DATA SCIENCE (2 SCU)

Learning Outcomes:

After successful completion of this course, students will be able to use Python and other tools to scrape, clean, and process data, use data management techniques to store data locally and in cloud infrastructures, use statistical methods and visualization to quickly explore data, apply statistics and a computational analysis to make predictions based on data, apply basic computer science concepts such as modularity, abstraction, and encapsulation to data analysis problems, and effectively communicate the outcome of a data analysis using descriptive statistics and visualizations.

Topics:

Through real-world examples of wide interest, students are introduced to methods regarding the five key facets of an investigation which are: data munging/scraping/sampling/cleaning in order to get an informative, manageable data set, data storage, and management in order to be able to access data – especially big data quickly and reliably during a subsequent analysis; an exploratory data analysis to generate hypotheses and intuition about the data; predictions based on statistical tools such as regression, classification, and clustering; as well as communication of the results through visualization, stories, and interpretable summaries.

Prerequisite(s): Introduction to Programming