Leverage the power of data analysis, machine learning, and artificial intelligence with a specialized master’s degree in one of today’s most in-demand fields. Taught by experienced professionals, this STEM-designated program prepares you to apply advanced analytical approaches to solve real-world problems in almost any industry. The M.S. degree can also be completed as a dual degree with Woods College’s M.S. in Applied Economics.
Our faculty have considerable industry experience directly related to the course they teach so that you can practice what you learn and understand how your skills apply in the given context.
Our active advisory board consists of leaders in the field. They help shape our curriculum, mentor and help you with your job search process, as well as provide placement opportunities.
We limit the class size in both our online and in-person offerings to foster faculty engagement with students.
We provide you with strong academic advising and support. In addition, we provide career coaching and organize recruiting events where we bring students together with recruiters and hiring managers.
In addition to teaching you in-demand skills, we infuse the curriculum with communication and non-technical skills necessary for you to succeed outside of the classroom.
In the project course, you will deliver an AI project from start to finish, practicing not just technical tools but also project management, advisory, presentation, and communication skills.
“The BC MSAA is a world-class program spanning the breadth and depth needed for practitioners and leaders in today's marketplace—from hands-on skills development to responsible and ethical governance. Boston College's and the Woods College's reputation will make this a marquee, sought-after program.”
These courses establish the necessary background for further study in the field. Students who have taken comparable courses in their undergraduate program can waive these courses and take electives instead.
|Mathematical Methods for Machine Learning I||Machine learning is the design of algorithms that routinely learn and adapt with use to discover hidden properties, patterns, and trends in complex data. This is a semester course on foundational methods in linear algebra and vector calculus to understand the structure and dimensionality of large and complex datasets.|
|Data Analysis||This course is designed to introduce students to the concepts and data-based tools of statistical analysis commonly employed in Applied Economics. In addition to learning the basics of statistical and data analysis, students will learn to use the statistical software package Stata to conduct various empirical analyses. Our focus will be on learning to do statistical analysis, not just on learning statistics. The ultimate goal of this course is to prepare students well for ADEC 7320.01, Econometrics.
These courses allow students to develop the competencies necessary to be able to conduct analytical work and apply it in the real world. Core courses bring students to the necessary proficiency level and enable them to either further hone their analytic skills or to further focus on the application of the tools in different settings. All students must take the core courses, including the project course.
|AI/ML Software Tools and Platforms||This course aims to prepare students to understand the data engineering required for data science research projects and industry products.
|AI Algorithms I / Big Data Econometrics||This course demonstrates how to merge economic data analysis and applied econometric tools with the most common machine learning techniques, as the rapid advancement of computational methods provides unprecedented opportunities for understanding "big data." This course will provide a hands-on experience with the terminology, technology and methodologies behind machine learning with economic applications in marketing, finance, healthcare, and other areas. The main topics covered in this course include: advanced regression techniques, resampling methods, model selection and regularization, classification models (logistic regression, Naive Bayes, discriminant analysis, k-nearest neighbors, neural networks), tree-based methods, support vector machines, and unsupervised learning (principal components analysis and clustering). Students will apply both supervised and unsupervised machine learning techniques to solve various economics-related problems with real-world data sets. No prior experience with R or Python is necessary.|
|AI Algorithms II||This course aims to teach students advanced AI algorithms and covers neural networks, deep learning architectures, and reinforcement learning. The course provides a high-level theoretical overview of each section and discusses practical applications through hands-on projects. The course uses Python as the programming language. Prerequisites: Data analysis and feature engineering, traditional machine learning theory and practice, python programming (intermediate level - e.g., familiarity with sci-kit learn, matplotlib, NumPy, pandas), linear algebra, and first-order derivatives.|
|Algorithmic Ethics and Governance - from traditional to AI/ML||This is a survey course of governance frameworks & techniques for algorithms that are used to make decisions within an organization or in servicing clients. The recent acceleration in the use of Artificial Intelligence (AI) and specifically Machine Learning (ML) techniques have introduced unique opportunities and risks that require governance to encourage their responsible and ethical use. We will start with the intent of governance, its roots, its current manifestations and identify trends that are shaping algorithmic decision-making governance with a focus on for-profit firms, mainly the US. Industries covered will vary but may include the Financial Industry, Healthcare, Manufacturing, Defense, and Biotech for illustrative examples.|
All students must complete the Applied Analytics project where they will obtain end-to-end experience in building and analytical solution to a business or a policy problem.
