Online MS in Applied Data Analytics, AI & Machine Learning

At a Glance

Completion Time

8–20

Months

Credits Required

32–40

Tuition

$27,204–$36,215

Part-Time*

*Based on 2025–2026 Boston University tuition and fees. Merit scholarship may reduce cost.

Master Intelligent Data Analytics and Innovative AI Technologies

The MS in Applied Data Analytics concentration in AI & Machine Learning provides an in-depth study of cutting-edge topics including neural networks, generative AI, automated reasoning, AI security, intelligent image processing, reinforcement learning, and ethical considerations in artificial intelligence. Graduates will be well-prepared to design and deploy intelligent applications across engineering, business, and industry sectors.

Through BU MET’s AI & Machine Learning master’s concentration, you’ll hone the skills necessary to create powerful predictive models and leverage machine learning to inform strategic business decisions. This expertise opens doors to a variety of high-demand roles such as data scientist, economist, business intelligence analyst, systems analyst, chief analytics officer, analytics manager, marketing analyst, business analyst, and financial analyst.

The Applied Data Analytics concentration in AI & Machine Learning emphasizes practical experience with advanced AI models, algorithms, and methodologies, preparing you for innovative roles that go beyond automation to foster creativity and breakthrough solutions. According to a recent LinkedIn report, companies adopting AI are expanding their workforces, utilizing AI as a tool for innovation rather than mere process simplification. The rise of emerging areas like generative AI and reinforcement learning underscores the growing demand for specialized knowledge in these domains.

The AI & Machine Learning track balances foundational data analytics with applied skills in databases, machine learning techniques, and large-scale data processing. This comprehensive program trains you to organize, cleanse, analyze, and visualize vast datasets using the latest industry tools and approaches, equipping you for the evolving data-driven landscape.

The MS in Applied Data Analytics, AI & Machine Learning concentration is also available on campus in Boston. Learn more.

Curriculum

A total of ten courses (40 units) is required. Students exempted from the foundation courses will complete a total of eight courses (32 units).

Students must complete recommended prerequisites along with the foundation courses (unless exempted), core courses, and AI & Machine Learning concentration requirements.

Prerequisites

Foundation Courses

(Two courses/8 units)

Qualified students may be exempt from one or both foundation courses based on previous academic background in information technology, computer science, and mathematics. Applicants will be notified of their curriculum requirements upon admission. If foundation courses are assigned, they must be completed within the first semester of study.

MET CS 521 Information Structures with Python

4 credits. Fall and Spring

Creativity/Innovation
Critical Thinking
Quantitative Reasoning II

This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking. Prerequisite: Programming experience in any language. Or Instructor's consent.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Lu	Independent	M	06:00:00 PM–08:45:00 PM	KCB 102

Section A2, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Mohan	Independent	T	06:00:00 PM–08:45:00 PM	PHO 202

Section O1, FALL 2025 Sep 2nd to Oct 20th
Instructor	Type	Days	Times	Location
Pinsky	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, FALL 2025 Oct 28th to Dec 15th
Instructor	Type	Days	Times	Location
Bond	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 526 Data Structures and Algorithms

4 credits. Fall and Spring

This course covers and relates fundamental components of programs. Students use various data structures to solve computational problems, and implement data structures using a high-level programming language. Algorithms are created, decomposed, and expressed as pseudocode. The running time of various algorithms and their computational complexity are analyzed. Prerequisite: MET CS300 and either MET CS520 or MET CS521, or instructor's consent.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Mellor	Independent	M	06:00:00 PM–08:45:00 PM	CGS 527

Section O1, FALL 2025 Sep 2nd to Oct 20th
Instructor	Type	Days	Times	Location
Doucette	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, FALL 2025 Oct 28th to Dec 15th
Instructor	Type	Days	Times	Location
Burstein	Independent	ARR	12:00:00 AM–12:00:00 AM

Core Courses

(Four courses / 16 units)

MET CS 555 Foundations of Machine Learning

4 credits. Fall and Spring

Formerly titled CS 555 Data Analysis and Visualization with R. This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: METCS 544 or equivalent knowledge, or instructor's consent.

Section A3, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	M	02:30:00 PM–05:15:00 PM	MET 122

Section A4, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	W	06:00:00 PM–08:45:00 PM	KCB 104

Section O2, FALL 2025 Oct 28th to Dec 15th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 577 Data Science with Python

4 credits.

Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Independent	M	06:00:00 PM–08:45:00 PM	CAS 226

Section A2, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Mohan	Independent	R	06:00:00 PM–08:45:00 PM	MET 101

Section A4, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Independent	T	09:00:00 AM–11:45:00 AM	MET 101

Section O2, FALL 2025 Oct 28th to Dec 15th
Instructor	Type	Days	Times	Location
Mohan	Independent	ARR	12:00:00 AM–12:00:00 AM

Plus one course from the following:

MET CS 544 Foundations of Analytics and Data Visualization

4 credits. Fall and Spring

Formerly titled CS 544 Foundations of Analytics with R. The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: METCS 546 and (METCS 520 or METCS 521), or equivalent knowledge, or instructor's consent.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Kalathur	Independent	M	06:00:00 PM–08:45:00 PM	CAS 203

Section A2, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Diwania	Independent	T	06:00:00 PM–08:45:00 PM	CAS 233

Section O1, FALL 2025 Sep 2nd to Oct 20th
Instructor	Type	Days	Times	Location
Kalathur	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, FALL 2025 Oct 28th to Dec 15th
Instructor	Type	Days	Times	Location
Kalathur	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 550 Computational Mathematics for Machine Learning

4 credits. Fall and Spring

Undergraduate Prerequisites: Basic knowledge of Python or R; or consent of instructor. - Mathematics is fundamental to data science and machine learning. In this course, you will review essential mathematical concepts and fundamental procedures illustrated by Python and/or R code and visualizations. Computational methods for data science presented through accessible, self-contained examples, intuitive explanations, and visualization will be discussed. Equal emphasis will be placed on both mathematics and computational methods that are at the heart of many algorithms for data analysis and machine learning. You will also advance your mathematical proficiency enabling you to effectively apply your skills to data analytics and machine learning.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Independent	T	06:00:00 PM–08:45:00 PM	CAS 324

And one course from the following:

MET CS 688 Web Mining and Graph Analytics

4 credits. Fall and Spring

Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. - The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms, link analysis and community detection based on graphs. Laboratory Course.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Hajiyani	Independent	T	06:00:00 PM–08:45:00 PM	CAS B06A

Section A2, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Vasilkoski	Independent	R	06:00:00 PM–08:45:00 PM	SHA 206

Section O1, FALL 2025 Sep 2nd to Oct 20th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 699 Data Mining

4 credits. Fall and Spring

Prerequisites: MET CS 521 & MET CS 546; MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. Students learn underlying theories of data mining algorithms in the class and they practice those algorithms through assignments and a semester-long class project using R. After finishing this course, students will be able to independently perform data mining tasks to solve real-world problems.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Lee	Independent	W	06:00:00 PM–08:45:00 PM	SCI 115

Section O2, FALL 2025 Oct 28th to Dec 15th
Instructor	Type	Days	Times	Location
Joner	Independent	ARR	12:00:00 AM–12:00:00 AM

Concentration Requirements

(Four courses/16 units)

Choose four courses from the following:

MET CS 664 Artificial Intelligence

4 credits.

Graduate Prerequisites: MET CS 248 and MET CS 341 or MET CS 342. - Study of the ideas and techniques that enable computers to behave intelligently. Search, constraint propagations, and reasoning. Knowledge representation, natural language, learning, question answering, inference, visual perception, and/or problem solving. Laboratory course. Prereq: MET CS 341, MET CS 342, MET CS 520 or MET CS 521. Or instructor's consent.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Kalathur	Independent	W	06:00:00 PM–08:45:00 PM	CAS 315

Section O1, FALL 2025 Sep 2nd to Oct 20th
Instructor	Type	Days	Times	Location
Braude	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 766 Deep Reinforcement Learning

4 credits. Fall and Spring

Perquisites: MET CS 677 or instructor's consent. - This course focuses on reinforcement learning, covering fundamental concepts and advanced techniques. It begins with an introduction to reinforcement learning and key concepts, such as exploitation versus exploration and Markov Decision Processes. As the course progresses, it delves into state transition diagrams, the Bellman equation, and solutions to the Multi-Armed Bandits problem. Students will explore challenges and methods related to control and prediction. Then, they learn tabular methods, including Monte Carlo, Dynamic Programming, Temporal Difference Learning, SARSA, and Q-Learning. Afterwards, the course also extends into reviewing neural network concepts, covering convolutional and recurrent neural networks, and moves on to approximation methods for both discrete and continuous spaces, including DQN and its variants. Policy gradient methods, actor-critic methods. Finally, ethical considerations in AI and safety issues are also discussed.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	T	06:00:00 PM–08:45:00 PM	CDS 264

