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Aug 22, 2025
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CIS 277 Data Analytics II 5
Prerequisite CIS 276 with a 2.0 or higher, or instructor permission.
Course Description
The second of two courses on data analytics programming. This class will combine and integrate the skills learned in previous courses to learn predictive data analytics, using regression models. Use case studies to apply the data analytics phases.
Course Content
A. Data visualization
B. Data preparation
C. Regression models
D. Charts
E. Repository management
Student Outcomes
- Apply data collection, preparation, cleansing and analysis methods to demonstrate data visualization
- Apply data analytics principles to case studies
- Create, use, and validate linear and multiple regression models for predictive data analysis
- Interpret charts and models to make decisions
- Use source control and repository management (Git, GitHub)
Degree Outcomes
Core Abilities- Creative and Reflective Thinking: Graduates will evaluate, analyze, synthesize, and generate ideas; construct informed, meaningful, and justifiable conclusions; and process feelings, beliefs, biases, strengths, and weaknesses as they relate to their thinking, decisions, and creations.
- Information Competency: Graduates will be able to seek, find, evaluate and use information, and employ information technology to engage in lifelong learning.
Program Outcomes - Research, analyze and integrate information to learn current technologies
- Create and model end-user data visualizations
- Collect, analyze, integrate and query data from disparate sources
- Develop data analytics applications using descriptive and predictive models
Lecture Contact Hours 50
Lab Contact Hours 0
Clinical Contact Hours 0
Total Contact Hours 50
Potential Methods
A. Demonstration
B. Observation
C. Individual projects
D. Group projects
E. Participation
F. Discussion
G. Quizzes
H. Peer review
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