Students will use electives to customize their learning to fit their objectives. Some electives within the program focus on more advanced topics, both in Mathematics and Analytics, geared toward the students that want to explore the material on a more theoretical level and/or better prepare for further graduate study. Other electives are designed to help students practice their skills in the context of business areas such as product management or communication. Students who matriculate with a background that allows them to waive Foundational Courses, can also take electives in another graduate program at Woods College of Advancing Studies in a domain of their interest such as healthcare, HR, Cyber Security, etc. which would provide them with exposure to an area of interest where they can explore how their skills would be used in the given industry.
|Electives (Choose at least three courses)||Description|
|Regression Models/Econometrics||This course focuses on the application of statistical tools used to estimate economic relationships. The course begins with a discussion of the linear regression model and examination of common problems encountered when applying this approach, including serial correlation, heteroscedasticity, and multicollinearity. Models with lagged variables are considered, as is estimation with instrumental variables, two-stage least squares, models with limited dependent variables, and basic time-series techniques.|
|Machine Learning Product Management||This course will to prepare students to develop product solutions that deliver user value and provide viability for the business in the technology space that is heavily using Machine Learning.|
|Predictive Analytics/Forecasting||This course will expose students to the most popular forecasting techniques used in industry. We will cover time series data manipulation and feature creation, including working with transactional and hierarchical time series data as well as methods of evaluating forecasting models. We will cover basic univariate Smoothing and Decomposition forecasting methods, including Moving Averages, ARIMA, Holt-Winters, Unobserved Components Models, and various filtering methods (Hedrick-Prescott, Kalman Filter). Time permitting, we will also extend our models to multivariate modeling options such as Vector Autoregressive Models (VAR). We will also discuss forecasting with hierarchical data and the unique challenges that hierarchical reconciliation creates. The course will use the R programming language though no prior experience with R is required.|
|Mathematical Methods for Machine Learning II||Machine learning is the design of algorithms that routinely learn and adapt with use to discover hidden properties, patterns, and trends in complex data. This is a semester course on foundational methods, probability theory, and statistical methods, focusing on data classification and pattern recognition, formulating and testing hypotheses, and statistical forecasting of trends in data that highlight potential tradeoffs and decision options by stakeholders. Topics include discrete and continuous random variables, the algebra of random variables, independence, central limit theorems, Gaussian distributions (univariate and multivariate forms). Topics in statistics focus on regression theory and hypothesis testing.|
|Software Tools for Data Analysis||The course provides students with an overview of popular software packages used today for data exploration, analysis and visualization. The first part of the course will offer an overview of the non-programming tools Excel and Tableau. In Excel we will cover basic charts with the emphasis on their use with pivot tables. In Tableau students will be introduced to more advanced data exploration and visualization methods via a variety of advanced charts and dashboards. The second part of the course will cover exploratory data analysis in R. Here students will learn how to write their own code for importing, cleaning and exploring large datasets, as well as how to create, modify and export complex charts and summaries for visual, qualitative and quantitative analysis of the data. The third part of the course will provide an intro to using SQL databases, where students will learn how to create SQL queries to select, filter and manipulate the data.|
|Operations Research||This course provides an introduction to the use of operations research methods in business. For this purpose, the course starts with a brief review of the basics from calculus and linear algebra, which is followed by the conceptual foundations of economic modeling and the applications of optimization techniques on various economic problems. The course provides a very sound perspective on how to use operations research techniques in any kind of economic and managerial decision making, which has become an increasingly sought after skill. We will work on various problems, including portfolio management, resource management, environment and energy related regulations, etc.|
|Data Visualization and Communication||Working with data to obtain the results is just a first step in effective modeling. Once the results are obtained they need to be transformed into insights and communicated to, often, a non-technical audience. In this course students will learn and practice techniquest of visualizing data, presenting insights and effectively communicating with a variety of audiences, including very non-technical ones.|
A dual M.S. in Applied Economics / M.S. in Applied Analytics degree provides students who are interested in deepening their expertise in both Economics and Analytics an opportunity to obtain a dual Master's degree by completing 15 courses.
After completing the program, students will be able to:
Students who complete the M.S. in Applied Analytics will develop a rich, applied skillset in four broad competency areas: Data, Technology, Business, and Soft Skills. This diverse competency base will provide the foundation for data-driven decision making at any level of the organization, from business analyst roles to division leaders to C-level executives seeking to broaden skillsets. Specific knowledge domains under each competency include:
As director of the M.S. in Applied Analytics and associate dean for strategy, innovation, and technology, Dr. Tomic draws from his background as an accomplished economist, researcher, and thought leader.
