CIS 389 - Big Data Analytics

5 Credits

This course focuses on developing competency in big data analysis techniques and the application of data mining to solve complex business problems. A useful takeaway from the course will be the ability to construct predictive models and perform powerful data analysis. This is a hands-on class in which students will develop data mining models and present big data strategies for implementing them.

Pre-requisite(s) MATH& 141 OR MATH& 146 min 2.0
Program Admission Required Yes Admitted Program BAS - CIS
FeesAcademic Technology Fee

Quarters Typically Offered
Fall Evening

Designed to Serve For students admitted to the BAS program in Cybersecurity and Digital Forensics.
Active Date 20260408T14:58:26

Grading Basis Decimal Grade
Class Limit 24
Contact Hours: Lecture 44 Lab 22
Total Contact Hours 66
Degree Distributions:
ProfTech Course Yes
Restricted Elective Yes
Course Outline

The big data landscape and data mining in the business community
How to analyze and explore data in preparation for data mining
- Introduction to tools
- Summary statistics and interpretation
- Correlation, tests, and significance
- Transform of data, log trans, missing data, and outliers
- Variable selection and data visualization
- Telling a story with data
Building predictive model building, evaluation, and strategy
- Linear regression
- Logistic regression
- Neural network
- Cluster analysis
- Decision tree
Modeling rare events
Case study in data mining for Cybersecurity

Student Learning Outcomes
Analyze the modern Data Science landscape, including distributed systems, cloud technologies, and emerging technologies.

Apply data preprocessing techniques to prepare data for analysis and modeling using relevant tools and technologies.

Demonstrate statistical concepts core to Data Science, including probability distributions, hypothesis testing, and statistical inference.

Use machine learning models and time series analysis for applications like prediction and anomaly detection.

Professionally and accurately communicate findings using visualization tools.

Apply data analysis and modeling techniques to identify anomalies in data sets.