Master of Science in Business Analytics for Working Professionals

Graduate & Executive Education / Master of Science in Business Analytics for Working Professionals

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Haslam Master of Science in Business Analytics: Using the Power of Data to Drive Business Outcomes


Build analytical skills and advance your career with Haslam’s Master of Business Analytics (MSBA) for Working Professionals. The program offers a unique mix of online and in-person learning, providing flexibility for all students. Expand your network, learn from expert faculty and develop the technical skills necessary for an advanced business analytics career.

Develop the technical acumen to derive insight from large data sets and the business fundamentals and analytics communication skills needed to drive organizational change.

Enjoy highly rated curriculum that combines technical experience with practical experience. As a Haslam MBSA graduate, be prepared to drive success and change with cutting-edge analytics from day one with your organization

Ready to become a dynamic analytics professional?

AT A GLANCE

Duration: 16- or 28-month programs available

Credit Hours: 30 hours

Online Component: live online course sessions, 1 evening per week

In-person Component: 2 one-week immersion sessions in Knoxville (mid-August); four weekends in a flexible location. For the cohort entering in 2024, the weekend location will be Nashville.

Start Date: A new cohort begins each August

Top 10


Most Prominent Analytics and Data Science Institutes

Analytics Insight Magazine

#15


Among all public universities in 2024 – Undergraduate Business Programs

US News And World Report

Designed for Working Professionals


The MSBA for Working Professionals program at the University of Tennessee, Knoxville, is designed for ambitious, fast-track professionals looking to grow their analytics knowledge and advance their careers. The ideal student is working full-time and has at least two years of work experience.

With a focus on both technical skills and professional development, the MSBA for Working Professionals degree is ideal for individuals with at least two years of professional work experience and who aspire to take on increasing levels of responsibility by using analytical tools to help all areas of their organization.

What You’ll Learn


As a Haslam MSBA for Working Professionals student, you will learn innovative data science applications and essential skills in analytical tools such as R, SQL and Python. You will also have the opportunity to learn from classmates through professional case studies and presentations while simultaneously practicing business communication techniques.

You will gain the technical acumen to derive insights from large data sets, as well as the communication skills necessary to explain those insights to a larger audience.

By developing technical skills and honing leadership, teamwork and presentation skills, you will graduate from the part-time MSBA program poised to drive organizational change.

Program Structure


Combining on-campus and distance learning, Haslam’s MSBA for Working Professionals 16- and 28-month programs offer a flexible modality that enables you to continue working full-time while earning your degree. The applied learning approach enables you to leverage real workplace challenges to master key business skills.

IN-PERSON LEARNING

During the 16-month and 28-month programs, there will be weekly live, online classes, two in-person immersion weeks in Knoxville (mid-August) and four in-person weekend sessions (eight days total) in a flexible location. The 2024 cohort weekend sessions will be Nashville. These experiences enable you to connect with your classmates and engage with expert faculty members.

DISTANCE LEARNING

The program incorporates faculty-led virtual learning sessions to bring course content to students in an accessible and interactive format. Students complete distance learning sessions on most Tuesday evenings throughout the duration of the program.

First In-Person Immersion Week

BZAN 533 – Probability & Statistics

BZAN 546 – Simulation

3 credit hours

Knoxville (Mon – Fri)

Fall 1

BZAN 533 – Probability & Statistics

BZAN 537 – Security & Ethics

BZAN 545 – Data Engineering

6 credit hours

Includes one in-person weekend in Nashville (Fri & Sat)

Spring

BZAN 535 – Statistics Methods

BZAN 542 – Data Mining

6 credit hours

Includes one in-person weekend in Nashville (Fri & Sat)

Summer

BZAN 540 – Regression

BZAN 557 – Text Mining

6 credit hours

Includes one in-person weekend in Nashville (Fri & Sat)

Second In-Person Immersion Week

BZAN 548 – Time Series

BZAN 536 – Business Analytics Cases

3 credit hours

Knoxville (Mon – Fri)

Fall 2

BZAN 583 – Special Topics I

BZAN 583 – Special Topics II

BZAN 531 – Optimization

6 credit hours

Includes one in-person weekend in Nashville (Fri & Sat)

*The 28-month program option delivers approximately one course per semester. Please contact us for more information.

