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Analytics

This undergraduate certificate consists of four courses in data analytics. Students must take Applied Statistics or have equivalent knowledge of statistics before taking these four courses.

Courses in this certificate include:

This course is designed as the entry point to learning SAS programming language and analytics programming concepts to gain business intelligence. It provides the tools necessary to write SAS programs to perform data management, analysis, and reporting including the creation of simple reports and computing basic statistics on data set variables. Hands-on exercises designed to facilitate understanding of business systems and business processes are included. The course also provides the basis for more advanced work in business analytics and advanced programming techniques for data modeling. This course aligns with the SAS Base Programming certification concepts. Prerequisite: MA 215
This course is designed to introduce students to the fundamentals of Structured Query Language (SQL) for the purpose of data management. Students will learn to identify the role and structure of relational databases as they apply to business analytics, apply the SQL in MySQL for data manipulation language (DML), apply the SQL in MySQL for data definition language (DDL), and apply normal forms (1NF, 2NF, & 3NF) for database normalization. Emphasis will be on ensuring data quality as it relates to business intelligence. Prerequisite: MA 215
This course introduces an analytical toolset to address business problems. The course provides an overview of the key concepts, applications, processes and techniques relevant to data modeling for business intelligence. The course makes use of SAS Enterprise Miner to illustrate the use of business analytics methodologies to enhance decision-making. Prerequisite: MA 215
This course provides an in-depth discussion on the data warehousing, data mining, and analytics used for business intelligence. A variety of data analysis tools will be used to discover patterns and relationships in data that may be used to make proactive, knowledge-driven decisions. The course provides an in-depth discussion on various techniques of data mining including data modeling, pattern recognition, predictive analytics, and text mining. Prerequisite: BAN 400 and MA 215

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