Our Curriculum

Built for Industry

What does industry need? The Tepper School of Business developed the curriculum for the online Master of Science in Business Analytics (MSBA) program from the ground up with this question in mind. In consultation with global business leaders, they determined that the greatest need is for professionals who not only have advanced analytical skills, such as machine learning and optimization, but also the appropriate business knowledge and communication skills to solve complex problems and bring value to industry.

Built for Today

Our students develop proficiency in the full range of state-of-the-art business analytics techniques; they also learn how to tell stories through and extract insights from data. Given the Tepper School’s view of a curriculum as an organic entity, our faculty continually work in concert to ensure that courses harmonize, even as they are individually updated and modified to ensure learning outcomes for students are always in step with an ever-evolving industry.

Built for You and Your Cohort

The flexible online format enables students to continue working while earning their degree and apply what they learn in the classroom to their work environment.

The cohort structure helps ensure that students make meaningful personal and professional connections with each other and develop the strong communication, collaboration and virtual team skills that will help them succeed in today’s increasingly interconnected and global business environments.


Learning Outcomes

Upon completing the program, students will be able to:

  1. Demonstrate a depth of knowledge of quantitative and analytical tools for decision making
  2. Make appropriate judgements regarding managing, manipulating and analyzing large data sets
  3. Develop and/or efficiently apply computer software to implement analytical techniques
  4. Identify the potential and challenges of applying data analytics in a business environment
  5. Communicate effectively and persuasively

Structure and Schedule

The online MS in Business Analytics can be completed in 18 months. It comprises eight to 10 terms, also called minis. A mini is the equivalent of a half-semester. The program begins with two foundation courses and culminates with a Capstone project that spans two minis. After completing the foundation courses, students take two courses per mini-semester in lockstep with their cohort. The first two minis are six weeks. All other minis are seven weeks. To graduate, students must complete 108 units of foundation, core and elective MSBA courses.

Please review the Summer 2018 and Fall 2018 academic calendars for more detailed information.

Sample an Online Course

To preview one of our courses, contact an admissions counselor toll-free at 888-876-8959, at 412-238-1101 or onlinemsba-admissions@andrew.cmu.edu.


Course Map

Year One
Year Two
Mini 1 and/or Mini 2 Foundation Courses (12 Units)
Introduction to Probability and Statistics
Programming in R & Python

Foundation courses can be taken consecutively, one per term, or simultaneously during either Mini 1 or Mini 2.

On-site

Mini 3 (12 Units)
Business Fundamentals
Statistical Foundations of Business Analytics

Mini 4 (12 Units)
Modern Data Management
Data Exploration and Visualization

Break

Mini 5 (12 Units)
Machine Learning for Business Applications 1
Optimization for Prescriptive Analytics

Break

Mini 6 (12 Units)
Machine Learning for Business Applications 2
Business Communication

Mini 7 (12 Units)
Business Value Through Integrative Analytics
Managing Teams and Organizations

Mini 8 (12 Units)
Elective 1
Elective 2

Mini 9 (12 Units)
Capstone
Elective 3

Mini 10 (12 Units)
Capstone
Elective 4

Back to Course Map

FOUNDATION COURSES

For more information about these courses, visit the Foundation Courses page.

46-880 Introduction to Probability and Statistics (6 units)

This course introduces tools for decision making under uncertainty, ranging from the fundamentals of probability theory, decision theory and statistical models to basic software for data analysis. Topics include statistical independence, conditional probability, Bayes theorem, discrete and continuous distributions, expectation and variance, decision trees, sampling and sampling distributions, interval estimation, correlation and linear regression.

46-881 Programming in R and Python (6 units)

This course provides an introduction to programming in Python and R, two of the most popular languages for data analytics. The course starts out by covering basic programming concepts, such as data types and inputting/outputting data from/to the user. We then cover basic control flow, including conditionals and loops. Finally, we will cover functions, program design, basic algorithms, and data structures. Most of the course materials will be introduced through Python and the final lectures will help students to port this knowledge to R.


CORE COURSES

Courses typically are divided into weekly or topical modules. Students begin each course with skills development. They progress into problem solving through working on specific business applications and typically use real data to gain business insights.

46-882 Business Fundamentals for Analytics Professionals (6 units)

This course provides a basic introduction on general business management. Topics include organizational structure and the role of different business domains, including accounting, finance, operations and marketing, and how they relate to each other in an organization.

46-883 Statistical Foundations of Business Analytics (6 units)

The objective of this course is to help students learn to analyze data and use methods of statistical inference in making business decisions. This course focuses on application of fundamental concepts from probability and statistics to drawing inferences from data. Topics will include Bayesian modeling, multivariate analysis, causal inference, A/B testing and experimental design, with special emphasis on diagnostics and model building techniques appropriate to the study of real-world data. Assignments with applications to real-world data are an integral part of the course.

46-884 Modern Data Management (6 units)

The focus of this course is on managing and retrieving data of all types (structured, semi structured or unstructured), from both technical and business perspectives. The course topics include relational data management systems, theory of databases and models (CAP, ACID, distributed computing and storage), document (MongoDB), and other models for big data. The course also provides a basic conceptual introduction to Hadoop, Map-reduce, Hive, Apache Spark (in general, the big data architecture).

46-885 Data Exploration and Visualization (6 units)

This course provides an introduction to the principles and techniques for data visualization. Students will learn visual representation methods and techniques that increase the understanding of complex data and models. Principles will be drawn from statistics, graphic design, cognitive psychology, information design, communications and data mining. Specific topics covered include design principles for charts and graphs and common visualization tools (Tableau, Google Visualization API, Python), effective presentations, dashboard design and web-based visualizations.

