Data Analytics Graduate Certificate
Data analytics is growing rapidly in organizations across the globe. From large to small, public to private, and profit to nonprofit, organizations are using analytics to improve decision-making. Executives realize that leveraging new technologies and better utilizing available data can lead to more effective strategies and, ultimately, to better ways to service their customers.
However, many organizations lack the knowledge to effectively utilize data analytics. As a result, a strong demand for professionals with analytics skills has developed and will continue to grow. The data analytics certificate prepares individuals to develop logical data models, construct data warehouses, build visually effective data displays and use sophisticated analytical techniques to glean valuable insights.
The certificate includes four core courses (eight credits) and two elective courses (four credits).
|Course No.||Course Title||Course Description||Credit Hours|
|*BANA 6043||Statistical Computing||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.
|BANA 7038||Data Analysis Methods||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 SAS and R.
|IS 6030||Data Management||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.
|IS 7034||Data Warehousing for Business Intelligence||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 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.
|Course No.||Course Title||Course Description||Credit Hours|
||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.
||Applications Development Using VBA
||The use of visual basic for applications for the development of applications of management science models for planning and decision support in a spreadsheet environment.
||Data Mining I
||This is a course in statistical data mining with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Data Processing. Variable Selection for linear regression and generalized linear regression. Out-of-sample Cross Validation. Generalized Additive models. Nonparametric smoothing methods. Classification and Regression Tree. Neural Network. Monte Carlo Simulation.
||Data Mining II
||This is a course in statistical data mining II with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Missing Data Imputation, Bootstrapping, Boosting and Multiple Additive Regression Trees, Bayesian Trees, Support Vector Machine, Discriminant Analysis, Cluster Analysis, Factor Analysis,Principle Component Analysis.
||Database Design||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.
||Data Mining for Business Intelligence
||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.
||Managing Business Intelligence Projects
||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.
The electives listed above represent the typical set from which most students will choose. Students may be able to choose other electives to match specific career goals, with the approval of the Program Director.
*Please note BANA 6043 (Statistical Computing) is a prerequisites to BANA 7038 (Data Analysis Methods) and IS 7030 (Data Modeling) is a prerequisite to IS 7034 (Data Warehousing and Business Intelligence).
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 this certificate please contact:
Yichen Qin, PhD
Assistant Professor, Department of Operations, Business Analytics, and Information Systems
3366 Carl H. Lindner Hall