|Can Erbil, Ph.D.||Boston College; Professor of Practice in Economics|
|Esin Sile, Ph.D.||College Find Me; Board Member|
|Eugene Wen, Ph.D.||Manulife Financial; Vice President, Group Advanced Analytics|
|Fr. Richard McGowan, D.B.A.||Jesuit East Coast Province; Treasurer / Boston College, Faculty|
|John Arbadjis, Ph.D.||BNY Mellon; Head of Macro Strategy Product & Analytics|
|John Griffin, Ph.D.||Charles River Associates; Vice President|
|Jose Fillat, Ph.D.||Federal Reserve Bank of Boston; Senior Economist and Policy Advisor|
|Lisa Emsbo-Mattingly, M.A.||Fidelity; Director of Research, Global Asset Allocation|
|Paul Garvey, Ph.D.||MITRE; Chief Scientist, Center for Acquisition and Management Science|
|Ra'ad Siraj, M.S.||MassMutual, Head of AI Governance
|Robert Murphy, Ph.D.||Boston College; Associate Professor of Economics|
|Sasha Tomic, Ph.D. (Chair)||Boston College|
|Stephen Lawrence, Ph.D.||Vanguard; Head of Investment Management Fintech Data Science|
|Mike Finegold, Ph.D.||Google; Data Science Lead, Google Travel|
|Nigel Gault, Ph.D.||EY-Parthenon; Chef Economist|
|Nurtekin Savas, M.S., M.B.A.||Capital One; Head of Machine Learning & Data Science [Enterprise Products + Platforms]|
|Oana Diaconu, Ph.D.||CVS Health; Executive Director, Data Science, Customer Experience and Call Servicing|
|Derya Isler, M.B.A.||Spotify; Product Leader - Personalization|
|Karen Muncaster, Ph.D.||Boston College; Dean of the Woods College of Advancing Studies|
Five-year growth rate in job postings
Source: Emsi/Burning Glass
Median starting salary for candidates with Master's in Analytics
Source: Emsi/Burning Glass
We know that a BC education is a worthwhile but significant investment. We're committed to helping you affordably achieve your educational goals while treating each student and their family equitably. Financial aid and payment plans may be available for students taking a minimum of six credits across a semester.
Applications are accepted on a rolling basis.
Bachelor’s degree from an accredited college/university (minimum GPA 3.0)
Statistics and Calculus I.
Application fee $60 (to be paid as part of the online application)
Applications are accepted on a rolling basis.
|Entrance Term:||Application Due Date:||Decision Letter Sent By:|
|Fall||Early Deadline: May 1||June 1|
|Regular Deadline: June 15
(International Students applying for Fall must submit an application by June 15th.)
|Rolling admissions after Regular deadline.||Applications will be reviewed on a case by case basis.|
|Spring||Early Deadline: October 15
(International Students applying for spring must submit an application by October 15th Applications received after October 15th will be considered for the summer semester.)
|Preferred Deadline: November 15||December 15|
|Applications will be accepted after November 15 and reviewed on a case by case basis, pending availability. Students are encouraged to apply after November 15.|
|Summer||Early Deadline: March 1||April 1|
|Regular Deadline: April 1
(International Students applying for summer I must submit an application by March 1st. Summer 2 applicants must submit an application by April 1st.)
|Rolling admissions after Regular deadline.||Applications will be reviewed on a case by case basis, pending availability.|
There is a $250 enrollment deposit for admitted students wishing to confirm their enrollment. Information about this will be included in your decision letter.
In 550 - 750 words, describe how would a graduate degree in applied analytics assist you in achieving your current and future career goals? What are these goals and how do you envision this particular program will help you toward these ends?
Two letters of recommendation (Letters must be sent directly from recommender either through online application portal or directly to Woods College. We will not accept letters that are not in a sealed envelope mailed directly from recommender or emailed directly from recommender).
Please note: Letters of recommendations should be provided by Professional or Academic recommenders.
Transcripts from each college or university in which you were enrolled are required.
Please mail transcripts to:
Woods College of Advancing Studies Admissions Office
St. Mary's Hall South
140 Commonwealth Avenue
Chestnut Hill, MA 02467
If your academic institution provides electronic transcripts please indicate firstname.lastname@example.org as the recipient.
Boston College Alumni and Current Students:
Your BC transcript must be formally requested from the Office of Student Services and submitted to the Woods College. Woods cannot request or access transcripts independently.
All students who have, or are currently attending, an institution outside of the United States must provide a detailed, course-by-course transcript evaluation indicating the conferral of an undergraduate degree that is equivalent to a U.S. bachelor’s degree from an accredited institution.
This evaluation is not just an English translation, but a document provided by an accredited evaluating agency that shows all grades, course titles, credit hours, United States degree equivalency, grade-point average (GPA), and date of degree conferral.
This detailed, course-by-course transcript evaluation must be submitted to complete the application.
Please request a detailed, course-by-course transcript evaluation for all international institutions where a degree was conferred from one of the following agencies:
Educational Credential Evaluators
Center for Educational Documentation
World Education Services Inc.
Applicants whose Native language is not English are required to demonstrate English language proficiency, for required scores, visit our International Student page.
Interview is optional or may be required by program director.
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