Curriculum


Many scenarios in a business present a challenge to make the best decision possible to reach an optimal objective within organizational constraints. For example, the goal of adding new merchandise to an existing display shelf is to increase profitability. However, the amount of shelf space available for all products is limited. So, merchandising decisions are made to facilitate the optimal product tradeoffs to maximize overall category profit.  Linear programming models are uniquely designed to solve optimization problems, such as these, which focus on making decisions to accomplish a specific goal given a limited set of resources. In this course, students will learn to apply the principles of optimization to solve problems around operations, transportation, revenue management, media mix, scheduling, and portfolio management problems, just to name a few.

Business environments are inherently uncertain and influenced by numerous factors and variables. Probabilistic modeling helps in quantifying and managing this uncertainty by providing a framework to understand the likelihood of different outcomes. This allows businesses to make more informed decisions and mitigate risks effectively, for example, by setting performance metrics and evaluating business performance against expected outcomes. When uncertainty is incorporated into performance assessments, businesses can gain a more realistic view of their achievements and adjust strategies accordingly.  In this course, students will learn the fundamentals of capturing and understanding uncertainty by developing and implementing probability models for random quantities across all areas of business. 

Has your boss ever asked you to compare two versions of something, like choosing among different marketing campaigns, new product development/delivery processes, or determining factors that improve the lifetime value of your customers? Statistical principles and methods are used to both design and evaluate such comparisons (or A/B testing experiments), to inform business decision-making. In this course, students will learn to apply the principles of statistical inference and uncertainty quantification to aid business decision-making related to operations and marketing, just to name two.

Learning from your mistakes is great, but learning from other people’s mistakes is even better.  In this class, we will examine true-life stories of how organizations have used or misused data and analytics in ways that have led to frustration, embarrassment, expense, and even loss of life.  We will also examine at least one true life story that demonstrates how analytics can breathe new life into an organization.  Organized into small groups emulating consulting teams, you will have the opportunity to apply what you’ve learned in the Business Analytics program to practical problems, but with the benefit of hindsight.  This course will teach you how to read and understand a case,
how to bring analytical frameworks to the problem, and how to employ business analytics techniques to make money, enrich lives, and ensure safety.

Trust is as valuable as data.  Or perhaps trust is what makes data valuable.  For data to be used for decision-making, we must preserve the confidentiality, integrity, and availability of data.  But data is a double-edged sword, and as much as data can be an asset, it can also be a crippling liability if trust is breached.  This could be the loss of reputation from a security breach, the expense of mitigating a privacy breach, or lost business from a bad decision driven by a tainted source. A failure in security and ethics can end a company.  This course will give you the opportunity to understand the importance and methods of caring for the data you steward, the consequences of misuse of data, and ways in which you can ensure that data is used appropriately to benefit and not harm.

Regression analysis is the foundation of most statistical and machine-learning models.  Regression models are designed to help you understand relationships between variables.  For example, how do different marketing interventions affect sales?  What factors are most associated with a customer having a good experience in your hotel?  What are the background skills of managers that are successful?   Beyond understanding these relationships, regression models are a robust tool for predicting outcomes and understanding the uncertainty in these predictions.   Regression can allow you to predict concert ticket sales on a particular day of the week in a particular city or forecast the percentage of voters from a particular demographic that will vote for your candidate.   As you can see, regression analysis is a key tool for creating analytical insights from data.

Your company wants to understand customer buying patterns to optimize their marketing strategies. How can you solve this problem using data mining techniques? In this course, students will learn how to prepare data for analysis, explore it visually to uncover patterns and trends and use various techniques like cluster analysis, logistic regression, decision trees, and neural networks to solve business problems. These techniques can analyze past purchase data and identify groups of customers with similar buying behaviors (cluster analysis). Then, they can predict the likelihood of a customer purchasing a particular product (logistic regression) or create decision trees to determine the most effective marketing approach for different customer segments. Throughout the course, you’ll gain hands-on experience using standard computer packages to apply these techniques to real-world business scenarios.

The ability to efficiently manage, manipulate, and store data is paramount in a business world driven by data. The speed and complexity of data today demand robust data pipelines as a foundation from which we can subsequently extract valuable insights. In our Data Engineering course, we embrace this challenge head-on, equipping students with the essential tools and techniques necessary to thrive in the dynamic landscape of data engineering. Through a blend of theory and hands-on projects, students will gain proficiency in a wide array of technologies.

