In an era when data-driven decisions and systems influence every sector of business and society, talented professionals who bring an ethical framework to data science are more in demand than ever. The online M.S. in Data Science program empowers you to apply technical methods, employ an ethical lens, and utilize relevant management skills to address the needs of organizations and communities, preparing both experienced professionals and recent college graduates for rewarding careers in one of the world’s fastest-growing fields.
Our program
Our online M.S. in Data Science program provides interactive programming by offering small class sizes that incorporate discussions, group projects, and case studies.
Free Python Programming Boot Camp for M.S. Data Science Students
This self-paced boot camp provides approximately 35 hours of comprehensive instruction and engaging assignments, priming you with essential Python programming skills to be successful in your degree program. For those without Python experience, it is recommended that you complete the optional boot camp before commencing your program.
The field of data science is rapidly expanding and requires professionals who can take a distinctive and empathetic approach to it. The online M.S. in Data Science program emphasizes the importance of ethical and responsible handling of data. Our program underscores the significance of prioritizing people and society in all analytical and decision-making processes. Upon completion of this program, you will be equipped with the knowledge and skills to transform data science for the betterment of society.
In this program, we draw from the rich strengths of Boston College's Jesuit background to integrate a focus on ethics into every aspect of what we do, and into every single one of our courses. Some examples of what this looks like include:
You'll engage with this human-centered approach and our core value of ethics throughout our program in a number of ways, including:
This program will enable you to:
Connect with us!
Talk with our team to learn about the program, the application process, and how we prepare to make you an ethical data scientist.
17,700
projected job openings in the U.S. each
year through 2032
Bureau of Labor Statistics
#4
in Best Technology Jobs
U.S. News & World Report
$108,020
median annual pay for data scientists
Bureau of Labor Statistics
During this program, you will:
combine synchronous and asynchronous learning with an online part-time program
build core skills in areas ranging from machine learning to statistical analysis and data visualization
participate in an interdisciplinary speaker series
complete a six-credit capstone project, earning a total of 36 credits
Course | Course Title | Credit |
---|---|---|
MESA8410 | Introduction to Human-Centered Data Science In this course, students will be introduced to the technical, social, and ethical considerations in the field of data science, including data security, governance, and privacy. Particular focus will be placed on data scientists’ responsibility to create effective and inclusive solutions that are responsive to the needs, values, and perspectives of people. This course will also introduce the themes and skills that will be developed in subsequent courses. Specifically, students will: learn about trends and advances in data science (e.g., A.I. and cloud computing); be exposed to data cleaning procedures for various types of commonly used data; learn about project lifecycle planning and execution; and will learn about the steps in a typical data science project (e.g., question framing, data collection, cleaning, exploration, modeling, interpretation of findings). Moreover, instruction will focus on developing students’ skills relating to project management, problem framing, communication, and project execution. | 3 |
MESA8412 | Applied Mathematics for Data Science This course will provide students with a strong foundation in mathematical tools relevant to data science. Selected topics from calculus, vector spaces, matrix algebra, numerical optimization, and probability theory will be covered. These tools will help students understand and solve data science problems and work with emerging methods and techniques in this rapidly growing field. A refresher in calculus will provide a foundation in mathematics necessary to understand data science concepts. Topics will include matrix algebra and vector spaces, which are essential for understanding mathematical models and statistical methods used in data science, and numerical mathematics and optimization, which are integral for understanding model training and efficiency. Additionally, skills in this area can help detect overfitting by providing a way to assess the quality of a model’s fit to data. Finally, instructional units on basic probability theory will lay the foundation for the subsequent classes on statistical models for data science. | 3 |
MESA8411 | Programming for Data Science This class will introduce essential programming concepts, data structures, and techniques focusing on data science applications. Students will learn what it means to write high-quality code. Additionally, topics such as testing, debugging, and exception handling will be taught. By the end of the course, students will have reviewed Python programming and be able to apply their knowledge to various data science problems. Topics include functions, recursion, loops, list comprehensions, elementary data structures, and reading and writing files. | 3 |