46-886 Machine Learning for Business Applications 1 (6 units)

In this course, students explore common machine learning techniques and think about the application of these techniques to both structured and unstructured datasets found in business. Specific topics include linear regression (logistics regression, k-nearest neighbors and SVMs) and unsupervised learning (principal components and clustering methods: hierarchical, partitioning and probabilistic).

46-887 Machine Learning for Business Applications 2 (6 units)

This course continues the introduction of machine learning techniques with an emphasis on business applications. Specific topics include model and variable selection (overfitting and overconfidence; bias-variance tradeoffs; information criterions and cross-validation; model averaging and ensemble learning; feature selection; regularization, shrinkage and LASSO Estimators); nonlinear prediction methods (tree-based methods: decision trees and random forests; regression splines; kernel methods and Gaussian processes); modeling with latent variables (hidden Markov models and graphical models).

46-888 Optimization for Prescriptive Analytics (6 units)

Mathematical optimization technology is key to turning data into better decision making. The application of large-scale optimization models can bring a critical competitive advantage to many firms. This course focuses on developing such optimization models for operational and strategic decision making, with applications that include vehicle routing, employee scheduling, network design and capacity planning. Methodologies include linear programming, integer programming, nonlinear programming, constraint programming, heuristics, and column generation.

46-889 Business Value Through Integrative Analytics (6 units)

This is an integrative course that charts the path to value from analytical modeling for business problems. Building on a set of multidisciplinary cases cutting across functional areas of business, this course integrates the three increasing levels of analytics involved in reaping business value from data in a given problem: the descriptive phase of inferring key features and relationships in the problem; the predictive phase of forecasting outcomes of short-term tactical actions; and the prescriptive phase of long-term planning based on analytics.

46-890 Managing Teams and Organizations (6 units)

This course introduces students to both the micro and macro perspectives of organizational behavior and theory. At the micro level, it covers factors for working in and managing an effective work team, including building teams, team contracting, team coordination and team creativity. Macro topics include team networks, informal and formal organizational networks, communication networks and innovation culture.

46-897 Business Communication for Analytical Decision Making (6 units)

This course helps students communicate with a range of business audiences including superior, peer and subordinate. Students learn delivery skills; how to construct arguments and problem-solve for decision makers; and how to understand what these audiences need from them. Students are evaluated on presentation assignments that are relevant to business analytics.

46-899 Capstone (12 units)

The Capstone course is a semester-long project course that is carried out in an actual business analytics context, e.g., analytical marketing, operations, finance or human resources analytics. Students are asked to manage a large data set, develop appropriate quantitative models and analytical insights, interact with the company, and deliver midterm and final presentations to company executives and faculty.


ELECTIVE COURSES

46-891 Mining Unstructured Data (6 units)

Consumers leave footprints in the form of vast amounts of data regarding their thoughts, beliefs, experiences and even interactions on social media and other web 2.0 platforms. The application of unstructured data mining tools, such as text mining, can help firms derive high quality information from text and bring a critical competitive advantage. This course focuses on tools for analyzing unstructured (text) data for strategic decision making, with applications that include white space identification, understanding customer needs and perceptions of a brand, identifying market structure through user generated content, and demand forecasting with social media or customer review data.

46-892 Data Analytics in Finance (6 units)

The course will address several areas of finance that rely heavily on data analytics, including 1) High frequency trading and market micro-structure, 2) Quantitative portfolios and asset management, 3) “Smart” beta and performance analysis, and 4) Credit analysis. The class uses tools from statistics, data mining (machine learning) and natural language processing/text-mining.

46-893 Operations and Supply Chain Analytics (6 units)

Operations and supply chain analytics is concerned with the development and application to data of business analytics tools to support high-impact strategic, tactical, and operational decisions within both manufacturing and service firms. Topics include supply chain design, demand forecasting, inventory planning, sales and operational planning, revenue management, staffing in service organizations, and healthcare management. The underlying feature in these applications is managing the risk that arises from supply and demand mismatches with the goal of maximizing enterprise value. The course emphasizes how sophisticated and holistic implementation of the operations and supply chain analytics toolbox, integrating descriptive, predictive and prescriptive analytics techniques, can be an essential lever to increase or sustain a firm’s competitive advantage.

46-894 Analytical Marketing (6 units)

Marketing has become much more quantitative and data intensive in recent years. Strategies like interactive marketing, customer relationship management and database marketing push companies to utilize the information they collect about their customers to make better marketing decisions. Marketing transaction data — which is a common type of big data — often forms the core set of information used for making marketing decisions. This course focuses on how analytical techniques from data mining, machine learning and statistical modeling can be applied to solve marketing problems, using a series of data intensive case studies. Specifically, the case studies considered include pricing decision support systems using retail transaction data, understanding customer churn in the cell-phone market, upgrading freemium customers to paying customers, and lifetime cycles in direct marketing.

46-895 Exploring Causality in Depth (6 units)

This course covers methods of causal analysis for both experimental and non‐experimental data. In addition, it highlights the way in which machine learning tools have enhanced established methods for tackling these problems. Possible course topics include 1) Evaluating experiments with non‐compliance and sample attrition, 2) Applications of instrumental variable (IV) methods in non‐experimental settings, 3) Self‐selection, 4) Models with fixed effects and panel data methods, 5) Regression discontinuity methods and 6) Analysis of duration models.

46-896 People Analytics (6 units)

This course covers how to apply analytical techniques to understand and address human resources challenges. Specific topics include hiring retention and attrition, developing talent culture (e.g., promoting innovation) and organizational change. The analysis will be carried out using statistical models including logistic regressions, coarsened exact modeling, mixed models (RE and FE), multiple membership models, and event history analysis.

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