MBA/MS in Applied Data Science
Earn a high-powered joint degree at the intersection of business and technology.
As businesses of all kinds increasingly rely on data to make decisions, an MBA and Applied Data Science degree equips you to bridge the gap between tech and management and provide effective leadership in data-centric environments.
Balancing theoretical rigor with practical application, our MBA for data scientists leverages the resources of Chicago Booth, the Data Science Institute, and the Physical Sciences Division to bring you a cutting-edge education in two of today’s most dynamic fields. A combination of online and in-person courses gives you flexibility in course scheduling, and you’ll earn two degrees in the time it would take to complete the MBA alone.
As a graduate of our MBA/MS in Applied Data Science joint degree program, you’ll be ready to lead in diverse fields ranging from A.I. research and machine learning to business analytics and product marketing.
To learn more, watch this brief information session with an admissions director.
Program Structure
As a student in the joint-degree MBA and Applied Data Science program, you’ll take the equivalent of 23 100-unit courses:
- 14 MBA classes
- 9 data science courses
- Leadership Effectiveness and Development (LEAD)
- Qualified Work Experience, a noncredit professional internship experience
Your Booth courses will be in person, while your MS courses will be online. Most students will earn both degrees in seven quarters—the same time it takes to earn the MBA.
Course Progression
Applied Data Science
- Pre-Quarter Foundational Course*: Introduction to Statistical Analysis
- Pre-Quarter Foundational Course*: R for Data Science
Booth
- 200 units
- LEAD
Applied Data Science
- Statistical Models for Data Science
- Foundational Courses*: Python for Data Science (first half) and Advanced Linear Algebra (second half)
Booth
- 100 units
Applied Data Science
- Machine Learning I
- 100 units of Electives+
Booth
- 200 units
Applied Data Science
- Machine Learning II
- Data Engineering Platforms for Analytics
Booth
- Internship
Applied Data Science
- Time Series Analysis and Forecasting
- 100 units of Electives+
Booth
- 300 units
Applied Data Science
- 100 units of Electives+
Booth
- 300 units
Applied Data Science
- Capstone Experience
Booth
- 300 units
* = Optional
+ = Students may substitute one MS elective for a Booth course
Career Paths
As data science continues to transform the way we live and work, a strong understanding of both business and analytics will set you apart in the job market. And as a student in our joint degree MBA and Applied Data Science program, you’ll have access to personal career coaching, leadership development, and on-campus recruiting with some of the world’s top employers.
Our recent MBA and MS graduates are working at high-profile companies around the world, including Apple, Google, Nike, Cardinal Health, and IBM, in a wide range of rewarding roles. For example:
- Data Scientist
- Data Engineer
- Data Science Analyst
- Senior Data Science Analyst
- Machine Learning (ML) Scientist
- A.I./ML Specialist
- Senior Data Science Product Manager
- Data Science Consultant
- A.I. Research Scientist
- A.I. and Advanced Analytics Specialist
- Director, Product Marketing
- Senior Health Data Science Manager
- Financial Data Science Director
- A.I. and Data Science Governance Manager
- Business Intelligence/Data Visualization Developer
- A.I./ML Solution Architect
Tuition
Tuition for Full-Time MBA students pursuing the Joint MBA/MS Program will be assessed through Chicago Booth at a flat rate for six quarters of enrollment. Pricing will differ from Booth’s standard Full-Time MBA Program, and final tuition amounts are made available in the spring. A student who takes more than 2300 units will be assessed for any additional units.
MS in Applied Data Science Courses
Statistical Models for Data Science
In a traditional linear model, the observed response follows a normal distribution, and the expected response value is a linear combination of the predictors. Since Carl Friedrich Gauss (1777-1855) and Adrien-Marie Legendre (1752-1833) created this linear model framework in the early 1800s, the “Linear Normal” assumption has been the norm in statistics/data science for almost two centuries. New methods based on probability distributions other than Gaussian appeared only in the second half of the twentieth century. These methods allowed working with variables that span a broader variety of domains and probability distributions. Besides, methods for the analysis of general associations were developed that are different from the Pearson correlation.
Machine Learning I
This course is aimed at providing students an introduction to machine learning with data mining techniques and algorithms. It gives a rigorous methodological foundation in analytical and software tools to successfully undertake projects in Data Science. Students are exposed to concepts of exploratory analyses for uncovering and detecting patterns in multivariate data, hypothesizing and detecting relationships among variables, conducting confirmatory analyses, and building models for predictive and descriptive purposes. It will present predictive modeling in the context of balancing predictive and descriptive accuracies.
Machine Learning II
The objective of this course is three-folds – first, to extend student understanding of predictive modeling with machine learning concepts and methodologies from Machine Learning 1 into the realm of Deep Learning and Generative AI. Second, to develop the ability to apply those concepts and methodologies to diverse practical applications, evaluate the results and recommend the next best action. Third, to discuss and understand state-of-the machine learning and deep learning research and development and their applications.
Data Engineering Platforms for Analytics
Data Engineering Platforms teaches effective data engineering—an essential first step in building an analytics-driven competitive advantage in the market.