MESA8430 | General Linear Models This course addresses the construction, interpretation, and application of general linear statistical models. Students will learn data and sampling distributions, A/B testing, significance testing, simple and multiple regression, power analysis, parameter estimation, partial associations, regression diagnostics, missing data analysis, remedial analysis, model misspecification, exploratory versus confirmatory model building, categorical data coding, dummy-variable regression, mediation, moderation, polynomial models, prediction, cross-validation, and regularization including ridge regression and Lasso regression. By applying methods learned in the class to real data, students will learn to be responsible and careful data scientists. Students will learn to be conscientious about data sources, paying particular attention to individuals and populations that may be systematically excluded. | 3 |
MESA8413 | Database Systems and Data Preparation This course will provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, and computational complexity. Students will be introduced to relational databases focusing on learning SQL with the Postgres database. Topics include schemas, indexes, query efficiency, server-specific navigation functions, and queries with grouping, ordering, sorting, collapsing, and joins. Distributed data storage and processing techniques for parallel processing (MapReduce) and their implementation (Hadoop) will be covered, as well as strategies for accessing unstructured data and handling streaming data. | 3 |
MESA8440 | Multivariate Statistical Analysis Modern statistics and data science often deal with high-dimensional data, and multivariate methods are used to handle these types of data. Approaches to supervised and unsupervised learning with multivariate data will be discussed. This course will cover exploratory data analysis, binary logistic regression, multinomial logistic regression, ordinal logistic regression, discriminant analysis, support vector machine (SVM), K-Nearest Neighbors (KNN), Multivariate Analysis of Variance (MANOVA), principal component analysis, exploratory factor analysis, structural equation modeling, cluster analysis. For each topic, theory and applied analysis will be covered, and students will use the statistical packages R and SPSS to calculate and report multivariate statistics. For many issues in public life, statistics and its application is a critical scientific approach to understanding how actions and policies affect people’s lives. Good policies advance social justice, and policy debates should be informed by responsible practitioners, reliable data sources, and statistical data analysis results. In this course, students will learn to use reasonable and appropriate methods to handle data analyses and to pay attention to data sources, outliers, and quality analytics. | 3 |
MESA8417 | Data Visualization and Storytelling This course will introduce the principles for guiding the development of compelling stories based on data and how to use visualizations to assist in articulating key findings. Visualization and storytelling are important tasks in the lifecycle of a data science project and essential skills to communicate effectively. Data visualization allows data scientists to take complex data and present it in a way that is easy to understand, while storytelling allows them to add context to their findings and explain their importance. Students will learn various data visualization techniques for reporting purposes and the widely used software tools for implementing these visualizations. Students will also learn how to create interactive reports based on dashboards deployed on cloud servers to maximize client engagement. | 3 |
MESA8414 | Applied AI and Machine Learning This class will provide a broad overview of various approaches to machine learning, including supervised and unsupervised learning. Students will learn about the fundamental algorithms used to train computers to learn. The course will also expose students to different application areas where data-driven decision-making is aided by machine learning (e.g., text classification, image recognition, and predictive modeling). Students will use the Python programming language and machine learning libraries (e.g., scikit-learn) to solve authentic problems. While working with authentic datasets, students will also learn about bias, accountability, and trust issues that arise when conducting human-centered data science. | 3 |
MESA8415 | Deep Learning and AI This course will introduce deep neural networks. Students will learn foundational concepts such as stochastic gradient descent and backpropagation and how they are used to train network models. Students will also gain practical experience using Python libraries (e.g., TensorFlow, Keras) to implement various deep learning architectures. Different types of artificial neural network architectures will be covered so that students will learn about classes of applications with an eye toward human-centered data science in this class. These include convolutional neural networks, recurrent neural networks, and long short-term memory networks. Students will learn how to choose different architectures depending on the tasks being addressed. | 3 |
MESA8418 & MESA8419 | Applied Data Science Capstone I & II In Capstones 1 and 2, students will complete a significant data science research project that requires executing learned skills in problem framing, problem-solving, and visualization. While working with a faculty member and internal and/or external partners, students will be responsible for identifying questions to investigate, data sources, and applying analytical techniques. Students will prepare a written report of their results and deliver a presentation to faculty and partners. The project synthesizes the statistical and computational challenges of solving complex real-world problems with social issues. The course activities focus on a two-semester-length data science project supervised by a faculty member and in collaboration with Boston College and/or external partners. At the conclusion of these capstone projects, students will have accumulated hands-on experience working with clients, applying methods, and developing materials and presentations that can be shared with others. | 6 |
