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Online MS Business Analytics Curriculum

Data is one of the most powerful resources for improving processes and performance for businesses who’ve invested in skilled data analysts.

The Mitchell E. Daniels, Jr. School of Business prepares students for the current and future landscape to draw valuable insights and implement optimal improvements in operations, procedures, and policies. Our Online Master of Science in Business Analytics (MSBA) program focuses equally on both tools and techniques, putting students to work on real industry projects centered on problems in business analytics derived either from outside organizations or Purdue University. Foundational courses elevate your technical and communication skills and a variety of electives allow you to tailor your studies to achieve your aspirations.

Your Coursework



Online MSBA students must complete 30 hours of coursework including core data and information courses, industry-specific electives for specialization, and general business electives to round out your business acumen.

Foundational Courses - 5 Credits

Data analysis and modeling are important skills for effective managerial decision-making in business and industry. Advances in technology have made a significant amount of data available to organizations. Managers now need the perspicacity to draw insights and create strategies. For example, the Dow Jones Industrial Average is one of the best-known and most widely watched indicators of the direction in which stock market values are heading. Administration and Congressional policymakers rely on statistics for budget decisions and related fiscal policy choices. The Federal Reserve System bases monetary policy on data analysis. A manager needs to know if the manufacturing process is producing a quality product based on monitoring and assessing process performance. A sales manager has to develop tools to regularly monitor the performance of its sales force. A manufacturer of certain electronic products needs to produce a forecast of future sales in order to decide whether or not to expand production. Banks use customer data to identify and design lucrative banking products. These are a few of the many examples from business where statistics can improve company performance. The techniques learned in this course will help you infer data and as such make better-informed decisions. The course covers basic probability, decision analysis, statistical analysis (hypothesis testing and regression analysis), and simulation and provides an introduction to optimization techniques. Probability models provide tools to handle uncertainty and risk. The statistical analysis focuses on the presentation of data and techniques to draw useful and valid inferences from data. Optimization models and decision analysis focus on techniques that use data to inform decision-making.
The course is an introduction to the Python programming language and its applications in business settings. Lectures will be problem-driven and projects will be mostly group-work based. Students will gain hands-on experience with a wide range of business problems. The focus of the course is to learn the basic elements of Python as a foundation for advanced topics such as data analytics. The main purpose is to develop the ability to write programs to solve real-world business problems. In addition to in-classroom time, this course may also meet in computer-based labs for hands-on instructions and implementation.

Core Courses - 6 credits

This course provides an introduction to data mining algorithms. The key objectives: understanding popular predictive and clustering algorithms, ability to implement them in SAS enterprise miner, and ability to interpret the results. Prerequisite: MGMT 67000 Business Analytics or similar statistics courses. Programming background on SAS is not needed.
The Visualization and Persuasion course enhances student professionalism in business contexts by improving oral communication skills. In this special course designed for MS Business Analytics students, you will focus on developing and presenting data-driven messages that are professional, clear, concise, and persuasive. By the end of the course, you will develop your ability to: present yourself professionally in diverse business communication contexts (e.g., presentations, group discussions, informal interactions, etc.; explain data and analyses in ways that are clearly understood by receivers; provide concise explanations that quickly get to the point without losing important context or content; demonstrate mastery at being data-driven by (a) translating data and analyses into a narrative that provides context for your message AND (b) creating informative, clutter‐free data visualizations to support your message; make persuasive recommendations that convince receivers to adopt a particular belief or take a course of action
The internet and other information technologies have reshaped the economic, organizational, cultural and personal landscape. Managers, consultants, and entrepreneurs are all expected to effectively utilize technology to achieve organizational goals. Organizations are now expected to not only adapt to change but also innovate, taking advantage of the benefits of the technology and thrive using their new capabilities. Accordingly, the objective of the course - from the perspective of Information Technology Leadership - is interested in enhancing an organization’s competitive advantage. Specifically, in the course, we will study in detail what the different types of technologies are, how they can be taken advantage of, and what the critical success factors are for optimal implementation of each type. The course will also focus on teaching analytics tools such as SQL, Excel PowerPivot, etc. The course material will be delivered using case-discussions, lectures, and examples.

