Curriculum: Data Analytics Graduate Certificate

Next Term Application Deadline

07.31.2024

Total Credits

12 Credit Hours

Next Term Starts

08.26.2023

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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.

CURRICULUM AT A GLANCE

Core Courses Electives

Includes four core courses (eight credits). IS7030 is designated for students who wish to pursue a Master's degree in Information Systems. All other students must enroll in IS6030.

For more information about Core Courses reach out to your Student Success Coordinator.

Course	Title / Description	Credit
BANA 7038	Data Analysis Methods Course: BANA 7038 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
IS 6030	Data Management Course: IS 6030 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
IS 7030	Data Modeling Course: IS 7030 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
BANA 6043	Statistical Computing Course: BANA 6043 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

Students choose two elective courses (four credits).

Course	Title / Description	Credit
BANA 6037	Data Visualization Course: BANA 6037 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
BANA 6043	Statistical Computing Course: BANA 6043 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
BANA 7025	Data Wrangling Course: BANA 7025 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
BANA 7046	Data Mining I Course: BANA 7046 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
BANA 7047	Data Mining II Course: BANA 7047 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
BANA 8090	Special Topics in Business Analytics Course: BANA 8090 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
IS 7032	Database Design Course: IS 7032 Credit: 2 This course provides in-depth coverage of the principles of database design. It is a follow on to IS 7030. Having learned to develop relational data models in the first course, students start this course with concepts related to validating and revising the database design using normalization theory. This is followed by relational algebra and structured query language (SQL) for data definition (DDL), data manipulation(DML), data control (DCL), and deeper level of data querying (DQL) for the implementation of the database design. Finally, higher level normalization concepts are introduced. Workshop and laboratory sessions are included to provide hands-on learning experience in normalization procedures and SQL.	2
IS 7036	Data Mining for Business Intelligence Course: IS 7036 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
IS 8034	Big Data Integration Course: IS 8034 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
IS 8070	Special Topics in IS Course: IS 8070 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

FAQs

Can I complete the program part-time?

Programs can be completed full-time or part-time. Many programs are set up to be part-time, with most containing fewer than 10 credit hours in any given semester. We know you have a lot going on, so want to make sure that school can fit into your schedule. Note: There are some exceptions.
Are your online programs asynchronous, or on a different schedule, versus your on-campus programs?

Yes. Although UC Online programs follow the same academic calendar, they have unique schedules depending on the course.

(There are a few exceptions to the asynchronous components, please view program FAQs for more information)
Do your online courses follow a particular schedule or can I take the course I choose?

Students follow an intentional sequence of courses. Our main didactic courses (Hematology, Chemistry, Immunohematology, and Microbiology) will be available on a rotating schedule; i.e. each course offered every five semesters. The first semester courses are available each semester and must be successfully completed prior to a student entering the main didactic courses. Once you complete the first five semesters, you will enter the 6th and 7th semesters which have courses that are offered each semester.