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When you earn a Master of Science in Applied Analytics degree from Boston College, you join a worldwide network of over 200,000 alumni. Program leadership and faculty have collaborated with an advisory board of industry leaders to develop an academic curriculum that’s aligned with employer demands. This degree is truly designed for leaders.

Why Boston College’s M.S. in Applied Analytics Program?

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Master of Science in Applied Analytics


Ten Courses are Required to Complete the Master of Science in Applied Analytics

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.

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.

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.

This course aims to prepare students to understand the data engineering required for data science research projects and industry products.

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.

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.

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.

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.

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.

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.

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.

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.

This course introduces students to computer vision concepts and methods. Students will learn how to conduct classification, detection, and recognition tasks. The course covers 1) the basics of computer vision, 2) machine learning (ML) models for vision, 3) Convolutional Neural Networks (CNN) and transformer architecture, 4) object detection and image segmentation, 5) autoencoders & image manipulation, 6) Generative Adversarial Networks for image creation, and 7) multi-input models.

This course introduces students to natural language processing (NLP) concepts and methods. Students will learn how to conduct both supervised and unsupervised NLP. The course covers 1) the basics of NLP, 2) text (document) classification, 3) text summarization, 4) text similarity & clustering, 5) semantic analysis, 6) sentiment analysis, and 7) deep learning approaches (Recurrent Neural Networks and transformer-based architecture.

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.

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.

Learning Outcomes

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 their skill sets.

Specific knowledge domains under each competency are provided below:


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


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

Soft Skills

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