Time Series Analysis and Forecasting
Time Series Analysis is a science as well as the art of making rational predictions based on previous records. It is widely used in various fields in today’s business settings.
Course availability varies each quarter, with sample electives including the following:
- Advanced Computer Vision with Deep Learning
- Advanced Machine Learning and Artificial Intelligence
- Bayesian Methods
- Data Science for Algorithmic Marketing
- Data Visualization Techniques
- Digital Marketing Analytics in Theory and Practice
- Financial Analytics
- Health Analytics
- Machine Learning Operations
- Natural Language Processing and Cognitive Computing
- Reinforcement Learning
- Supply Chain Optimization
To maximize curricular flexibility, you may substitute one of the three required MS electives for a Booth elective.
In this course, you’ll complete a research-focused capstone project designed specifically for this joint degree. In a small group, you will collaborate with a real client on a relevant data science project across multiple sectors such as finance, entertainment, automotive, and more.
Booth Courses
This course is required of all Full-Time MBA students and is completed during the Autumn Quarter of their first year of residency in the program. The course is designed to enhance students’ self-awareness and interpersonal effectiveness by providing them with an opportunity to benchmark themselves with respect to critical aspects of leadership—working in teams, influencing others, conflict management, interpersonal communication, presentation skills, etc. The course also helps students create a personalized plan to guide their continued development at Booth and beyond.
- Basic Courses: Financial Accounting
- Advanced Alternatives: Accounting & Financial Analysis 1, Accounting & Financial Analysis II, Financial Statement Analysis
- Basic Courses: Microeconomics, Advanced Microeconomic Analysis, Accelerated Microeconomics
- Advanced Alternatives: Topics in Microeconomics Theory
- Basic Courses: Business Statistics, Applied Regression Analysis
- Advanced Alternatives: Analysis of Financial Time Series; Financial Econometrics; Statistical Insight into Marketing, Consulting, and Entrepreneurship; Data Mining; or any PhD-level statistics
Students are encouraged to complete Applied Regression Analysis to complete the Statistics foundation requirement. However, it is not required. Students who do not take 41100 can waive the Statistics foundation requirement and, in its place, take 100 additional units of Booth electives.
- Functions, Leadership & Management, and the Business Environment: 700 course units
- Electives: 400 course units (or 500 if waiving the Statistics foundation requirement)
Admissions Process
Applicants interested in the Joint MBA/MS degree will apply through Booth’s centralized, joint-application process. Applicants should complete the Chicago Booth Full-Time MBA application and select the MBA/MS in Applied Data Science as their program of interest. An MBA/MS program supplement will be available for completion within your Booth application. The supplement contains Applied Data Science specific questions that will be reviewed by the Applied Data Science admissions team along with your full Booth application. For complete consideration, applicants must complete the MBA application and the joint degree program supplement in the same application round prior to submitting the application.
As part of the online application, candidates will be required to submit a GMAT or GRE score for the joint program. International applicants may be required to submit proof of English language proficiency by submitting a TOEFL iBT or IELTS test score. The minimum TOEFL iBT score required for admission is 104; the minimum IELTS score required is 7.
Eligibility requirements: Students who wish to do the joint degree with Applied Data Science must be admitted to and begin the joint programs in the same academic year. Students who have already started their MS or MBA are not eligible for the joint degree.
Frequently Asked Questions about the MBA/MS in Applied Data Science Joint Degree Program
The fields of Statistics, Mathematics, and Computer Science intersect with industry domains in different ways. The MPCS program focuses on the center of Computer Science, including Software Engineering, High Performance Computing, Data Analytics, and Application Development. The MS-ADS Program focuses at the intersection of multiple fields, such as Computer Science, Mathematics, and Statistics (including Statistical Inference, Linear/Non-Linear Models, Machine Learning, Natural Language Processing, and Deep Learning). The outcomes for MPCS students include Software Engineer (Developer), Senior Software Engineering Management, Software/Hardware Architect, and Senior Cyber Security Engineer. The outcomes for students in MS-ADS include roles as Data Scientist (most common), Senior Data Science Consultant, Business Intelligence (BI) Director, Data Visualization Manager, Data Analytics Engineer, and AI Solution Architect.
The expectations are similar in terms of STEM aptitude, including undergraduate exposure to Mathematics, Statistics, and Computer Science and experience in internships/industry. Specific requirements differ, with the MS-ADS Program requiring programming skills in specific languages used in Data Science (Python and R). Statistics and Linear Algebra as it applies to Machine Learning are required for MS-ADS. There are enough differences that the background required to prepare for the program is specified within each program’s supplemental requirements page in the Booth application.
Upon acceptance into the program, you will be invited to complete online Foundational Skill Assessments about 2 months before Autumn quarter begins. These required assessments help you understand what, if any, content areas you wish to refresh your knowledge for a strong start in program coursework. The assessments also help Applied Data Science faculty and academic advisors understand how to best support you once you begin the program. All foundational courses are five weeks long and held virtually. These courses will not appear on your official university transcript and will not be assessed for a letter grade or pass/fail notation. If you wish to shore up your knowledge in the foundational areas outside of the Foundational courses, Applied Data Science can recommend resources.