Jun 07, 2020
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 111 min 2.0 or MATH& 141 min 2.0 or MATH& 146 min 2.0
Program Admission Required Yes Admitted Program BAS - CIS
Designed to Serve For students admitted to the BAS program in CyberSecurity and Forensics.
Active Date 2015-05-27
Grading System Decimal Grade
Class Limit 24
Contact Hours: Lecture 44 Lab 22 Worksite 0 Clinical 0 Other 0
Total Contact Hours 66
I. The Big Data landscape and Data Mining in the Business Community
II. How to analyze and explore data in preparation for data mining
a. Introduction to R and XLMiner
b. Summary Statistics and interpretation
c. Correlation, T-Test, and Significance
d. Transform of data; log trans; missing data; and outliers
e. Variable Selection and Data Visualization
f. Telling a Story with data
III. Building predictive model building, evaluation and strategy
A. linear regression
B. Logistic Regression
C. Neural Network
D. Cluster Analysis
E. Decision Tree
IV. Modeling Rare events Date
V. Case study in Data mining for Cybersecurity
Student Learning Outcomes
Describe and define the Big Data Landscape and its attributes
Given a set of dataset, construct a customer and /or objects signature by applying data exploration and writing a comprehensive analysis of that data.
Describe and explain current issues in big data analytics
Analyze and explore data in preparation for data mining
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)
Communicate to organizational stakeholders with professionalism, accuracy and transparency using interactive and dynamic visualization tools to translate statistical findings.
Demonstrate the ability to use Predictive model building, evaluation and strategy
Demonstrate the ability to use technical skills in predicative modeling to support business decision-making.
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