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M.S. in Data Analytics: Curriculum

Curriculum Details

30 Total Credits Required

The master’s in data analytics online requires 30 credit hours of coursework. You’ll study business technology strategy, ethics in IT, IT research and development, IT project management, data science and data analytics, business statistics, and programming for analysts. You’ll also gain skills vital to business intelligence, decision support systems, and big data analytics.

CSP’s online data analytics master’s can be completed in as little as five semesters, although your transfer credits and general education coursework will vary the time it takes you to finish.

Required Courses

Explore real-world information science dilemmas and frameworks to identify ethical problems and reach ethical decisions within the context of analyzing data. This course focuses on the ethical use of data for the purpose of utilizing it to fulfill organizational strategies while at the same time meeting legal, moral and ethical standards.

Learn the overall methodology for information systems development and understand the tools used for requirements determination, use case analysis, process modeling and data modeling. This course explores the method for general technology design, user interface design as well as program design. It includes examining how data analytics is used in the preceding tools and processes as both a tool and an intended outcome. This will be accomplished by looking through the lens of operating in a DevOps organization using agile delivery methods.

Explore the five domains of digital transformation: Customers, Competition, Data, Innovation and Value. This course will identify how to harness customer networks and build platforms. An identification of how to turn data into assets and the exploration of innovation by rapid experimentation will be pursued utilizing data analytics as the prime driver. Understanding how to adapt a Value Proposition while learning how to master disruptive business models will be discovered.

Learn the overall process of designing a research study from inception to completion and develop an academic literature review associated with a potential topic of interest for the capstone project. Understand hypothesis testing, how to use the appropriate instruments to collect data, and why reliability and validity are so important to the integrity of a research project.

This course in programming provides for a broad range of students who need to work with data. Students will learn basic skills in programs like Python and/or the open-source R statistical package. It introduces the programming of statistical graphics simulation methods, numerical optimization, and computational linear algebra.

Learn how to prepare data and design meaningful visualizations for effective communication and decision support. Analytical tools such as Tableau, R, and Excel, will be utilized to develop tables, charts, graphs, maps and dashboards for effective data analysis and storytelling.

This course provides an introduction to decision support systems (DSS) forbusiness intelligence (BI). It looks at decision-making, data components, model components and the use of user interfaces. It explores designing a DSS using object-oriented technologies and implementing it with a recognition of how to evaluate a deployed system. Executive information and dashboards coupled with group decision support systems will be identified.

This course looks at a managerial approach to understanding business intelligence (BI) systems. Its objective is to help future managers use and understand analytics by providing a solid foundation of BI that is reinforced with hands-on practice. This includes an introduction of business intelligence, data analytics and data science. It explores descriptive, predictive and prescriptive analytics. It identifies big data concepts and tools. It also describes future trends, Analytics and Artificial Intelligence

This class will explore various aspects of big data analytics. Discover tools, technology, applications, use cases and research directions in industry. Initially it will explore challenges in big data and big data analytics. The Big Data Reference Model will be examined. A look at big data analytic tools such as Hadoop, Spark and Splunk will be completed. Looking at predictive models used in analytics and a framework for minimizing data leakage will be explored. Storing big data will be examined plus a study of big data cluster analysis will be done. Finally, non-linear extraction of big data analytics will be described along with data mining and large-scale data clustering.

Demonstrate an understanding of data analytics through skills developed in this program. This course will afford students the opportunity to showcase a capstone data analytics project of their choice. Students will identify an issue to be resolved, or an opportunity to be exploited through their analysis. Elements from previous courses will be incorporated for research of a chosen topic and suggest potential solutions or future research to be done. Data will be analyzed and visualizations developed through this process. A faculty panel will judge the final capstone project.

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