Learn Differently

Master of Science in Applied Analytics

Applied Means…

Breaking through in the Board RoomUncovering Insights that Drive ActionCultivating Skills that Sustain Relevance

Breaking through in the Board Room.

Uncovering Insights that Drive Action.

Cultivating Skills that Sustain Relevance.

Whether you are an established professional who wants to elevate your career in a changing world, or a recent undergrad ready to maximize your career opportunities in today’s data-driven workplace, the Boston College Master of Science in Applied Analytics will position you to drive strategy and effect change across organizations and industries.

Upcoming Start Dates

Start Term Class Start Date
Summer 2023
May 17, 2023
Fall 2023 August 28, 2023
Spring 2024
January 2024

On Campus or Online

Choose the learning format that works best for your learning and lifestyle

Flexible Scheduling

Study full- or part-time online, on campus during the evening, or any combination that works for you.

Broad-based or Specialized

Prepare for multi-industry career opportunities or take a deeper dive into a specific focus area

Where it Can Take You

Enhance your credentials, expand your network, and accelerate your career with an MS in Applied Analytics from Boston College and become part of an engaged and passionate alumni network of more than 180,000 worldwide

How many courses?

10 courses—two foundational, four core courses, three electives, plus an AI practicum. Students can complete the degree in as little as 12 months of full-time study or 20 months of part-time study.

How much does it cost?

$1,298 per credit Academic Year 2021 – 2022 tuition rate; the total cost of the MS in Applied Analytics degree is $38,940.
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Source: EMSI/Burning Glass

Curriculum

10 Courses

30 Credits

Program of Study

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. We also envision developing assessments that would allow students to waive these courses.

Foundational Courses Description
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.

Core Courses Description
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.

Applied Analytics Project

All students will benefit from the Applied Analytics Project, where they would obtain end-to-end experience in building an analytical solution to a business or policy problem.

Choose at least three electives:

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 will be designed to help students practice their skills in the context of business areas such as product management or communication. For example, students, especially those who matriculate with a background that allows them to waive Foundational Courses, can also take electives in another graduate program in a domain of their interest such as healthcare, HR, Cyber Security, etc. This 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 / Econometric 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.
Advanced AI algorithms
Data Visualization and Communication
Machine Learning Product Management This course aims 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.
Operations Research
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.

After completing the program, students will be able to:

  • Design analytic approaches to solve complex problems
  • Understand and deploy advanced analytic techniques in search of actionable insights
  • Use machine learning and artificial intelligence tools and approaches to leverage data for business and policy decisions
  • Draw insights from analytics and communicate them clearly to non-technical audiences
  • Drive real impact based on results and insights from analytics

Learners who complete the MS 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 are provided below:

Data

Business Analysis
Business Intelligence
Data Analysis
Data Management
Data Modeling
Data Visualization
Machine Learning
Statistics
Data Privacy

Business

Agile Methodology
Business Intelligence
Business Process
Business Requirements
Business Systems
Customer Service
Finance
Process Improvement
Project Management
Business Advisory

Soft Skills

Business Analysis
Business Intelligence
Data Analysis
Data Management
Data Modeling
Data Visualization
Machine Learning
Statistics
Data Privacy

Data

Communication
Influencing
Leadership
Management
Planning
Presentations
Problem Solving
Research
Storytelling
Stakeholder Management

What you’ll learn in our
M.S. in Applied Analytics Program

  • Design analytic approaches to solve complex problems
  • Understand and deploy advanced analytic techniques in search of actionable insights
  • Use machine learning and artificial intelligence tools and approaches to leverage data for business and policy decisions
  • Draw insights from analytics and communicate them clearly to non-technical audiences
  • Drive real impact based on results and insights from analytics

Diving Deeper

“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.”

Ra’ad Siraj, MassMutual Head of AI Governance

MSAA Program Director

Aleksandar (Sasha) Tomic

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.