Curriculum: Data Analytics Graduate Certificate

Curriculum: Data Analytics Graduate Certificate
11.15.2025
12 Credit Hours
01.12.2026
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curriculum icon Curriculum at a Glance

The  Data Analytics Graduate Certificate at the University of Cincinnati includes four core courses (eight credits) and two elective courses (four credits).

Course Pre-requisites

  • BANA 7011 Intro to Data Analysis & Statistical Computing ( Required for non-STEM majors)
  • BANA 6043 (Statistical Computing) is a pre-requisite to BANA 7038
  • BANA 7046 is a pre-requisite to BANA 7047
  • IS 6030 is a pre-requisite to the following courses
    • IS 7032
    • IS 7036
    • IS 7034

Due to class offerings, individuals interested in this certificate as a standalone program should apply to the spring or fall semesters.

To learn more about our coursework, review a sample.

Please note that IS7030 is exclusively designed for students who wish to pursue a Master’s degree in Information Systems. All other students must enroll in IS6030. It is highly encourage you speak with your Student Success Coordinator to ensure your goals are aligned to your desired outcome in the Data Analytics certificate program.

Core Courses

Students must complete the following 2 core courses (4 credit hours).

Course Title / Description Credit
BANA7038
Data Analysis Methods
Course: BANA7038
Credit: 2
This course covers the fundamental concepts of applied data analysis methods. Various aspects of linear and logistic regression models are introduced, with emphasis on real data applications. Students are required to analyze data using major statistical software packages. BANA 7038 should not be taken for credit by MS-Business Analytics students.
2
IS7034
Data Warehousing and Business Intelligence
Course: IS7034
Credit: 2
This course is designed for the comprehensive learning of data warehousing technology for business intelligence. Data warehouses are used to store (archive) data from operational information systems. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive business environment. This course will introduce students to the design, development and operation of data warehouses. Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.
2
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Students must complete one of the following courses (2 credit hours).

IS7030 is for MSIS students only and all other certificate students will take IS6030.

Course Title / Description Credit
IS6030
Data Management
Course: IS6030
Credit: 2
This course provides an introduction to the use and design of databases to store, manipulate and query data. The course introduces the structured query language (SQL) used to manage data. Students who complete this course should understand how to use SQL for basic data manipulation and queries. This course is intended for users of existing databases to extract needed information and should not be taken by MSIS students or those students who wish to learn detailed database design techniques.
2
IS7030
Data Modeling
Course: IS7030
Credit: 2
This course provides in-depth coverage of the principles of data modeling. Starting at the highest level of abstraction, the data requirements culled out from user requirements specification are rendered as a conceptual data model using Entity-relationship modeling grammar. Students then learn how to map the conceptual model to the logical tier using relational modeling grammar, in preparation for the ultimate database design. Workshop sessions are included to provide students hands-on modeling opportunities. A basic introduction to Structured Query Language (SQL) is also included.
2
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Required Core

Students must complete one of the following two core courses (2 hours)

Course Title / Description Credit
BANA6043
Statistical Computing
Course: BANA6043
Credit: 2
This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.
2
BANA7025
Data Wrangling
Course: BANA7025
Credit: 2
This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.
2
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Electives

Students must complete 4 credit hours from the following courses. Substitutions may be made with prior approval of the academic director of the program.

Course Title / Description Credit
BANA6037
Data Visualization
Course: BANA6037
Credit: 2
This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.
2
BANA6043
Statistical Computing
Course: BANA6043
Credit: 2
This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.
2
BANA7025
Data Wrangling
Course: BANA7025
Credit: 2
This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.
2
BANA7046
Data Mining I
Course: BANA7046
Credit: 2
This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees.
2
BANA7047
Data Mining II
Course: BANA7047
Credit: 2
This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include advanced trees: bagging, random forests, boosting; nonparametric smoothing methods; generalized additive models; data preprocessing/scaling; neural networks; deep learning; cluster analysis; association rules.
2
BANA8090
Special Topics in Business Analytics
Course: BANA8090
Credit: 1-4
This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering.
1-4
IS7032
Database Systems
Course: IS7032
Credit: 2
This course provides a comprehensive introduction to data modeling and database design, covering the full process from conceptual modeling to database implementation. Students will learn to analyze user requirements and develop Entity-Relationship (ER) models, map them to relational models, and apply normalization techniques to refine database structures. The course also covers Structured Query Language (SQL) for data definition (DDL), data manipulation (DML), and data control (DCL). Through hands-on workshops and lab sessions, students will gain practical experience in database normalization, optimization, and query development to design and implement efficient, scalable database solutions for real-world applications
2
IS7036
Data Mining for Business Intelligence
Course: IS7036
Credit: 2
This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages.
2
IS7038
Managing Business Intelligence Projects
Course: IS7038
Credit: 2
This course discusses key concepts in the management of Business Intelligence Projects. Using the Systems Development Life Cycle as an organizing framework, and a case discussion based pedagogy, students are exposed to the major challenges in justifying BI projects, eliciting user requirements, selecting the right tools and technologies, and implementing the final solution.
2
IS8034
Big Data Integration
Course: IS8034
Credit: 2
This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity.
2
IS8070
Special Topics in Information Systems
Course: IS8070
Credit: 1
This course is used to explore topics of current interest in the IS domain, that do not fall within the scope of any of the regularly scheduled courses. By the very nature of the course, specific topics covered will vary with each offering.
1
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