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Certificate in Data Analytics: Curriculum

Curriculum Details

12 Total Credits Required

Data analytics courses cover the fundamentals of data science and data analytics while exploring programming for analysts, big data analytics, and decision support systems for business intelligence.

The data analytics certificate can be completed in as few as seven months.

Required Courses

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, privacy and managerial considerations in Analytics.

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.

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 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.

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