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Online MS Business Analytics Curriculum

We will prepare you for the current and future landscape of business data by exposing you to various functional areas of business and how they use information. Our Online MSBA program focuses equally on both technologies and techniques, and puts you to work on real industry projects centered on problems in business analytics derived either from outside organizations or Purdue University.

Your Coursework

To earn a Master of Science degree in Business Analytics (MSBA), you must complete 30 hours of coursework in the following areas:
  • Business Analytics (3 credits)
    Data analysis and modeling are important skills for effective managerial decision making in business and industry. Advances in technology (computers, scanners, cell phones) have made a significant amount of data available to managers. 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 the 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 salesforce. 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.

  • Python Programming (2 credits)
    The course is an introduction to the Python programming language and its applications in business settings. Lectures will be problem-driven and 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.


  • Data Mining (2 credits)
    Simon (1977) stated that managerial decision-making is synonymous with the entire process of management. In order to make intelligent decisions, one must have access to data and information. Today’s electronically networked world provides a nearly infinite number of opportunities for data collection. The issue thus becomes: How does one approach these large quantities of data with the purpose of intelligent decision-making? The purpose of this course is to introduce the concepts, techniques, tools, and applications of data mining. The material is approached from the perspective of a business analyst, with an emphasis on supporting tactical and strategic decisions.

  • Visualization and Persuasion (2 credits)
    The Communication 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

  • IT for Innovations (2 credits)
    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 the technology to achieve the organizational goals. Organizations are now expected to not just adapt to technology changes, but also innovate taking advantage of the benefits of the technology and thrive using their new capabilities. Accordingly, the objective of the course is from the perspective of the Information Technology Leadership interested in enhancing the 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 successful 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 by using case-discussions, lectures, and examples.
  • Spreadsheet Modeling and Simulation (2 credits)
    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.

  • Big Data and Cloud Computing for Future Leaders (2 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.

  • Web Data Analytics (2 credits)
    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.


  • Using R for Analytics (2 credits)
    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.


  • Machine Learning and Big Data (2 credits)
    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, nonparamteric 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.

  • Industry Practicum (Credits vary)
    Students will work on real industry projects focusing on problems in business analytics derived either from their organizations or those provided by Purdue.

  • Data Engineering I (1 credit)
    Basic data manipulation; review of Python; introduction to Unix scripts; data cleaning; dealing with missing data; summarizing data.

  • Linear Algebra for Data Science (1 credit)
    Fundamentals of linear algebra for data science and applications. Includes matrix operations; eigenvalues and diagonalization, orthogonality and least squares. Basic knowledge of vectors is a prerequisite.

  • Numerical Computing (1 credit)
    Numerical modeling of data, applications and methods of linear systems & eigenvalues on networks, massive matrix methods for data analysis; numerical optimization (Pre-requisite - Linear Algebra)

  • Accounting for Managers (3 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.

  • Financial Management (3 credits)
    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.

  • Marketing Management (3 credits)
    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.

  • Intro to Operations Management (3 credits)
    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.

  • Strategic Management (3 credits)
    Strategic Management is concerned with understanding how organizations might achieve 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 shall 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?

  • Microeconomics (2 credits)
    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.

  • Organizational Behavior (2 credits)
    Individual and group behavior are the central components of the study of behavior in organizations. Focus 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.

For free electives, students may choose elective courses to suit their individual interests. They may use as free electives any MGMT, ECON or OBHR courses or credits that they have NOT used for filling other requirements. Courses taken from HR/OB Selectives in excess of 6 hours will count as free electives.