MET CS 767 Advanced Machine Learning and Neural Networks

4 credits. Fall and Spring

Prerequisites: MET CS 521; MET CS 622, MET CS 673 or MET CS 682; MET CS 677 strongly recommended; or consent of instructor. Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN's, Neural Nets and Deep Learning, Transformers, Recurrent Neural Nets, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptrons, backpropagation, attention, and transformers. Each student creates a term project.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	R	06:00:00 PM–08:45:00 PM	MET 122

Section A2, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	W	02:30:00 PM–05:15:00 PM	CDS 264

Section O2, FALL 2025 Oct 28th to Dec 15th
Instructor	Type	Days	Times	Location
Braude	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 787 AI and Cybersecurity

4 credits.

Prerequisite: MET CS 677 or consent of instructor. Explore the critical intersection between Artificial Intelligence (AI) and cybersecurity, focusing on two interconnected themes: protecting AI systems from vulnerabilities and harnessing the power of AI to address cybersecurity challenges. As AI becomes a cornerstone of modern technology, ensuring the security of AI-powered systems against adversarial attacks, backdoor threats, and model theft is essential. Simultaneously, AI offers transformative capabilities for malware detection, intrusion prevention, and malware analysis. Through a blend of theoretical foundations, hands-on exercises, and real-world case studies, you will study topics such as adversarial machine learning, backdoor injection and defense, intellectual property (IP) protection, and privacy-preserving AI. You will also learn how to design and implement AI-driven tools to identify and mitigate cyber threats in dynamic environments. Practical applications emphasize building resilient AI systems and utilizing advanced AI techniques to enhance security and detect emerging threats. Hands-on labs using existing tools will also be provided and required.

MET CS 788 Generative AI

4 credits. Fall

Prerequisites: MET CS 677, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or instructor's consent. - The first part of the course covers statistical concepts required for generative artificial intelligence. We review regressions and optimization methods as well as traditional neural network architectures, including perceptron and multilayer perceptron. Next, we move to Convolutional Neural Networks and Recurrent Neural Networks and close this part with Attention and Transformers. The second part of the course focuses on generative neural networks. We start with traditional self-supervised learning algorithms (Self Organized Map and Restricted Boltzmann Machine), then explore Auto Encoder architectures and Generative Adversarial Networks and move toward architectures that construct generative models, including recent advances in NLP, including LLMs, and Retrieval Augmented Methods. Finally, we describe the Neural Radiance Field, 3D Gaussian Splatting, and text-2-image models.

Master’s Thesis Option

(Two courses/8 units)

Students have the option to complete a master’s thesis by taking two Master Thesis courses (8 units) in addition to the program’s ten course (40 units) requirement. The thesis must be completed within 12 months and is available to MS in Applied Data Analytics candidates who have completed at least four courses toward their degree (not including foundation courses) and have a grade point average (GPA) of 3.7 or higher. Students are responsible for finding a thesis advisor and principal readers within the department. The advisor must be a full-time faculty member; the principal readers may be part-time faculty. Department approval is required.

MET CS 810 Master's Thesis in Computer Science 1

4 credits. Fall and Spring

This thesis must be completed within 12 months. Students majoring in Computer Science may elect a thesis option. This option is available to Master of Science in Computer Science candidates who have completed at least seven courses toward their degree and have a GPA of 3.7 or higher. Students are responsible for finding a thesis advisor and a principal reader within the department. The advisor must be a full-time faculty member; the principal reader may be part-time faculty member with a doctorate. Permission must be obtained by the department. 4cr.

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A2, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Rawassizadeh	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A3, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A4, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A5, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

MET CS 811 Master's Thesis in Computer Science 2

4 credits. Fall and Spring

Section A1, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A2, FALL 2025 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Dates & Deadlines

View BU MET’s academic calendar for online programs, including important dates and deadlines.

Application Requirements

Learn about application requirements for BU MET graduate degree and certificate programs.

A person holding a tablet comparing data with a person using a laptop.

How You Benefit from a
Boston University Education

A BU credential can help lay the foundation for career advancement and personal success.

Benefit from an average 24:1 student-to-instructor ratio.
Learn from highly qualified faculty and industry leaders who have substantial backgrounds and achievements in data analytics, data science, data storage technologies, cybersecurity, artificial intelligence (AI), machine learning, software development, and many other areas.
Gain in-depth, practical experience with the latest technologies through case studies and real-world projects.
Experience a supportive online network, with courses developed and taught by PhD-level full-time faculty and professionals with hands-on expertise in the industry.
Learn from the best— BU MET’s Department of Computer Science was established in 1979 and is the longest-running computer science department at BU. Over the course of its existence, the department has played an important role in the emergence of IT at the University and throughout the region.

