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Dec 21, 2024
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CS 3300 - Data Science3 lecture hours 2 lab hours 4 credits
Course Description
This course provides an introduction to applied data science including data preparation, exploratory data analysis, data visualization, statistical testing, and predictive modeling. Emphasis will be placed on extracting information from data sets that can be turned into actionable insights or interventions. Problems and data sets are selected from a broad range of disciplines of interest to students, faculty, and industry partners. (prereq: CS 3400 and MA 262 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Understand the basic process of data science and exploratory data analysis including modes of inquiry (hypothesis driven, data driven, and methods driven)
- Identify, access, load, and prepare (clean) a data set for a given problem
- Select, apply, and interpret appropriate visual and statistical methods to analyze distributions of individual variables and relationships between pairs of variables
- Communicate findings through generated data visualizations and reports
- Apply and interpret unsupervised learning models for exploratory data analysis
- Generate appropriate supervised learning problem descriptions
- Determine and apply appropriate experimental setup, evaluation metrics, and models for supervised learning problems
- Engineer features for machine learning tasks
- Perform and interpret feature selection to identify relationships between features and predicted variables
- Apply methods to real-world data sets
Prerequisites by Topic
- Proficiency in at least one programming language
- Familiarity with common data structures (e.g., lists, maps, and sets)
- Familiarity with Python, Pandas, and visualization libraries
- Basic probability and statistics, including statistical testing
- Basic linear algebra
Coordinator
Dr. Ronald J. Nowling
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