CIS 389 - Big Data Analytics

5 Credits

This course focuses on developing a competency in Big Data Analysis techniques and to be able to apply 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
FeesCF

Quarters Typically Offered

Winter Evening
Spring Evening

Designed to Serve For students admitted to the BAS program in CyberSecurity and Forensics.
Active Date 20190723T08:34:58

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
I. The Big Data landscape and Data Mining in the Business Community

II. How to analyze and explore data in preparation for data mining

Introduction to R and XLMiner
Summary Statistics and interpretation
Correlation, T-Test, and Significance
Transform of data; log trans; missing data; and outliers
Variable Selection and Data Visualization
Telling a Story with data

III. Building predictive model building, evaluation and strategy

Linear regression
Logistic Regression
Neural Network
Cluster Analysis
Decision Tree

IV. Modeling Rare events Date

V. Case study in Data mining for Cybersecurity

Student Learning Outcomes
Describe current issues in big data analytics, incorporating the big data landscape and its attributes.

Write a comprehensive analysis of a data set set based on the data exploration.
.

Prepare data for data mining in a manner consistent with industry standards.

Establish a foundation in the statistical pre-requests for data mining.

Construct a target's signature with data visualization.

Demonstrate competency in the three major types of data mining models - (Target, non-target, and machine learning models).

Professionally and accurately communicate statistical findings to organizational stakeholders using interactive and dynamic visualization tools.

Support business decision making through predictive model building, evaluation and strategy.