Master of Science in Business Analytics Curriculum

Fall 2019/Spring 2020

Maryland Smith’s curriculum is interactive, data-focused and adjusts to reflect the trends of the modern business world. The courses you’ll take are taught by knowledgeable scholars and experts with years of experience leading others in their field.

Database Management Systems 
3 credits | BUDT 758Y

Decision Analytics
3 credits | BUDT 758P

What makes a decision difficult? Usually, the answer is that the decision requires us to spend scarce resources. A “resource” is any asset that can be leveraged to achieve business objectives: time, money, staff, trucks, computer cores, and research effort can all be viewed as resources. When you decide to spend your resource on something (e.g., investing into a research project, or accepting a delivery job), there is less of the resource that can be spent on something else. You thus have to think carefully about the tradeoffs involved in allocating resources to one objective as opposed to another.

This class develops a quantitative framework for studying resource allocation problems. Resource allocation problems arise in many industries and areas such as transportation, electronics, advertising, finance, and health care. The specifics of each problem are very different. Nonetheless, all resource allocation problems, in all of these areas, have common elements that behave in exactly the same way. We will develop an abstract modeling language that emphasizes these common elements, so that you will be able to write down any resource allocation problem on paper and then apply standard tools (such as Microsoft Excel) to obtain solutions.

Data, Models and Decisions
3 credits | BUDT 758Q

Data Mining and Predictive Analytics
3 credits | BUDT 758T

In business magazines, on TV, and in board rooms, “big data” and “data analytics” are now hot topics. Vast quantities of data are being generated these days, including new types of data such as web traffic, social network data, and reviews and comments on websites. This data is a valuable resource that, when used correctly, can create a competitive edge for companies by improving the quality of decision making. Recent advances in computing hardware and software have made the application of advanced analytical methods much easier. This course takes advantage of these developments to introduce data analytics to those interested in developing expertise in datadriven decision making.

The course is intended to provide an introduction to the tools and techniques of data mining & machine learning that are central to business analytics, with particular emphasis on classification and prediction. The focus will be on business applications and examples from Marketing, Finance, Healthcare, and Operations will be used to illustrate the breadth of applications.

Data Processing and Analysis in Python
3 credits | BUDT 758X

This course provides an introduction to the Python programming language for the purpose of processing, analyzing, and visualizing data. In addition, students will be introduced to developing basic regression, optimization, and simulation models in Python, using highly popular packages. Course emphasis is on mastering basic Python functionality and developing intermediate to advanced skills in working with data, through instruction and active learning. 

Big Data and Artificial Intelligence for Business
3 credits | BUDT 758B

Big data represents unprecedented opportunities for companies to generate insights and create wealth. Huge amount of data is being generated. At the same time, much of the big data is unstructured, in real time and only loosely connected. It defies the traditional ways of managing databases. This creates challenges even to tech-savvy companies on how to leverage big data to gain competitive advantages. Challenges and opportunities coexist. To extract the great value from the data, we should be equipped with advanced techniques. Artificial Intelligence (AI) is penetrating our daily routines deeply, and shows great promise in exciting areas such as healthcare and autonomous driving cars. 

This course uses a hands-on, learning-by-doing approach to understanding the concepts behind Big Data and AI, the strategic drivers of these technologies and the value propositions that they provide to industries. In addition, the course will also serve as an introduction to some of the key technologies within this ecosystem, such as Hadoop, AWS, Pig, Hive, Amazon Web Services and Spark. Examples of AI using Deep Learning will be conducted in class. The focus is on creating awareness of the technologies, allowing some level of familiarity with them through hands-on exercises and projects, and enabling strategic thinking around the use of these technologies in business.

Operations Analytics
2 credits |  BUDT 758V

Google Analytics Online Challenge
3 credits | BUDT 758F

Price Optimization and Revenue Management
3 credits | BUDT 758L

Business Communication
1 credit | BUDT 758A

Capstone Project
3 credits| BUDT 758W

0 Credits | BMGT099 | Download Sample Syllabus

This online course allows you to gain academic recognition for completing an internship in your area of study. This course is offered through Maryland Smith’s Office of Career Services (OCS).

  • The course is offered in fall, winter, spring and summer semesters.
  • You’ll apply the business concepts you learn in class to the real world. Gain more industry insight, clarify your career goals, and develop your professional and technical skills.
  • Assignments include completing a work log, reflection activity, LinkedIn post and employer survey. All are completed virtually using Canvas.

Prerequisites and Guidelines

Prior approval from OCS is required to register for the course. More information on this process can be found in the Registration Process, Process Map and Registration Approval Checklist documents. To be approved, you must:

  • Be in good academic standing. 
    You must be a current student enrolled in a specialty master’s program for at least two consecutive semesters. You must also have a GPA of 3.0 or higher.
  • Meet the working hours requirement and location guidelines. 
    You must work at least 75 hours at your internship during that semester. Depending on which semester you enroll, workplace location guidelines may also apply.
  • Update your Internship putcome and complete an Experiential Learning profile. 
    Submit your information on HireSmith and upload the following forms to your Experiential Learning profile:

Special Guidelines for International Students

Registration in this course does not guarantee Curricular Practical Training (CPT). However, this course does fulfill the academic requirement for CPT. Authorization is granted by the Office of International Student and Scholars and Services (ISSS). Once you are enrolled in this course, see ISSS for more details on CPT authorization.

For questions about the course and receiving internship credit, email Angela Vaughn, director of Specialty Master’s Programs for OCS, at