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  1. Home
  2. Programs
  3. Online Master's Degrees & Certificates
  4. Online Master's Degrees
  5. Business Analytics

Guide decision-making in your organization with an online MS in Business Analytics (MSBA)

With an online MS in Business Analytics, you’ll be able to strategically position yourself as a brand analytics manager, e-commerce project manager, web metrics analyst, or a consultant to senior decision makers. Position yourself on the leading edge in these careers by taking our advanced courses in machine learning and generative AI. You’ll be in demand as businesses around the world seek to leverage data to innovate and stay competitive. As one of the first top-ranked schools to offer an MS in Business Analytics, Kelley sets the pace in this field and provides innovative solutions to today’s business landscape.

Typical time to completion is between fifteen months and two and a half years, with the option to take classes at the pace you choose. Complete this 30 credit hour degree and graduate prepared to:

  • Make business decisions based on analytic modeling
  • Think strategically in management situations
  • Unlock valuable statistical information from any dataset
  • Develop analytical models to provide valuable insight across multiple business function areas
  • Gain cutting-edge AI skills with innovative machine learning approaches

If you have an undergraduate degree in business, economics, information technology, engineering, or statistics, an online MS in Business Analytics can refine your focus as a specialist. If you have an MBA, this degree will help you develop more in-depth analytical expertise.

Applications are now open for fall 2025

Application deadline: June 15

Start your application

Interested to learn more?

Request info

Description of the video:

Hello. My name is Carolyn Goerner, and I am the faculty chair for Kelley Executive Education Programs, the home of the Online Master of Science and Business Analytics or MSBA. The online MSBA is the ultimate career enhancer. Quantitative and analytical skills tied with a good understanding of data will allow you to leverage your existing knowledge in ways that will help you to be in demand as a professional, regardless of the field where you work. This is a degree that will support the career aspirations of professionals in fields as diverse as marketing, finance, human resources, and operations. We offer a comprehensive curriculum distributed across 30 credits, covering topics like predictive analytics, data intelligence, visualization, simulation and optimization, and game theory. This degree will prepare you to make business decisions based on analytic modeling, to think strategically in management situations, to unlock valuable statistical information from any dataset and to develop analytical models to provide valuable insight across multiple business functional areas. We also know that you are a busy person, someone who needs to balance your education with professional and family needs. Because of that, we've designed this program to allow you to advance at your own pace in each of our courses. Our faculty experts have recorded content in our state of the art, Jellison Studios. That content is designed to be consumed by you in weekly modules at your convenience with assignments complete at the end of each week. Very importantly, the same faculty who designed and created the content will be the faculty teaching the course. Ally was the first major business school to develop online education, and our model has always been one in which students have direct access to our top experts. We do not delegate course management to facilitators. Instead, you will have direct access to our top faculty in bi weekly live sessions. Now, these live sessions will be recorded and they're not required. But even if you can't attend them, our professors will always be one message or post away whenever you need help. Our online MSBA is designed to be finished 15 to 30 months, depending on how fast you want to complete the program. This is a degree designed with you in mind. Adapting to your needs while enhancing your career cross sects with each course taken. All our courses have practical learning objectives that can be directly translated into your career. The Kelly Online MSBA is the ultimate degree for those who need analytical skills to elevate their career prospects. This is a degree from the Kelly School of Business, a leader in online education, recognized in many rankings as offering the best online programs in business education and delivered by top experts in the field. So join us. Let's build your career together.

10

online courses

30

credit hours

$25,500

total tuition

Admission requirements

Fill out the online application first.

After you complete the Kelley application, you’ll receive an email with instructions on how to apply through the Graduate CAS Application. During this stage, you will upload the following materials to complete your application:

  • Resume
    Provide a copy of your resume, summarizing your professional experiences and accomplishments.
  • Personal statement
    Tell us what you want to achieve in this program in 500 words or less.
  • Letter of recommendation
    Ask someone familiar with your professional career to write a letter of recommendation for you. Provide the contact information of your recommender within the application. An email prompt will be sent to their email address, allowing them to fill out a form and upload the letter. If you need alternative arrangements, please contact keep@iu.edu.
  • Undergraduate transcripts
    Provide copies of your transcripts from the undergraduate institutions you attended. Your transcripts can be uploaded into the Part 2 Application.

