To start your program in the fall, apply by July 1, 2021.
Through a partnership with Raytheon Technologies, the Kelley School of Business offers you the opportunity to earn a degree from our top-ranked, online Master of Science in Business Analytics.
The Raytheon MS in Business Analytics program will give you in-depth knowledge on business analytics topics, plus material designed specifically to help you thrive at Raytheon and grow within the company.
Best of all? All of your coursework will be taught online, giving you maximum control of life outside of work.
The Kelley School is one of the top-ranked business schools in the country. As such, the curriculum for our RTX Program is rigorous: It matches, in breadth and depth, the score of our standard online and on-campus MBA program.
All courses will be geared to help RTX employees meet the specific challenges it will face as a successful global company in the future.
Your online MS in Business Analytics program will:
Be completed in approximately 1.25 years
Total 30 credit hours
Consist of 2 classes (6 credit hours) per quarter; 4 quarters per year
Total 5 quarters
Be earned completely online
You’ll learn from the same highly ranked faculty that teach in the residential program at the Kelley School of Business. As a student, you will:
Make business decisions based on analytic modeling
Think strategically in management situations
Unlock valuable statistical information from any dataset
Develop analytical models to provide solutions across multiple business functional areas
Plan of Study
Launch the next stage of your career in less than two years
Predictive Analysis/Data Mining
Data Warehousing and Visualization
Simulation and Optimization for Business Analytics
Data Predictive Analytics and Business Strategy
Developing Value through BA Applications
Thinking Strategically: Game Theory and Business Strategy
Define and explain the business analytics process (problem definition; data preparation; technical analysis and modeling; evaluation of results; implementation and deployment)
Understand and describe the functionality and role of analytic techniques in data mining and predictive analytics.
Perform basic and advanced analytics tasks with JMP and Excel
Construct, validate, and interpret data mining and predictive analytics models using large multivariate data sets
Apply data mining and predictive analytic techniques to problems in areas such
Perform data exploration to evaluate variables for data mining and to suggest and implement approaches to handle data problems such as missing values, outliers, and skewed distributions
Compute and interpret key predictive accuracy measures and methods including lift charts, gain charts, and ROC curves
In this course, we enhance the your statistical and mathematical modeling skills covering the following topics:
Probabilistic decision making
Simulation with @@RISK
Optimization modeling with the EXCEL Solver
Making decisions when multiple objectives are involved
Using neural networks to improve forecasting.
Applications from all major functional areas will be discussed.
In this course, you will:
Understand and utilize unsupervised models including principal components analysis, cluster analysis, and market basket analysis. You will explore the following business intelligence-related concepts:
The notion of corporate information factory
Dashboards and scorecards that support the businesses
Different types of problems related to data quality including a methodology for maintaining data quality
An overview of a variety of software tools that are employed in the development of a data warehouse: ETL (extraction, transformation and loading) and analytic tools
Recognize some of the key issues for managerial considerations, including:
The tools of metrics in decision-making
Types of business risks along with how to alleviate the risks associated with implementing informational systems
Issues related to data governance
In this course, you’ll survey the management of operations in manufacturing and service firms. Students will work through diverse activities, such as:
Determining the size and type of production process
Purchasing the appropriate raw materials
Planning and scheduling the flow of materials and the nature and content of inventories
Assuring product quality
Deciding on production hardware and how it gets used
You’ll also learn that managing operations will require both strategic and tactical skills. The topics considered include:
Quality and productivity
The course makes considerable use of business cases. Most classes will be spent discussing the cases assigned. For each case, students will be asked to review actual company situations and apply technical and managerial skills to recommending courses of action. Most cases will be taken from manufacturing, but some will be service-oriented. Several of the cases will focus on international companies or challenges.
In this course, you will focus on the second main component of business analytics: analytic capabilities. You will:
Develop analytical models using simulation and optimization to analyze and recommend sound solutions to complex business problems
Develop models to provide solutions to operational problems in various business functional areas including finance, economics, operations, and marketing
Solve complex problems using various tools on spreadsheets; including Excel solver for linear and integer programming problems, @@RISK for probabilistic simulations, and risk analysis
Undertake input and output statistical analysis for simulation models
Solve complex optimization problems using the ILOG-CPLEX package
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. Additionally, you will develop presentation and communication skills, particularly with regard to quantitative outputs. Finally, you will learn the basics of identification in order to better understand which data is most useful to collect when answering a given empirical question.
In this course, you will focus decision-making capabilities in business analytics.
Understand how various analytical techniques and tools are used to analyze complex business problems and derive business value for applications
Apply analytical techniques to various retail and marketing problems; including determining customer value and using the concept to aid in business decision making by allocating marketing expenditures between customer acquisition and customer retentions (Marketing Analytics)
Apply EXCEL financial functions (XIRR, XNPV, FV, PV, PMT, CUMPRINC, and CUMIPMT) and develop models to solve financial problems on spreadsheets (Financial Analytics)
Develop inventory models under uncertainty including service level and reorder point models for supply chains (Supply Chain Analytics)
Deploy analytics, such as aggregate planning models in production planning and scheduling (Operations Analytics)
Use Data Envelopment Analysis (DEA) in solving various healthcare problems (Healthcare Analytics)
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. Game theory is a tool to use to examine these interactions.
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.
Here’s your introduction to the process of creating a market-driven organization. Specific course topics will include marketing strategy, market research and analysis, and the development of products and services, pricing, distribution and promotion. The course employs lectures, classroom discussion through threaded discussion forums, case analysis, and field research projects.