Course Logistics

Learning Objectives

At the conclusion of this course, students will be able to:

understand and articulate key concepts and methods in applied data science; acquire, manipulate, and manage data from varied sources; conduct exploratory data analyses; test statistical hypotheses; build models to classify and make predictions about data; and evaluate model performance;
use modern tools for data analysis (e.g., the Unix command line, version control systems, the R programming environment, web APIs) and apply “best practices” in data science and data management;
interact with both local and remote data sources to store, query, process, and analyze data presented in a variety of common formats (e.g., delimited text files, structured text files, various database systems);
comfortably write their own simple computer programs/scripts for data management, statistical analysis, visualization, and more specialized applications;
design and implement reproducible data science workflows that take a project from data acquisition to analysis to presentation and organize their work using a version control system;
and apply all of these tools to questions of interest in the natural and social sciences.

At least one semester of introductory statistics is recommended. Prior programming experience is not expected, but would be helpful!