Master of Data Analytics (MDATA)

The Data Analytics & Information Systems and Economics and Finance Departments, within the Huntsman School of Business, as well as the Department of Mathematics & Statistics, in the College of Science, have created a program to produce graduates with the skills and competencies needed to support effective fact-based decision making. The Master of Data Analytics (MDATA) program in the Huntsman School of Business prepares students to become the next generation of business analysts, statisticians, and economists. The program integrates coursework in Data Analytics, Information Systems, Statistics, and Economics and Finance. The MDATA degree gives graduates a broad set of skills and knowledge to perform crucial data analysis, data management and business intelligence tasks in a variety of organizations.

Learning Goals and Objectives

L1: Students will be prepared to perform the steps required for a successful data analytics process.

  • L1.1: Students will demonstrate an understanding of the steps of the data analytics process.
  • L1.2: Students will demonstrate the skills to execute a data analytics project.

L2: Student will have the skills necessary to prepare them for the computing requirements of a data analytics professional.

  • L2.1: Students will use an appropriate programming language and coding environment to implement data analytics algorithms.
  • L2.2: Students will demonstrate the understanding and use of data structures and data manipulation in the context of data analytic tasks.

L3: Students will have the skills necessary to prepare them for the data manipulation and retrieval requirements of a data analytics professional.

  • L3.1: Students will utilize database systems and leading edge analytical and reporting tools to analyze big data and provide business intelligence.
  • L3.2: Students will understand the relational model, multidimensional model, object-relational techniques, and web accessed data.

Outcomes Data

The MDATA program gathers data for competency objectives from three of the foundational courses in the program where students demonstrate thier skills and knowledge necessary to perform as effective data analysts. Assessments focus on the goals and objectives of the program that are themselves derived from the strategic pillars of the Huntsman School of Business. Instructors gather measurements from assessments to compare to a-priori established performance benchmarks in key areas.

Links to outcome data are provided below. Users can also hover over each display to obtain more details on the assessments.

MDATA

Closing the Loop & Continuous Improvement

When assessments of program objectives are submitted, the submission form provides an opportunity to add “closing the loop” discussion, including changes that could be made. Examples of items that were noted include better exam preparation, more lecture time on coding, and improved rubrics. Additional notes included more focus on effectively defining a problem, keeping students focused on the most important issues, as well as a desire to bring in outsiders to provide feedback to the students.

Faculty are encouraged to innovate and experiment in the delivery of courses and content in this new and important discipline. During the past few years since the establishment of the program, our instructors developed and modified courses to deliver the most recent state-of-the-art skills. Experimentation and innovation are essential in such a rapidly developing area and faculty are given leverage in experimentation to achieve the most effective ways to prepare students. 

Our improvement efforts broadly fall into two categories: changes to improve student performance and changes to improve the gathering of assessment measures.

Here are some examples our instructors implemented to improve student performance:

  • Developing new course content (DATA 3500)
  • Developing real-world or quasi real-world analytics projects (DATA 6110)
  • Adding project management tasks (DATA 6110)
  • Integrating knowledge and skills from multiple courses (DATA 6110)
  • Spending more time on objective-relevant content (IS/DATA 6230)
  • More use of active learning, application of course material, and practice in low-risk scenarios (DATA 3500, IS/DATA 6230)

In terms of improving the gathering of assessment measures, instructors have identified a variety of potential changes to this process. Examples of these include:

  • Updating existing assessments (Exams, projects, presentations)
  • Creating new assessment forms (Demonstrations, presentations)
  • Reorganizing the timing of assessments (mid-course, end of course)
  • Standardizing and streamlining incentives to enhance participation for both on-campus and off-campus students

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