Business Analytics Electives - 10 credits

Cloud computing and big data technologies are rapidly enhancing an organization's business intelligence ecosystem. The two modules of the course are specially designed for future leaders and data scientists to gain valuable hands-on experience of collecting, cleaning, formatting, integrating, and storing massive amounts of data that may be structured or unstructured, archived, or streaming in a cloud platform. The first module will introduce the fundamentals of cloud computing, its enabling technologies, main building blocks, and hands-on experience through projects utilizing one of the public cloud infrastructures such as Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure. The second module will cover processes for creating data pipelines in the cloud so that students will be able to curate big data for training, analysis, and prediction using AI/ML and other data science techniques.
The internet is now the lifeblood in many individuals’ lives, integrated in their health, work, entertainment, social connections with family and friends, as well as managing in smart devices in their homes and automobiles. While this “everything is connected” environment has impacted many lives in a positive way, it has also opened the doors to exploitation for financial and political gain. In today’s world, security is everyone’s responsibility. In this course you learn information security fundamentals, issues, and terminology that allow you to have informed conversations with security teams, other technical teams and your colleagues. You will learn how to assess risk in order to improve security defenses and reduce vulnerabilities. In addition, you will learn about common security frameworks and the laws and regulations that dictate how organizations implement security controls. Finally, you will learn about security governance and the steps required to create a cyber security strategy for an organization.
With the rise in big data, machine learning has experienced rapid growth over the last ten years with major advances in its subfields of deep learning, reinforcement learning, natural language processing, computer vision, robotics, and other subfields. The purpose of this course is to provide the students with a systematic introduction to the recent developments in deep learning through the coverage of modern machine learning concepts and practical business applications, as well as hands-on experience with modern machine learning frameworks. The course plans to cover neural networks, convolutional neural networks, recurrent networks, deep generative models and deep reinforcement learning.
With an emphasis on foundational database management concepts, this course serves as a comprehensive guide to mastering the art of efficient data utilization. The journey begins with delving into the intricacies of conceptual data modeling, culminating in the creation of precise entity-relationship (ER) diagrams. Subsequently, participants will navigate the realm of logical design, immersing themselves in the intricacies of the relational data model. A pivotal skill set in the modern business landscape, the structured query language (SQL) is explored in detail, equipping participants with the capability to extract actionable insights from complex data repositories.
This is an introductory course in statistical and machine learning. It will cover fundamental concepts and essential tools that are critical in understanding cutting-edge machine learning techniques. Students will develop skills in applying a wide variety of modeling and prediction methods. Topics include linear regression, classification, regularization and shrinkage methods, nonparametric regression, tree-based methods and support vector machines. An integral part of this course is the extensive use of the open-source statistical software R. Students will gain hands-on experience in analyzing datasets commonly used in business and economics.
This course covers essential statistical models and strategic metrics that form the cornerstone of marketing analytics. The objective of the course is to convey the ample benefits of an analytical approach to marketing decision-making and help students to build skills/knowledge/confidence in undertaking such analyses. Students will gain hands-on and computer-based experience with basic and advanced analytical tools to analyze marketing data for addressing marketing decisions that create value and build competitive advantages. A variety of relevant topics are discussed, such as marketing engagements, customer analytics, segmentation, and A/B testing. The final piece of this course is guiding the students on how best to craft data-driven presentations to key stakeholders.
This course introduces different phases of managing projects from conception to termination with particular emphasis on quantitative tools for planning, scheduling, resource allocation, monitoring, and control. In addition, topics such as risk management, communication, and conflict management will be covered. The course will use qualitative frameworks, analytical tools, and real-world cases to achieve the learning goals.
The main goal of this course is to introduce economics students to computation and programming in Python. In the first part of the course, we will cover Python essentials including basic programming techniques and the use of popular packages for data analysis. In the second part of the course, we will consider more advanced programming techniques including numerical methods, dynamic programming, and simulation-based methods. Throughout the course we will consider a number of applications related to microeconomics, macroeconomics, and econometrics covered in the MS Econ program curriculum.

This course provides Daniels School students pursuing business careers an opportunity to apply the knowledge learned from their studies in an applicable area. This course is designed to polish and integrate the knowledge, skills, and abilities you have developed from your coursework by successfully developing a solution with an industry partner in a structured fashion. Thus, you will devote most of your time to working with your project teammates to provide the answers and deliverables specified by the partner over the semester.  Your project team will be given a project description. In some cases, a data dictionary and access to datasets may be provided by the company (e.g., in a data analytics project or an HR project). Whereas, in other cases, the data may have to be searched for from external sources (e.g., market research, or strategic fore-sighting exercises). By the end of this course, you will have developed and presented a solution to a challenging industry problem in an agile fashion.

This is a second course in machine learning. After studying the fundamental concepts and essential machine learning tools in Machine Learning I, this follow-up course will go over a range of more advanced topics and learning methods. Topics include support vector machines, deep learning and neural networks, principal components analysis and other unsupervised learning methods. As in the first course, we will be using the statistical software R and its packages extensively to implement various learning methods. 