Paraphrasing the old saying that the only certainty in life is that nothing is certain, making good decisions in today’s dynamic, uncertain business environment is far from trivial. For example, how do you determine appropriate staffing and bed capacity levels for an emergency department seeking to accommodate uncertain, variable patient volumes while maximizing care quality given limited resources? Or how do you decide appropriate inventory replenishment – e.g., when and how much to reorder – to meet uncertain customer demand while minimizing supply chain costs and accounting for uncertain delivery lead times? Simulation models are uniquely suited to handle the dynamic and uncertain nature of business decisions and empower organizations to make robust decisions that can adapt to evolving circumstances. In this course, students will learn to apply the principles of Monte Carlo and discrete-event simulation to solve operations and supply chain problems in the presence of uncertainty.

It has always been humanity’s fascination to predict future events. We understand this is not necessarily possible in the broadest sense, but identifying the patterns and structural relationships within past data and utilizing these to forecast future values is a common modern practice in the industry, requiring a unique set of methods. Consider a certain product we would like to sell. For the upcoming month how much of it should we have in our inventory so that we will be able to meet the demand without a costly overstocking? Time-dependent data seem to hide certain dependencies to past values, such as the sales amount this month having a strong association with sales amounts of the last two months, as well as the same month of the last year. In this time series forecasting course, students will learn to unearth such relationships, build auto-regressive and other time series models based on these associations, and successfully conduct forecasting. All the while they will be mastering the strengths and limitations of such methods as well as effectively communicating their findings.

Ever felt overwhelmed by the sheer volume of emails flooding your inbox or wondered how to sift through endless customer reviews to find actionable feedback? Text mining is the key to unlocking meaningful insights hidden within this textual chaos. Text mining can help you analyze customer reviews, social media comments, and survey responses to pinpoint areas of improvement or features driving satisfaction. In this course, we demystify text mining and help you build practical skills that you can apply from day one. We’ll show you how to transform unstructured text into actionable insights by learning simple yet powerful techniques to preprocess text data, making it ready for analysis. We will identify trends and patterns by diving into the world of sentiment analysis, topic modeling, and named entity recognition. Armed with the insights gained from text mining, you’ll be better equipped to make informed decisions in areas like product development, customer service, and market strategy. By the end of this course, you’ll have the tools and confidence to navigate the data jungle, extracting valuable nuggets of insight to drive your business forward. Say goodbye to information overload and hello to actionable intelligence with text mining.

Admissions


Applications for the MSBA for Working Professionals program are submitted through the University of Tennessee’s Graduate School; however, Haslam has application requirements specific to the MSBA.

Application Quick Links

Apply To The Program

Requirements

Applicants should have at least two years of work experience. All undergraduate degrees are accepted, but the most successful students have a strong technical aptitude and have taken quantitative courses such as math, statistics, engineering, computer science and calculus. Due to the lock-step nature of our program, transfer credits are not accepted.

A strong record of academic achievement, as evidenced by a high GPA, is expected of all applicants. Applicants with undergraduate GPAs below 3.0 are typically not considered.

All students are expected to demonstrate quantitative abilities either through undergraduate coursework or by completing a quantitative assessment test such as the Pearson MyMath or GRE/GMAT test, or an approved test waiver.

Official test scores can be sent to the Haslam College of Business at the University of Tennessee, Knoxville. GMAT ID: 8GR-KN-71, GRE ID: 2834

Applicants whose native language is not English must score at least 600 or 100 IBT for the TOEFL or 7.5 for the IELTS. Test scores are valid for two years. Please refer to the Graduate School’s website for more details.

At least two current references are required, one of which should be a professional reference. In the application, please enter the name and email address of your reference(s). Each reference will receive an email with a form when you submit your application. The admissions committee prefers to see one academic (faculty) and one professional (current/previous supervisor) reference.

Admission opens in August, and applications are accepted on a rolling basis until the class is full.

International applicants must meet the UT Graduate School’s February 1 application submission deadline.

Final priority review date – July 1

Tuition


Estimated tuition and fees:

In-State

$34,632

Out-of-State

$65,456

Contact us for more information!


Katie Wood
Assistant Director of Admissions, Graduate Business Programs
(865) 974-3959
Kwood31@ utk.edu

Bogdan Bichescu
MSBA Director
bbichescu@utk.edu