Hear from program leaders at an online info session

Do you have questions about our MS in Business Analytics program? Register for one of our upcoming Zoom info sessions to connect with our admissions team and faculty chair.

General info session

May 13 at 4 p.m. ET

Courses

There are 10 required courses for the online MS in Business Analytics.

*Plans of study and course descriptions are subject to change*

In this course, we enhance the students basic statistical and mathematical modeling skills covering the following topics: 1. probabilistic decision making, 2. regression analysis, 3. forecasting 4. simulation models and 5. optimization modeling with the EXCEL Solver.

Game Theory has traditionally been a tool of economists, but its use in management situations has been growing rapidly in recent years. This trend is sure to continue. Managerial decisions are not static and cannot be made in isolation. Instead, a manager must account for the reactions of both rival firms, subordinates, and superiors to these directives and proposals. Game theory is a tool to use to examine these interactions. The course extends the analysis of game theory and business strategy that you began in the Managerial Economics portion of the Core. The ultimate aim of the course is to strengthen your ability to think strategically in business situations, rather than to teach you facts or theories. To achieve this aim, we will iterate between theory and practice. We will use both formal case studies and real world examples to sharpen our strategic thinking skills.

This course on data mining and predictive analytics provides students with both the conceptual underpinnings of a broad variety of data mining models as well as experience with analyzing real data sets.

Topics include: data preparation, cleaning, and exploratory analysis using data visualization and descriptive statistics; applications of multiple regression for numeric prediction; building predictive models using logistic regression, k-nearest neighbors, Naive Bayes, classification and regression trees, neural nets, discriminant analysis, advanced predictive techniques based on ensembles of predictions such as bagging and boosting, and selected time series forecasting methods; finding patterns in data using unsupervised models including principle components and cluster analysis; evaluating the performance of predictive models using training, validation, and testing data subsets as well as k-fold cross-validation; evaluating the performance of predictive and classification models using Receiver Operating Characteristic (ROC), lift charts and statistics on confusion matrices.

The class sessions provide overview of the theory behind each model as well as demonstrations using Excel, Jupyter, and other tools as needed.

Per the course catalog, this course explores an end-to-end framework for managing data as assets to enhance managerial decision-making. You will learn to create databases, manage data throughout its lifecycle, and generate reports and dashboards using data visualization tools. Additionally, you'll gain hands-on experience with SQL, Tableau, Talend, Alteryx, and more.

This course helps students to effectively analyze data using machine learning approaches, enhance prediction performance and robustness, and extract valuable insights from large datasets to support managerial decision-making processes. The course structures machine learning knowledge into four layers—applications, tasks, models, and algorithms—applied to a variety of business contexts.

This course focuses on building mathematical models for applied business situations using primarily optimization and simulation. The emphasis is on practical solution methods while gaining managerial insight. Topics include math programming for linear, integer, and non-linear models where finding an optimal solution can be found for reasonably sized models; models for finding good solutions for combinatorial problems; simulation techniques to evaluate the risk of solutions over a range of scenarios; and methods for modeling time varying demand by identifying elements like trend and seasonality, and ultimately creating a practical forecast.

Students will learn to create generic models but use Microsoft’s Excel as the solution tool. By the end of the course, students should be able to build complete models from a written explanation including the data set, enter the model into Excel, solve the model, and then be able to offer insights and make managerial decision recommendations from their model.

This course focuses on the application of analytical techniques to specific business applications and on application-specific interpretation of analytical models. Analytical techniques include Monte Carlo simulation, linear programming, nonlinear optimization, discrete optimization, linear regression, logistic regression, and data envelopment analysis. These models are constructed using a combination of Excel tools and Python. At the conclusion of the course, students will know how to structure spreadsheet models and basic programming models to complete analysis of business applications, as well as how to utilize Python libraries to analyze larger datasets.

Statistics are often met with skepticism and are seen by many as highly manipulable. However, they can be a powerful tool for unlocking valuable information from any dataset. Econometrics is the application of statistics and mathematics to economic and financial data. As these types of data have become more readily available and as computers have become much more powerful, econometrics is playing an even greater role in business forecasting, marketing, and strategic decision-making. In this course, we will study fundamental econometric models, their statistical properties, and how to apply them to real data. The goal is for you to finish the course feeling comfortable estimating, interpreting, critiquing, and justifying commonly used econometrics models for cross-sectional data – skills that can also be applied for other types of data, including time series and panel. Consequently, you will be equipped to extract information from datasets that businesses and/or government organizations will value, identify strengths and weaknesses in others’ econometric analyses, and properly address challenges to your econometric analyses if and when they arise.

