CCOG for CIS 277S archive revision 202401
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- Effective Term:
- Winter 2024 through Winter 2025
- Course Number:
- CIS 277S
- Course Title:
- Introduction to Data Science
- Credit Hours:
- 4
- Lecture Hours:
- 30
- Lecture/Lab Hours:
- 0
- Lab Hours:
- 30
Course Description
Introduces algorithms and techniques used in the organization and analysis of large amounts of data. Covers the extraction, cleansing, and preparation of large data sets, and their analysis using basic statistical principles of supervised and unsupervised machine learning, including clustering, feed-forward and recurrent neural networks, and top-down induction of decision trees. Covers techniques for testing and validating the accuracy of machine learning models. Discusses the use of machine learning models in classification, data mining and exploration, summarization, and prediction. Audit available.
Intended Outcomes for the course
Upon completion of the course students should be able to:
- Create training sets and test sets from data contained in a variety of sources.
- Analyze data sets using various machine learning and statistical techniques.
- Validate the accuracy of machine learning models.
- Improve data representation to increase the accuracy of machine learning models.
- Discuss the application of machine learning models to classification, prediction, data exploration, and summarization problems.