Certificate of Advanced Study
in Data Science

Earn your certificate of advanced study in data science.

Being able to understand and apply data-driven insights can help businesses understand their customer’s needs, solve unique problems in creative ways, and use innovative techniques to improve their operations processes. With our Data Science certificate program, you’ll gain the theoretical background and technical skill needed to give you a competitive edge in any industry.

  • Gain practical skills to leverage data-driven insights and improve business operations across industries.
  • Learn to handle the full lifecycle of data, from acquisition to analysis and archiving.
  • Prepare for in-demand roles in data science, one of the top-ranked professions for job satisfaction and market demand.
  • Acquire interdisciplinary expertise in areas like statistics, scripting, project management, and information visualization.
  • Graduate ready to solve real-world challenges in diverse fields such as healthcare, finance, national defense, and scientific research.
Quick Info

Next term starts Spring 2025, on campus

5 Courses / 15 Total Credit Hours

Courses & Curriculum

This certificate requires 15 graduate credits. All courses are 3 graduate credits unless specified otherwise.

View Full Course Catalog

Primary Core – 6 credits

IST 659 | 3 CREDITS
Definition, development, and management of databases for information systems. Data analysis techniques, data modeling, and schema design. Query languages and search specifications. Overview of file organization for databases. Data administration concepts and skills.

IST 687 | 3 CREDITS
Introduces information professionals to fundamentals about data and the standards, technologies, and methods for organizing, managing, curating, preserving, and using data. Discusses broader issues relating to data management, quality control and publication of data.

Electives – 9 credits

Students need to choose three classes from the list of electives below

IST 644 | 3 CREDITS
Increase the agility of a data science project by improving the process a team uses to execute their project. Explore data science life cycles (e.g., CRISP-DM, TDSP) and collaboration frameworks (e.g., Kanban, Scrum).

IST 652 | 3 CREDITS
Double Numbered with IST 462
Scripting for the data analysis pipeline. Acquiring, accessing and transforming data In the forms of structured, semi- structured and unstructured data. Additional work for graduate students.

IST 664 | 3 CREDITS
Crosslisted with CIS 668
Linguistic and computational aspect of natural language processing technologies. Lectures, readings, and projects in the computational techniques required to perform all levels of linguistic processing of text. Additional work required of graduate students.

IST 681| 3 CREDITS
Introduces metadata modeling, data binding, vocabulary, interoperability, administration, tools, quality control, and evaluation. Examines international metadata standards, activities, and projects through case studies. Students will have hands-on experience with metadata management systems such as D-Space.
PREREQ IST 659

IST 686 | 3 CREDITS
Multiple strategies for inferential reasoning about quantitative data. Methods for connecting data provenance to substantive analytical conclusions.

IST 691 | 3 CREDITS
Introduction to Deep Learning concepts and techniques required to develop Deep Learning based applications. Hands-on experience applying models using open-source frameworks and packages.
PREREQ IST 687 OR IST 387 (B grade or higher)

IST 692 | 3 CREDITS
This course will provide students the critical skills necessary to discuss and evaluate more just and equitable AI models, as well as leverage Python or R packages to build such models.
PREREQ IST687 OR IST387 (B grade or higher)

IST 707 | 3 CREDITS
General overview of industry standard machine learning techniques and algorithms. Focus on machine learning model building and optimization, real-world applications, and future directions in the field. Hands-on experience with modern data science packages.
PREREQ IST 687, OR IST 387 with a minimum grade of B or higher

IST 718 | 3 CREDITS
A broad introduction to big data analytical and processing tools for information professionals. Students will develop a portfolio of theoretical and practical resources for several real-world case studies.
PREREQ IST687 & IST707, or IST387 & IST407 (B grade or higher)

Electives – 9 credits (continued)

IST 719 | 3 CREDITS
A broad introduction to data visualization for information professionals. Students will develop a portfolio of resources, demonstrations, recipes, and examples of various data visualization techniques.
PREREQ IST 687, OR IST 387 with a minimum grade of B or higher

IST 722 | 3 CREDITS
Introduction to concepts of business intelligence (BI) and the practice/techniques in building a BI solution. Focuses are on how to use data warehouses as a BI solution to make better organizational decisions.
PREREQ IST 659 OR IST 359 with a minimum grade of B or higher

IST 736 | 3 CREDITS
Introduces concepts and methods for knowledge discovery from large amount of text data, and the application of text mining techniques for business intelligence, digital humanities, and social behavior analysis

IST 737 | 3 CREDITS
Analytic dashboards find valuable insights from large scale data. Students will gain knowledge of human visual reasoning, and obtain technical skills necessary to design, develop and implement analytic dashboards for business, government, or personal data.

IST 769 | 3 CREDITS
Double Numbered with IST 469
Analyze relational and non-relational databases and corresponding database management system architectures. Learn to build complex database objects to support a variety of needs from big data and traditional perspectives. Data systems performance, scalability, security. Additional work required for graduate students.
PREREQ IST 659 OR IST 359 with a minimum grade of B or higher

MAS 766 | 3 CREDITS
General regression model, estimation methods, general linear hypothesis tests, residual analysis, indicator variables, multicollinearity, autoregressive model, weighted least squares, variable-screening procedures.

MAS 777 | 3 CREDITS
Fundamental concepts and procedures for forecasting discrete time series for planning and control. Regression analysis, ARIMA methods, econometric modeling, transfer functions, intervention analysis, Kalman filters, univariate and multivariate methods.
PREREQ MBC 638

MBC 638 | 3 CREDITS
Concepts, principles and methods to support scientific approach to managerial problem solving and process improvement. Basic statistical techniques, their appropriateness to situations and assumptions underlying their use.

SCM 651 | 3 CREDITS
Business analytics including advanced spreadsheets; relational database and SQL queries; statistical analysis in R including multi-linear regression, interactions, tests for regression assumptions, logit, probit; neural networks; and dashboards.

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