The goal of this course is to develop the analytical tools and hands-on experience with data and economic models to optimally utilize information in decision-making, often in the context of economic consulting. We will cover data management and descriptive statistics, along with advanced analysis including policy evaluation and endogeneity control. We will discuss these topics in the context of classic economic and business questions. In addition, students will develop presentation and communication skills, particularly with regard to quantitative outputs. Finally, students will learn the basics of identification in order to better understand which data is most useful to collect when answering a given empirical question.

This course covers four modules providing an overview of: (1) prevailing application environments, modern data generating processes, data exploration approaches, and fundamental data encoding structures; (2) fundamental ML concepts; (3) DL and GenAI design learning strategy, implementation, and sample applications; and (4) DL and GenAI model evaluation, deployment, and presentation.

Course delivery model crafted for your convenience

Our asynchronous model allows you to consume content on your own time.

  • The content is recorded by our top-ranked Kelley faculty.
  • You have the option to enhance your learning experience by attending live virtual sessions with Kelley faculty every other week.
  • You will receive course content in weekly modules and can choose which day of the week to complete assignments.

Request info

Join Professor John Hill in this brief clip from K520 as he breaks down data tables in a relatable, hands-on way. This is just a snapshot of how Kelley’s online classes create an engaging learning experience that goes beyond the virtual classroom.

Description of the video:

Let's gauge here is measuring pressure in this tank. That's the output. You can imagine a lot of things that affect that pressure, what's inside the tank, temperature of the tank, and who knows what else? I'm not a chemist. But in this video, we're going to explore the relationship between inputs and outputs and specifically look at how we can use data tables to quickly assess how an output changes when our input. One of the more flexible tools in analyzing business problems in Excel is called a data table. Let's video. Let's look at how to build a data table and how to apply a data table. Why data tables. Data table provide a very quick and efficient way to measure how different inputs in a model affect an output. I'm building a spreadsheet with a bunch of calculations, I don't want to have to manually go in and change numbers and somehow manually record the output changing. I want an automated way to do this, and data tables provide that automated way to do it. It can evaluate multiple inputs at once, and it's much quicker and more systematic than manual approaches. It can also provide a steady state output for randomness. So if my inputs are changing randomly, I can use a data table to say, what on average would I expect the output to be? And it allows a complex analysis to be completed with just a few random variables. We're going to get to the point where we're doing simulations in this course, and data tables provide a simple way to execute those simulations without having to create loads and loads of randomness on its own.

