Data Analytics Learning Outcomes

Upon completion of the the Master of Science in Data Analytics, students should be able to:

Successfully compete for employment in the public and private sector in the field of data analytics.
Develop methods to obtain and structure large volume sets and multiple sources.
Apply statistical and predictive modeling to big data for decision making.
Demonstrate the ability to use Big Data tools.
Demonstrate knowledge of high ethical standards and the application of those standards in real life criminal justice situations consistent with the Christian humanistic philosophy of St. Francis de Sales.

Program Learning Outcomes are mapped in the following way: (F = Focus, E = Extensive Focus)

	A	B	C	D	E
IT 511	F	F			F
IT 513	F	E	E		E
IT 541	F	F			F
IT 553	E		E	E	F
IT 562	E		E	E	F

Specific course outcomes are as follows:

IT 511 Database Management	IT 513 Data Mining and Visualization	IT 541 Decision Support Systems	IT 553 Statistical Analysis and Predictive Modeling	IT 562 Data Analytics
Describe advanced database design techniques	Be able to use the RStudio IDE to create the R’s data structures necessary to read, manipulate, and graph data files with basic separators (blanks, commas, tabs, soft and hard returns), Excel files, and SAS, and SPPS files	Define and describe a decision support system	Understand statistical concepts and apply to projects for decision making	Understand statistical concepts and apply them to predictive analytics models
Implement advanced database management skills	Learn critical programming skills with R Programming and its interaction with some other analytical tools	Analyze, model and design a decision support system	Demonstrate the ability to use and interpret statistical software effectively	Demonstrate the ability to use and understand statistical software effectively
Develop the physical and logical models as related to database planning	Improve and clean data quality for reporting and analytics for decision makers	Define knowledge management: acquisition and validation	Develop methods and form conclusions to solve a real world problem	Develop predictive models couple with AI.
Apply advanced SQL programming techniques	Learn the fundamentals of statistics and analytics.	Unitize artificial intelligence and neural network software	Apply predictive modeling techniques to large data sets	Demonstrate a graduate-level competency in the use of analytical tools and techniques to do predictive analytics
	Exploring and visualizing data	Conceptualize a web-based decision support system
	Explore career opportunities in Data Science, Mining, and Warehousing