Introduction to Data Science 

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This introductory course develops computational thinking and tools necessary to answer questions that arise from large-scale datasets. This course emphasizes an end-to-end approach to data science, introducing programming techniques in Python that cover data processing, modeling and analysis. 

First, how can data be extracted that describes real-world phenomenon? This part of the course includes data collection, processing, cleaning (“munging”) and dealing with formatted and semi-formatted data (e.g., json). Second, how can data be modeled and used to make predictions? This includes methods in regression and classification, and experimental design. And third, how can the results of this analysis be understood and reasoned? This includes topics  in visualization and methods for hypothesis testing and validation. The course involves hands-on analysis of a variety of real-world datasets, including economic data, document collections, geographical data and social networks. The class includes working on a real-world case study utilizing data and is based on student interests.