All graduate students are automatically considered for merit scholarships during the application process and nominated based on eligibility. Learn more.

Rankings & Accreditations

U.S. News & World Report - Best Online Programs - Grad Information Technology 2025

#10, Best Online Master’s in Computer Information Technology Programs

MET’s online master’s degrees in computer information technology are ranked #10 in the nation by U.S. News & World Report for 2025.

Graduate with with Intelligent Analytics Expertise

In addition to the learning outcomes derived from Metropolitan College’s Applied Data Analytics master’s degree program, the concentration in AI & Machine Learning will equip you with the following skills:

Advanced Machine Learning and Deep Learning: Students will be able to solve complex problems such as computer vision, natural language processing, and speech recognition using machine learning algorithms, including supervised and unsupervised learning models, neural network architectures, and deep learning techniques.
Artificial Intelligence Development: Students will be able to design and implement agents and algorithms for self-learning systems, leveraging AI models for data representation and prediction, implementing evolutionary and genetic algorithms for optimization, and developing software systems that incorporate AI models to enhance capabilities.
Ethical AI and Communication: Students will be able to evaluate the ethical implications of AI systems, ensuring model fairness, accountability, and transparency, and effectively communicating technical AI concepts to non-technical stakeholders.

$103,500

Median annual wage for Data Scientists, May 2023

Source: Bureau of Labor Statistics

“This program led me to my current position as a data scientist at Boston’s Massachusetts General Hospital, where I implement machine learning models to improve the hospital’s operational efficiency and support physicians with clinical research. I am immensely happy with my investment in graduate school, as it led me to many amazing opportunities!” Read more.

Melissa Viator (MET’23)
Data Scientist, Massachusetts General Hospital
MS, Applied Data Analytics

Advance Your Career

BU MET’s Applied Data Analytics master’s concentration in AI & Machine Learning prepares you for a wealth of different roles, such as Data Scientist, Business Intelligence Analyst, Systems Analyst, Chief Analytics Officer, Analytics Manager, Marketing Analyst, Business Analyst, Financial Analyst, Economist, Machine Learning Engineer, AI Specialist, Data Engineer, Predictive Modeler, and Decision Scientist, among others.

Recent graduates have found job opportunities and career paths at companies such as:

Fidelity Investments
Akamai Technologies
Amazon Web Services (AWS)
Chi Alpha Campus Ministries
Deloitte Consulting

Drew University
McKesson
Olympus Americas
Travelers
Turiyatree Technologies

Take Advantage of Career Resources at BU MET

You will find the support you need in reaching your career goals through MET’s Career Development office, which offers a variety of job-hunting resources, including one-on-one career counseling by appointment for online students. You can also take advantage of tools and resources available online through BU’s Center for Career Development.

Computer Science Faculty

Guanglan Zhang

Associate Professor, Computer Science

Coordinator, Health Informatics Programs

Chair, Computer Science

Shengzhi Zhang

Associate Professor

Associate Chair, Computer Science

Suresh Kalathur

Assistant Professor, Computer Science

Director, Analytics

Farshid Alizadeh Shabdiz

Professor of the Practice, Computer Science

View All Faculty

Interested in Learning More?

MET Events

Tuition & Fees

Financial Aid

Recent News

Can Overseas Students study Online with BU?

Boston University (BU) welcomes learners from around the world to join its growing community of online students. Through BU Virtual and the university’s top-ranked schools and colleges, international students can earn a world-class Boston University degree no matter where they live. Whether you’re looking to advance your career, change industries, or gain expertise in a […]

Exploring the Costs of Online Degrees

Understanding Opportunity Cost When comparing online and residential graduate programs, it’s important to look beyond tuition. The true cost of earning a degree includes opportunity cost – the income you forego and living expenses you incur while studying full-time on campus. By choosing an online degree, you can typically continue working, maintain your income, and […]

Will My Degree Say Online?

What Is an Online Degree? Earning your degree online simply means that all the classes and coursework needed for your degree are virtual. An online degree does not differ in terms of academic rigor or quality. The key difference is the way that you earn your degree—online. Online learning offers unique opportunities with its flexibility […]

Area of Study

Be the first to learn about new programs and updates.

Online Master of Science in Applied Data Analytics, AI & Machine Learning Concentration

At a Glance

Master Intelligent Data Analytics and Innovative AI Technologies