- Ygor Bortolato, Brazil
MBA with a concentration in Data Analytics
More than ever, today’s global economy requires a deeper understanding of products, customers, competitors and strategies. The world of business is quickly converging to a personalized market for goods and services that requires a thorough understanding of what customers want, when they want it and where they want it.
Graduate Program Tabs
The goal of the TU MBA Data Analytics concentration is to introduce students to the tools and techniques that Data Analysts use to examine data in order to draw meaningful and actionable conclusions. Students will learn how to gather data, examine the suitability of the data, determine relevant models, calculate parameters of the models using software, interpret and communicate the results and suggest strategies to improve business performance.
Our program is unique in several ways:
- Courses taught by real faculty with expertise in analytics
- Live interaction with and access to faculty
- A highly robust curriculum that includes such topics as data acquisition, management of data, use of statistical software to produce intelligent outcomes, knowledge of concepts that advance the understanding of “what and why to do” and data visualization and presentation
- “Hands on” use of real data to advance understanding of concepts
- Case based format to further reinforce the understanding of concepts
- Capability to complete the concentration in less than a year
- Cost that is very reasonable and highly competitive
- A high Return on Investment (ROI), based on industry trends
Data Analytics Concentration 12 hours
- DAX 511 Data Mining
- DAX 521 Descriptive & Inferential Statistics
- DAX 631 Advanced Inferential Techniques
- DAX 641 Data Visualization & Presentation
MBA Core Curriculum 24 hours
- MGT 515 Managerial Business Foundations**
- MGT 516 Fundamentals of Quantitative Business**
- MGT 522 Human Resources
- MKT 523 Marketing Management
- ECO 524 Managerial Economics
- MGT 526 Quantitative Business Analysis and Research
- FIN 612 Managerial Finance
- MGT 614 Global & Transnational Management
- MGT 621 Org Analysis and Design
- MGT 622 Strategic Management
- MGT 623 Legal & Ethical Issues in Management
- MGT 630 Innovative Decision Making
**MBA core courses required of non-business undergraduate majors, international students and special admit applications.
Total MBA 36 hours
This is a sample course sequence to illustrate course offerings for this major. Consult the official Academic Bulletin for detailed registration and advising information.
Online: Two 7-week terms per semester starting in January, March, May, July, August and October
On Campus: Two 7-week terms per semester starting in January, May, August and October
- Core MBA, Leadership & Change and Data Analytics courses are offered online or on campus; remaining concentration courses are offered exclusively online.
Data Mining (DAX511) - In this course, students will learn how to load different types of data files into a usable format as well as levering external data sourcs with internal data. Students will become proficient with articulating problem solving techniques that involves data from different sources and the value external data sources can provide to organizations
Descriptive & Inferential Statistics (DAX521) - In the course, students will develop techniques to help visualize and understand the data gathered. Students will also learn how to draw inferences from the data using some simpler models and statistical techniques.
Advanced Inferential Techniques (DAX631) - This course will focus on building and using different tools to find information within the data sets retrieved. Students will be expected to review data sets and find value within the data sets for the data owners. The data can answer questions and it can generate additional questions to be asked using SAS and SPSS software.
Data Visualization & Presentation (DAX641) - This course will conclude concepts in data analytics and statistics to allow students to complete the process of answering a question through statistical analysis. Students will discuss how to build an appropriate model; what data to include in the model; how to interpret and make predictions from the model; and, finally how to present your results to people who are unfamiliar with data analytics in a clear and concise manner.