Full-time MS in Business Analytics


Full-Time, STEM-Designated MSBA Program


Welcome to 10 months of business analytics immersion. In the full-time MS in Business Analytics program, you’ll combine strong technical and quantitative training with comprehensive business acumen. You’ll do it with the direction of some of the greatest minds in business: Goizueta professors. In a collaborative, project-driven learning environment, you’ll create innovative solutions to business problems for real-world clients. By the end of the business analytics degree, you’ll see a data problem from every angle and find innovative solutions.
  • Academic Tracks

    Business Analytics & AI in Business
  • Intensive Months

    A Full-time MSBA
  • Months of OPT

    STEM degree (possible 24-month extension of OPT beyond 12 months)

The Intersection of Business, Data, & Technology

Full-time MS in Business Analytics

Program Timeline

Explore the highlights of what 10 months in our full-time Master's in Business Analytics degree look like. We combine market-relevant skills, experiential learning, and professional development to create business data scientists ready to tackle any data-driven problem. Personalize your academic experience with two tracks: Business Analytics or AI in Business.
Full-time MS in Business Analytics
  • Summer Semester

  • Summer Semester

    Boot Camps
    Strengthen your technical and professional skills. Participate in a series of boot camps (Math, Technology, Business, and Business Problems Solving) to refine your skills.
    View MSBA Catalog
  • Summer Semester

    Team Building Skills
    Put your team building skills to the test as you navigate various challenges and activities through the streets of Atlanta.
  • Fall Semester

  • Fall Semester

    Core Courses
    Gain foundational skills and hands-on business analytics experience through Master's in Business Analytics courses designed to help you excel in any industry. This includes business statistics, business analytics, social network analytics, machine learning, data visualization, and designs analytics & optimization.
  • Fall Semester

    Leadership Development
    Learn how to become a better leader and more effective team member. Attend seminars and events related to building effective teams and receive team and personal coaching from a Goizueta MBA Leadership Fellow.
  • Fall Semester

    Finding Your Fit
    Build your network as you engage with the Atlanta Data Science Ecosystem.
  • Spring Semester

  • Spring Semester

    Core Courses
    Gain foundational skills and hands-on business analytics experience through Master's in Business Analytics courses designed to help you excel in any industry. This includes managing big data, machine learning, and a capstone experience.
  • Spring Semester

    Analytics Practicum
    Apply the skills you learned in your MS in Business Analytics coursework and solve sponsored firms’ business problems, using proprietary data. This is your chance to put your skills to the test tackling a real problem for a real client using real data.
    Capstone Project
  • Spring Semester

    Elective Courses
    Complete two MS in Business Analytics elective courses and take a deeper dive into the areas of study and action-based experiences that inspire you most.
  • Spring Semester

    Career Trek
    Travel to the West Coast and witness analytics in action as you engage with tech firms and Goizueta alumni residing in the West Coast.
  • Spring Semester

    After just 10 months, you will graduate Goizueta Business School armed with a robust set of skills and experiences to elevate your career to the next level.

MS in Business Analytics Course Map by Semester

MSBA curriculum structure


This course examines how statistical methods lead to understanding and describing data. We use R/RStudio to review basic data exploration and statistical methods. Topics covered include simple descriptive statistics through hypothesis testing and modeling.

In this course, students gain competence in practical database, data warehousing, and data management skills with emphasis on query, data modeling, ETL, and data management. They will also become familiar with major elements of the big data ecosystem.

We will study the fundamental principles and techniques of data mining in order to extract useful information and knowledge from data. We will improve our ability to approach problems "data-analytically,” examine real-world examples that place data mining in context, and apply data-mining techniques while working hands-on with data mining projects. The course will provide an understanding of the general framework for building and evaluating predictive models, both for classification and numeric prediction data mining tasks. The course will cover supervised predictive modeling techniques as well as unsupervised predictive modeling techniques.

Learn modern network analysis methods and how to apply them to network data. Study the latest in theory, methods, and substantive applications. This course covers the application of network theory to the study of careers, competition, innovation, inequality/stratification, IT-mediated networks, network formation, and network dynamics.

This is an introduction to basic concepts in machine learning, both supervised and unsupervised. The basic ideas of neural networks will be presented. Machine learning from data streams (online learning) using stochastic gradient descent is also covered.

Where the art of graphic design meets with the science of data analytics. Learn how to perform exploratory analysis through visualization, how to create professional-looking visualizations for use in business reports and presentations, and how to design interactive visualizations and dashboards.

Delve into a number of selected current and emerging data analytics areas that are increasingly important for organizations. Areas include advanced elements of the predictive modeling process, ensemble methods, cost-aware data analytics, mining text and data, recommender systems, and other advanced topics.

This course introduces students to optimization and simulation, powerful techniques for better decision-making (in particular, linear, integer and non-linear programming), and Monte Carlo and discrete event simulation, and discuss their application to problems in business and data analytics.

Deep learning models aim to recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions, and reinforcement learning methods focus on learning how to map situations to actions, so as to maximize a reward. ISOM 678 introduces some of the key ideas and techniques developed from the machine learning communities on deep learning and reinforcement learning, with an emphasize on the practical understanding and implementations of these techniques in real world business problems. In this course, algorithms and concepts are presented to build intuition for how different methods work, without advanced mathematics. Topics include single- and multi- layer networks, convolutional and recurrent neural networks, multi-armed bandits, finite Markov decision processes and on-policy and off-policy data methods. Programming is central to the course and is based on the R and Python programming languages.

The course introduces modern data-driven supply chain management techniques. Specific focus will be on supply chain resiliency and efficiency. The topics include deep-tier supply network visibility, supply risk identification, demand and lost sales estimation, data-driven sales and operations planning, and inventory management. We will use graph-theoretic, simulation, econometric, and operations research methods relevant to supply chain management.

The MSBA Analytics Practicum leverages skills and techniques learned throughout the course of the program and applies them to real-world business situations. Students formally define problems, clean data, aggregate with other data sources, and identify and use appropriate analytical techniques to address questions.

This course uses analytic methods to solve marketing problems (including pricing, customer relationship management and sales forecasting). We emphasize the development of tools that can be implemented in practice to support decision making and rely on frequentist and Bayesian methodologies.

This course focuses on tools and techniques applicable to decision making in sports. The analytical foundations for the class are classical statistical and optimization techniques. We solve sports problems using tools such as linear regression, logistic regression, Markov chains, and optimization.

This course introduces students to statistical and computational methods used in employee recruitment, selection, promotion and turnover. A significant portion of the course is devoted to experimental methods designed to test the efficacy of HR policies before an organization implements them.

Real Data. Real Clients. Real World.

MSBA Capstone team
Master's in Business Analytics

Business Analytics Practicum

Learn by doing. That’s the philosophy that drives our MS in Business Analytics program. You’ll work within a small team mentored by faculty to apply everything you learned in the program to devise a data-driven solution for a real client business problem. Projects conclude with an industry day where you’ll present the analysis and findings of your work to the firms that have shared their data.

The Goizueta Difference

Goizueta Experts