After completing this course, students will be able to:

  1. Explain how common learning methods are implemented
  2. Train a range of deep learning models
  3. Perform dimension reduction techniques to summarize high-dimensional data in an efficient manner
  4. Select appropriate models and learning methods for a particular application

Prerequisites: The prerequisite is ECON576 Statistical and Machine Learning. Students should also have a good working knowledge of programming (e.g., R, Matlab or Python). Students without any prior programming experience are strongly encouraged to go through the preparation materials for R programming thoroughly posted on Brightspace.

The main goal of this course is to extend the computational and programming toolkit developed in Quantitative Economics with Python. In particular, we will cover advanced methods for working with, visualizing and analyzing data in Python. In addition, we will consider more advanced programming techniques including stochastic optimization and infinite-horizon dynamic programming. Throughout the course we will consider a number of applications related to microeconomics, macroeconomics, and econometrics covered in the MS Econ program curriculum.

In the past eighteen years, Excel spreadsheets have become the standard tool that business people use to model and analyze quantitative problems. The latest versions of these spreadsheet packages contain powerful analytical tools that could be possible only with mainframe computers and mathematically trained personnel more than a decade ago. This course covers up-to-date and practical spreadsheet modeling and simulation tools that can be applied to a wide variety of business problems in finance, marketing, and operations. The topical coverage mainly consists of the following four modules: (1) deterministic and stochastic optimization techniques to determine the best managerial actions under internally- and/or externally-imposed constraints; (2) probability distribution fitting techniques to find the most likely description of the uncertainty in future business; (3) simulation modeling techniques to discover and analyze the risk and uncertainty in business environment and processes; (4) application of spreadsheet modeling and simulation techniques in forecasting asset dynamics (stock price) and pricing options and real investment opportunities. This course provides hands-on experience of computer applications using Microsoft Excel and the spreadsheet add-ins @RISK, RISKOptimizer, SimQuick, etc.

Supply Chain Analytics focuses on data-driven and rigorous decision making in supply chain management, which is a complete problem solving and decision-making process that integrates a broad set of analytical methodologies that enables the creation of business value.

This course exposes students to RStudio and the R programming language as tools for data analytics. Students will develop a small portfolio of projects that demonstrate fundamental knowledge of programming, study reproducibility, data reshaping, exploratory data analysis, data visualization, and basic predictive modeling techniques such as regularization and shrinkage using R.

This industry-agnostic course is focused on training leaders to be able to talk to and manage the people who are collecting data and are making inferences from the data, and then make data-driven decisions. It will cover tools to collect, manipulate, and analyze data from the web and other sources, with the objective of making students data-savvy and comfortable with deriving insights from real-world, large datasets. Students will be exposed to the power of clickstream analysis and the possibilities that can be unleashed from testing and experimentation. The emphasis of the course will be on data savviness and practical usefulness.

General Business Electives - 5 credits

This course is an introduction to Financial Management. As such, the course addresses the two basic financial problems that all companies face: (1) On what should funds be spent (i.e., investment decisions)? and (2) From where should funds be obtained (i.e., financing decisions)? Specific topics include financial statement analysis, financial planning, stock and bond valuation, project analysis (i.e., capital budgeting), estimating the cost of capital, understanding capital structure, and estimating firm value. Readings, case analyses, and problem sets focus on the basic tools used by financial analysts and financial decision-makers.

The purpose of this course is to provide you with essential tools and concepts you need to help create and sustain needed change in your personal and/or professional life, your work teams and your organizations. The course is taught in an executive-style format intended for working future managers. Emphasis is placed on knowledge application and experiential learning.

This course is an introduction to Financial Management, approached from the view of a general manager. The objective of the course is to provide you with the conceptual and practical framework necessary to evaluate the financial impact of operating decisions. Readings, case analysis, and problem sets focus on the basic tools used by financial analysts and financial decision makers. The course is devoted to the two basic financial problems that all companies face: (1) On what should funds be spent (i.e., investment decisions)? and (2) From where should funds be obtained (i.e., financing decisions)? In this course, we consider such topics as financial statement analysis, financial planning, stock and bond valuation, project analysis (i.e., capital budgeting), estimating and using the cost of capital in practice, understanding the differences among financing alternatives, understanding financing decisions, and estimating the value of an operating business.

As goods and services are produced and distributed, they move through a set of inter-related operations or processes in order to match supply with demand. The design of these operations for strategic advantage, investment in improving their efficiency and effectiveness, and controlling these operations to meet performance objectives is the domain of Operations Management. The primary objective of this course is to provide an overview of this important functional area of business.

Leadership is essential to the realization of organizational goals. Successful leaders can inspire and enliven followers and influence them to pursue particular courses of action. This course focuses on leadership from a managerial perspective. Participants will explore leadership models, learn about current research findings, investigate examples of leadership in practice, and engage in developmental activities to evaluate and enhance their leadership skills.

