D.C. 20004
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This workshop will introduce students to data exploration and machine learning techniques. Students will learn about the data science workflow and will practice exploring and visualizing data using R and built in libraries. Students will also explore the differences between supervised and unsupervised learning techniques and practice creating predictive regression models.
A background in computer science, programming, and/or statistics is preferred for this workshop. It is not required but you are expected to be somewhat familiar with the command line tools and how to write simple programs.
PART I: Data Exploration
-Understand course contents and structure -Describe the data mining workflow and the key traits of a successful data scientist. -Extract, format, and preprocess data using UNIX command-line tools. -Explore and visualize data using R and ggplot2.
PART II: Intro to Machine Learning
-Explain the concepts and applications of supervised & unsupervised learning techniques. -Describe categorical and continuous feature spaces, including examples and techniques for each. -Discuss the purpose of machine learning and the interpretation of predictive modeling results.
See pre-work document: https://www.dropbox.com/s/2t2gex4e0wxsft3/DAT%20PreWork.pdf
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