MESA0000 | Natural Language Processing This class on natural language processing (NLP) will cover issues such as topic modeling, text summarization and classification, sentiment analysis, large language models, and automatic scoring of a student’s written response, including essay scoring. NLP provides essential methods for dealing with large amounts of text data created when sourcing information from the web and other large text corpora. Practical exercises in this class will help improve student awareness around fairness, bias, and human-centered data science by providing students with opportunities to learn and apply methods for dealing with large amounts of real-world text data. The techniques taught in this class will help to improve the accuracy and fairness of predictions made by machine learning models and can also help to improve the interpretability of those models. | 3 |
Course | Course Title | Credit |
---|---|---|
MESA8100 | Master's Comprehensive Exam In order to ensure that all students graduating from the master's program have a fundamental understanding of the field which they are about to enter, they are required to complete a comprehensive examination covering the broad areas of the core courses. | 0 |
35%
projected growth in data science jobs, 2022–2032
Bureau of Labor Statistics
A core part of Boston College’s recent $300 million investment in the sciences, the Schiller Institute fosters human-centered, transdisciplinary research on pressing issues related to energy, environment, and health. The institute will help facilitate the M.S. in Data Science seminar series and students’ capstone projects, providing opportunities for interdisciplinary collaboration.
The Lynch School of Education and Human Development provides more than $11.4 million in financial aid to students each year. As a result, the quality of BC’s instruction, the benefit of our alumni network, and the impact a BC degree will have on your employment options is both affordable and invaluable.
In a data-driven world, our data science program provides a purpose-driven compass by empowering graduates to harness data to drive positive change. Our program teaches solid technical skills while also instilling a sense of ethical responsibility in data scientists. We equip our students with the tools to reshape industries, address diverse challenges, and strive for a better society
A non-refundable application fee of $75 is currently waived.
Deadlines Fall 2025:
Prerequisite Information:
Applicants must have taken college-level Calculus 1 and Statistics 1 and received passing grades. Familiarity with a programming language and coursework in advanced calculus and statistics are preferred but not required.
Highly-qualified applicants who have not yet met this criteria may be conditionally admitted with a requirement that the courses be completed before the program begins.
To be uploaded to your online application.
In 1,000-1,500 words, describe your academic and professional goals, any technical experience related to this program, and your future plans, expectations, and aspirations.
Two letters of recommendation are required, with at least one preferably coming from an academic source. Applicants who have been out of school for more than a couple of years may submit two professional recommendations. Applicants may submit one additional recommendation of their choice.
Transcripts from all college/university study are required.
Applicants who have received degrees from institutions outside the United States should view the "International Students" section for additional credential evaluation requirements.
Please begin your online application before submitting your transcripts. Details on how to submit transcripts and international credential evaluations can be found within the application.
In order to ensure your transcript reaches our office, it is important to review and follow the instructions.
Submitting GRE test scores are not required for this program for the 2024 entry term(s). If you wish to send GRE scores, the Lynch School GRE code is 3218.
Please view the "International Students" section for information on English Proficiency test requirements.
To be uploaded to your online application.
In addition to your academic history and relevant volunteer and/or work experience, please include any programming or other relevant technical skills or experience, any language skills other than English, and any research experience or publications.
Fully online programs* do not support sponsorship of an F1 visa for International Students.
Applicants who have completed a degree outside of the United States must have a course-by-course evaluation of their transcript(s) completed by an evaluation company approved by the National Association of Credential Evaluation Services (NACES). Submission of falsified documents is grounds for denial of admission or dismissal from the University.
Applicants who are not native speakers of English and who have not received a degree from an institution where English is the primary language of instruction must also submit a TOEFL or IELTS test result that meets the minimum score requirement.
Please click the link below for full details on these requirements.
Requirements for International Students
*The M.S. in Data Science is a fully-online program.
gsoeonline@bc.edu
617-552-4214