What do I need for a data table? Well, I need one or two inputs. Fortunately, I can't do more than two based on how data tables are structured. We'll see that in a minute. But I take one or two inputs and I need to identify an output that I wish to measure. I need a spreadsheet model then that's going to connect the input to the output. If the input is not connected to the output, then when I change the inputs to the model, the output isn't going to move, Data table is not going to tell me anything. Let's look at a quick example here. So I've got a model here. I've copied a spreadsheet screenshot, and this is a loan example right here. So in this case, I'm borrowing $10,000 on a 6.2% interest rate over the course of five years. And to make this problem simpler, I'm just going to assume we're making annual payments instead of monthly payments. I also am going to put $1,000 down. So that makes my loan amount $9,000. You can see here it's just the purchase price minus the down payment, and then I'm going to calculate a yearly payment using the payment function in Excel, and that's shown here as well. So let's think about identifying our inputs and our outputs. I want to try to figure out how does the length of the loan, the term here, currently five, affect my payment? And how does my down payment affect my payment? Now, conceptually, we already know that the longer you make the loan, the smaller my payments going to be. And the more money I put down, the smaller the loan amount is and therefore, the smaller my payments are. But because we know what to expect, it makes a great example for introducing the data tables. Okay? So I need to make sure that my output changes when my inputs change. So let's look at the equations in this table that I've pasted here. I've got the payment function for the yearly payment. And that payment function refers to my interest rate, the terms, and the loan amount. Now, one of the inputs I said was the length of the loan. That's the term in this equation. So that's directly inside my output function. The other input was the down payment. Now, I don't have down payment directly in this function, but I do have the loan amount in this function. And the loan amount, if we go up one row on that table, refers directly to the down payment. Right? So as I change the down payment, that makes a loan amount change, which then makes the monthly payment change or the yearly payment change, right? So, they don't have to be directly connected, but there has to be a link through my spreadsheet that eventually connects my inputs to my output. I'm going to create a table here that you see in the middle of the screen that contains inputs as rows and or columns. In this case, I have two inputs, so I have one in the row and one in the column. Across the top row, I have different links of the loan, a one year loan, a two year loan, a three year loan, a four year loan, a five year loan, et cetera. On the way down, I've got down payments from $1,000 down to $5,000. So different loan terms, I think it's three to seven years, and different down payments, $1,000 to $5,000. In the top left corner, I have another cell reference in red there, and that is a cell reference to the box there that is the yearly payment. So that number is not typed in. Every other number on there is typed in. That number is not typed in. That number is equal to, and then I've referenced whatever cell I calculated the payment inside. So what this table is going to do, I am going to tell it to put those various values for length of loan up into the terms box, where the five is right now. I'm going to tell it to sit those down payments up into the down payment box, where the $1,000 is right now. And when it does that, for each combination of down payment and term, it's going to go back to the cell if reference in the top left corner, read the new value for the payments, and insert it into that table in the appropriate box. Okay? The way I do that is under my data ribbon, I use the table option for what if analysis. I'm going to what if analysis. You'll see a little question mark when we get into Excel, you'll see it. Under what if analysis is a data table option. If I click on that, I get this lower blue box. The blue box asks for these inputs. What it's asking me is for the row input cell, it's looking at my table that I highlighted before clicking the what if analysis, and it's saying, Okay, that top row, those numbers, three through seven, what are those? I need to tell it those are terms of the loan because it doesn't know. It might try to put those in as the interest rate or the purchase price, unless I tell where it goes. I'm going to take those numbers. I'm going to cell reference in that first box for row input cell, the cell that contains a term of the loan, which is that third cell down in the very top table. And then for column input cell, it wants to know where do these down payment values go. I'm going to reference the cell that contains the down payment in my model, the fourth box down in that top table. Once I reference those two things, it knows where to put those values to calculate new payment amounts. When I do that, the output of this model looks something like this. Again, that number $2,148, that payment amount, that comes from a cell reference, the very bottom element in my spreadsheet model. I've told it, take the length of the loans, put it up in the terms, Take the down payment amount, put it up where the down payment goes, and it has auto filled the meat of that table that's red, yellow, and green. Now, why is it red, yellow, and green? Because I put some conditional formatting down there to make it easier to read. I'll show you that in Excel a little bit as well. But what we can see here is exactly what we expected. If we assume a lower payment is better for most of it it is, we can see that either increase in the terms of the loan or increase in the down payment or some combination of the two results in the most favorable payment, the lowest payment. Now we're going to pay more interest if we have a longer loan, but that's not what I'm measuring here. I'm measuring what the payment amount is. So with this data table, you can see I didn't have to go and manually change the values for term and down payment and manually record the outputs. This very quickly goes through in a millisecond, evaluates all the terms, all the down payments, and records for me all the outputs that I need to look at. So let's go back into Excel now. I'm going to jump into the same example, work through it mechanically, show you how I did it, and then break it down a little bit further.

So, I showed you the output of the data table, Let's go ahead and look how I built the thing in the first place. We're going to vary the length of the loan 3 and 8 years and vary the down payment here $1,000 and $5,000. Again, we want to put our data table are the yearly payments associated with each of those combinations of different terms and different down payments. Let's start by building a row that represents the different lengths of the loans in years. Down a column, how much of a down payment we want to have.

And, inside this table, I want to report the yearly payments. In the upper left corner, I'm going to reference this cell right here. What this data table is going to do for me is one at a time, stick these lengths of the loan into this cell where it belongs and take these values and put them in this cell. If it sticks a three over here, and 100 here, it should record $33,79.45 into this cell. If it sticks a seven up here for the length of the loan, and a $4,000 down payment, it should stick $1,082.46 into the cell right here.