This course examines how the US economy functions and develops a theoretical framework permitting an analysis of the forces affecting national income, employment, unemployment, interest rates, and the rate of inflation. Emphasis is placed upon the role of government fiscal and monetary policy in promoting economic growth and stable prices. Throughout the semester we will discuss both the theory and the empirical evidence on aggregate macroeconomic measures; consumption, savings, and investment; labor supply and demand; employment and unemployment; economic growth; money and banking; and macroeconomic stabilization techniques.

The course “Managerial Economics and Business Strategy” studies optimal decision-making processes made by managers and policy makers within firms and other organizations. The course is based on microeconomics and applies advanced microeconomics related concepts with an emphasis on pricing strategies. The course covers topics such as monopolies (price discrimination, optimal pricing strategies), advanced strategic decision-making and imperfect competition (optimal pricing and production), new product introductions, product variety offered on the market, and entry deterrence strategies.

The objective of this course is to familiarize students with the methods and frameworks necessary to execute strategic plans in a marketing context. Marketing managers must be able to properly identify the needs of their given consumer base and design strategic plans to align the different dimensions of the marketing mix, such as pricing, promotional campaigns, product characteristics, and the necessary distribution channels, while taking into consideration the offerings of the competitors. To this end, we offer an immersive course, which leverages both lectures and case discussions, to enhance the thought process and presentation of hallmark marketing frameworks.

This course covers microeconomic concepts relevant to managerial decision making. Topics may include demand and supply analysis; consumer demand theory; production theory; price discrimination; perfect competition; partial equilibrium welfare analysis; externalities and public goods; risk aversion and risk sharing; hidden information and market signaling; moral hazard and incentives; rudimentary game theory; oligopoly; reputation and credibility; and transaction cost economics.

Negotiations permeate every aspect of our lives both inside and outside companies. We negotiate with everyone from our spouses to potential employers, yet studies show this vast experience does not translate into aptitude. In other words, most of us simply are not good at negotiating, though we use the skill constantly. Despite popular opinion to the contrary, good negotiators are made, not born. In a course based on experiential learning using cases from Kellogg School of Management's Dispute Resolution Center, students will learn tools and techniques for maximizing their outcomes in both business and personal negotiations, an invaluable skill in today's globally competitive marketplace.

Individual and group behavior are the central components of the study of behavior in organizations. The focus of the course is on the managerial application of knowledge to issues such as motivation, group processes, leadership, organizational design structure, and others. The course employs cases, exercises, discussions, and lectures.

Strategic Management is concerned with understanding how organizations might achieve an advantage relative to competitors. In particular, it deals with the organization, management, and strategic positioning of the firm so as to gain long-term competitive advantage. To address this issue, we take on the role of general managers, or integrators – that is, managers who make decisions that cut across the functional and product boundaries of a firm. By focusing on what makes managers effective, we will develop the ability to evaluate different situations and give you usable skills regardless of the business context in which you want to work. Strategic management issues that we will consider include the following: How can my firm create value (e.g., low cost or differentiation; using resources; integrating activities correctly) relative to the competition? How do other players in the industry impact the amount of value I capture from my activities? How can the firm identify new opportunities for value creation and value capture and implement those activities within the firm? How can a corporation create (rather than destroy) economic value through its multimarket activities? What options are available to a firm to successfully diversify?

In addition to the traditional 2 or 3 credit general business courses, there are also "Essentials of" courses offered for many of these same courses, but in a condensed 4-week period. All “Essentials of” courses are 1 credit hour and cannot be used in conjunction with a 2 or 3 credit course that has already been completed (for example, you cannot use both MGMT 60000 - Accounting for Managers and MGMT 69000 - Essentials of Accounting towards fulfilling program credit hour requirements).

Additional Electives - 4 credits

Students may choose elective courses to suit their individual interests. They may use as additional electives any MGMT, ECON or OBHR courses or credits that they have NOT used for filling other requirements.

Plan of Study

The Online MSBA program at the Daniels School of Business helps students accelerate their careers with critical expertise to draw insights from the data on which organizations build successful strategies. You’ll apply management techniques, problem solving, programming skills, and the latest technologies to solve case studies, complete projects and learn to make informed decisions that translate across industries and organizations. With a wide variety of electives, you’ll acquire the skills you need to further your career and reach your aspirations.

If you’re considering similar residential or non-degree program offerings, compare the curriculum of our Online MS in Business Analytics (MSBA) degree with that of our MS in Business Analytics and Information Management (MSBAIM) residential program or explore our Online Business Analytics Graduate Certificate.

Explore the MSBAIM program Explore the Graduate Certificate

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