Data Analytics (DATA)

DATA 130, ETHICAL AND LEGAL FRAMEWORK OF BIG DATA 3 (3)

This course will cover the ethical and legal issues in society with regard to collecting, processing and utilization of personal data, particularly in large interconnected data sets. An exploration of both the positive benefits and negative consequences will be part of the course.

General Education: IN1, IN3, IN4

Course Entry Requirement(s): Concurrent: ENGL 161

Typically Offered: Fall Semester

DATA 200, DATA MANAGEMENT IN BIG DATA 3 (4)

This course is an introduction into what is meant by Big Data and its uses. An overview of the analytical platforms and tools to work with Big Data are discussed in the context of running better businesses and providing better services to customers. Students will learn about the issues of data variability, velocity, volume and data governance. Students will become familiar with the application of the tools Hadoop, HBase, Hive, MapReduce, Yarn and SQL.

General Education: IN1, IN2, IN4

Course Entry Requirement(s): Prerequisite: MTHM 168 with a "C" or higher and CISS 143

Typically Offered: Fall Semester

DATA 210, BIG DATA MANAGEMENT TECHNOLOGIES 3 (4)

This course will develop the skills with using analysis tools to create information from Big Data sets. Students will discover the power of visualization and graphical tools as an analysis technique using industry standard applications such as R, Spark 1 and 2, Apache SparkR, Spark Graphx. Students will learn to apply machine learning (Spark MLLIB) rules to enhance the integrity and consistency of large data sets.

General Education: IN1, IN2

Course Entry Requirement(s): Prerequisite: MTHM 168 with a "C" or higher and CISS 143

Typically Offered: Spring Semester

DATA 220, LINUX ADMINISTRATION FOR BIG DATA 3 (3)

This course provides Linux administration and user skills. Students will utilize Linux to develop an understanding of how to install, configure, secure and operate and Linux server platform. The focus of Linux administration and operation will be towards processing data and understanding basic Linux administration tasks and the command logic of the operating environment.

General Education: IN1, IN2, IN4

Course Entry Requirement(s): Prerequisite: CISS 121

Typically Offered: Spring Semester

DATA 221, MODELING & ANALYSIS WITH R & PYTHON FOR DATA PROFESSIONALS 3 (4)

This course provides a foundation in the R and Python programming languages as applied to data analytic applications. Students will learns the basics of the Python and R languages for the purposes of data set preparation, data modeling, linear regression, and other statistical calculations.

General Education: IN1, IN2, IN4

Course Entry Requirement(s): Prerequisite: MTHM 168 with a "C" or higher and DATA 220

Typically Offered: Fall Semester

DATA 222, BUILDING ANALYTICAL MODELS AND MACHINE LEARNING ALGORITHMS 3 (4)

This course provides a foundation in the R and Python programming languages as applied to data analytic applications. Students will learns the basics of the Python and R languages for the purposes of data set preparation, data modeling, linear regression, and other statistical calculations. Assignments, lab activities and examples will feature implementations of common data analytics statistical calculations, data management scenarios, and linear regression modeling.

General Education: IN1, IN2, IN4

Course Entry Requirement(s): Prerequisite: DATA 221

Typically Offered: Spring Semester

DATA 230, PREDICTIVE AND VISUAL ANALYTICS 3 (4)

This course will teach the student how to use the software tool, SPSS, for in depth predictive statistical analysis, reporting and modeling of Big Data sets. Students will learn optimization techniques and how to access data sets from Hadoop data stores. In addition, students will use the I2C visualization tool to discover relationships within large data sets.

General Education: IN1, IN2, IN4

Course Entry Requirement(s): Prerequisite: MTHM 168 with a "C" or higher and CISS 143

Typically Offered: Spring Semester

DATA 248, DATA ANALYTICS CAPSTONE PROJECT 4 (4)

This course concentrates on machine learning and the development of a capstone project. Students will explore the application of tools to prepare datasets, analyze data, and machine learning modeling to predict and validate an hypothesis. Capstone projects include initial feasibility study, data analysis, data design, presentation of results emphasizing data visualizations, and conclusions as outcomes of the analysis. Students completing the Data Analytics curriculum will utilize the tools and techniques to analyze datasets from various sources and industries. Students can select a project from their area of interest. Project approval is accomplished through a feasibility study. Using Big Data tools and Cloud Computing , each student completes their analysis and prepares a presentation of the results and how the analysis was accomplished.

General Education: IN1, IN2, IN4

Course Entry Requirement(s): Prerequisite: DATA 200, DATA 220, DATA 221; Concurrent: DATA 210, DATA 222, DATA 230

Typically Offered: Spring Semester