Now obviously, I can do this manually, but the data table is going to do it a lot more quickly. Once I have the data table constructed, I'm going to select the entire table. Under my Data ribbon, I can go over to the what if analysis with the question mark. From there, I will select the data table option. The table here wants to know two values, a row input cell and a column input cell. What do these mean? The row input cell is asking me what to do with the values on the top row of my selected data table. In other words, in what cell, do these values belong when calculating payment? These values belong in this model in cell B7. That's where the term is. The column input cell wants to know, where do these numbers belong? Well, these represent down payment amounts. I'm going to place them in B5. I've got my row input for terms in B4. I've got my column input for down payment in B5. This tells Excel to one by one put these numbers in B4, these numbers in B5, and for every combination, because I've referenced B7 here, record the new value of B7 in the appropriate square in my table. And here you can see that's done that. Just to check it. If I have six years on my loan and make a $2,000 down payment, my yearly payment is $1,637.14. That six years and $1,637.14. So there you have it, regardless of whether you're worried about pressure inside this tank or the car payment for your car loan, data tables are a way to quickly assess how change in input can affect an output.

Apply for a graduate certificate and the online MS in Business Analytics at the same time

A professional is shown in front of a presentation screen sharing business analytics data, demonstrating the skills acquired from an online master's in business analytics.

You can apply for both the Business Analytics Graduate Certificate and the online MS in Business Analytics at the same time. Once you successfully pass the designated certificate courses, you'll receive your graduate certificate. Complete all 10 courses and you'll earn a Master of Science in Business Analytics from the Indiana University Kelley School of Business. 

Part 1 of the application takes five minutes.

Start your application

Another option is to combine the MS in IT Management with the MS in Business Analytics. This allows you to gain advanced skills in both technology leadership and data analytics. With four shared courses, you can earn the MSBA by completing just six additional classes after finishing your MSITM. This powerful and cost-effective combination will equip you with expertise in two of the most sought-after areas in today's job market.

Learn more about combining these two Kelley MS degrees by emailing keep@iu.edu. 

Upcoming terms for academic year 2025–26

Summer 2025

May 18 to August 6

Fall 2025

August 18 to November 6

Application deadline: June 15

Winter 2025

November 10 to
February 19

Spring 2026

February 23 to May 14

Application deadline: December 1

Frequently asked questions (FAQs)

Below you’ll find the answers to the questions prospective students ask us about the MS in Business Analytics program. If you need further assistance or can't find what you're looking for, please feel free to reach out to us at keep@iu.edu or give us a call at 812-856-5366. We're here to help!

We conceptualized this program as a career enhancer, more than a tool for career transition. Undoubtedly, this program will deepen and widen your skill set and increase your marketability. However, we would encourage you to seek out informational interviews with individuals in your desired job to identify all the variables that will go into a prospective employer’s assessment of your viability for such a position.

We believe that successfully completing our program will make you a stronger candidate for (and improve your job performance in) any position that requires outstanding quantitative skills. For example, if you work in a finance or accounting role, our program will teach you how to develop more sophisticated financial models. If you work in a marketing or sales role, our program will allow you to analyze your results in greater depth and forecast more accurately. If you work in an operations or supply chain role, our program will allow you to run simulations and predictive models to anticipate potential issues.

As such, if you desire a position where quantitative skills are important, this program will provide you with the tools you need. However, please see our answer to the question above regarding the many variables that contribute to a candidate’s qualifications for a new role/industry.

We use Tableau in our courses. However, no prior experience is needed.

Broadly speaking, people who are comfortable with quantitative methods will have an easier time in this program. For example, if you took a statistics class as an undergraduate and did well, the MSBA will likely be a comfortable next step. Ultimately, only you can decide what is best for you, based on your prior experience, existing skill set, learning style, and long-term goals.

We will use Python in multiple classes. There is no expectation that you will already be familiar with it, however, prior exposure to programming concepts will be of additional benefit to you.

We recommend looking up the 10 required courses in the Kelley School of Business course catalog.

The 10 courses are:

C520 Quantitative Analysis

C531 Predictive Analytics/Data Mining

C533 Data Intelligence and Visualization

C534 Simulation and Optimization for Business Analytics

C535 Developing Value Through Business Analytics Applications (C534 is a prerequisite for this course)

C527 Business Econometrics

C528 Data Predictive Analysis & Business Strategy (C527 is a prerequisite for this course)

C565 Thinking Strategically: Game Theory & Business Strategy

C570 Strategic Marketing Management

C580 Operations Management

The course catalog can be found here. Please note, that you’ll need to scroll down to the Online Graduate section.

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