Online MS in Applied Data Analytics, AI & Machine Learning

At a Glance

Completion Time

16–20

Months

Credits Required

32–40

Tuition

$27,920–$32,675

Part-Time*

*Based on 2026–2027 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, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Mohan	Independent	T	06:00:00 PM–08:45:00 PM	MET 122

Section O1, SPRG 2026 Jan 13th to Mar 2nd
Instructor	Type	Days	Times	Location
Zhang	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, SPRG 2026 Mar 10th to Apr 27th
Instructor	Type	Days	Times	Location
Trajanov	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 526 Data Structures and Algorithms

4 credits. Fall and Spring

Prerequisites: MET CS300 and either MET CS520 or MET CS521, or consent of instructor. 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.

Section O1, SPRG 2026 Jan 13th to Mar 2nd
Instructor	Type	Days	Times	Location
Braude	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, SPRG 2026 Mar 10th to Apr 27th
Instructor	Type	Days	Times	Location
Zhang	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

Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. Learn the foundations of machine learning, regression, and classification. Topics include how to describe data, statistical inference, 1 and 2 sample tests of means and proportions, simple linear regression, multiple linear regression, multinomial regression, logistic regression, analysis of variance, and regression diagnostics. These topics are explored using the statistical package R, with a focus on understanding how to use these methods and interpret their outputs and how to visualize the 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 in order to help you learn when and how to deploy different methods.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Zhang	Independent	R	12:30:00 PM–03:15:00 PM	COM 217

Section A2, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	W	06:00:00 PM–08:45:00 PM	CAS 116

Section O2, SPRG 2026 Mar 10th to Apr 27th
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, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Pinsky	Independent	W	06:00:00 PM–08:45:00 PM	CAS 313

Section A2, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Pinsky	Independent	T	06:00:00 PM–08:45:00 PM	HAR 210

Section O2, SPRG 2026 Mar 10th to Apr 27th
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

Prerequisites: MET LB 103, MET LB 104, and (METCS 520 or METCS 521), or equivalent knowledge, or consent of instructor. 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 methods. Data populations using discrete, continuous, and multivariate distributions are explored. Sampling methods and errors during measurements and computations are analyzed in the course. String manipulations and data wrangling methods are examined in detail. The concepts covered in the course are demonstrated using R. Laboratory Course. Restrictions: This course may not be taken in conjunction with MET CS 550.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Rizinski	Independent	M	06:00:00 PM–08:45:00 PM	MET 122

Section O1, SPRG 2026 Jan 13th to Mar 2nd
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

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. Restrictions: This course may not be taken in conjunction with MET CS 544.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Pinsky	Independent	M	06:00:00 PM–08:45:00 PM	SOC B57

Section O1, SPRG 2026 Jan 13th to Mar 2nd
Instructor	Type	Days	Times	Location
Pinsky	Independent	ARR	12:00:00 AM–12:00:00 AM

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, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Hajiyani	Independent	M	06:00:00 PM–08:45:00 PM	FLR 123

Section O2, SPRG 2026 Mar 10th to Apr 27th
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 LB 103 and MET LB 104; and either 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. You will learn underlying theories of data mining algorithms in the class and practice those algorithms through assignments and a semester-long class project using R. After finishing this course, you will be able to independently perform data mining tasks to solve real-world problems.

Section A2, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Lee	Independent	W	06:00:00 PM–08:45:00 PM	MCS B33

Section O1, SPRG 2026 Jan 13th to Mar 2nd
Instructor	Type	Days	Times	Location
Lee	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.

Prerequisites: MET CS 248 and 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.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Kalathur	Independent	M	06:00:00 PM–08:45:00 PM	MET 101

Section O1, SPRG 2026 Jan 13th to Mar 2nd
Instructor	Type	Days	Times	Location
Mansur	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 766 Deep Reinforcement Learning

4 credits. Fall and Spring

Prerequisites: MET CS 577 or consent of instructor. - 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, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Mohan	Independent	W	06:00:00 PM–08:45:00 PM	PHO 201

MET CS 767 Advanced Machine Learning and Neural Networks

4 credits. Fall and Spring

Prerequisites: MET CS 521 and at least one of MET CS 577, MET CS 622, MET CS 673 or MET CS 682; 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: perceptron's, backpropagation, attention, and transformers. Each student creates a term project.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Mohan	Independent	R	06:00:00 PM–08:45:00 PM	MET 101

Section O2, SPRG 2026 Mar 10th to Apr 27th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 787 AI and Cybersecurity

4 credits.

Prerequisites: MET CS 577 or consent of instructor. This course provides an in-depth exploration of 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 tackle cybersecurity challenges. As AI becomes a cornerstone of modern technology, ensuring the security of AI-powered systems against adversarial attacks, backdoor attacks, and model theft is essential. Simultaneously, AI offers transformative capabilities for malware detection, intrusion prevention, and malware analysis. Through a combination of theoretical foundations, hands-on exercises, and real-world case studies, students will delve into topics such as adversarial machine learning, backdoor injection and defense, IP protection, and privacy-preserving AI. They will also learn how to design and implement AI-driven tools for identifying and mitigating cyber threats in dynamic environments. The course emphasizes practical applications, encouraging students to build resilient AI systems and utilize advanced AI techniques to enhance system security and detect emerging threats. Hands-on labs based on existing tools are provided and required.

MET CS 788 Generative AI

4 credits. Fall

Prerequisites: MET CS 577, Python programming, mathematics required for machine learning, and familiarity with neural networks. Or consent of instructor. - 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.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	R	06:00:00 PM–08:45:00 PM	CAS B06A

MET CS 790 Computer Vision in AI

4 credits. Fall and Spring

Prerequisites: MET CS 566 or instructor's consent. - Students enrolled in this course will gain comprehensive insights into fundamental and advanced concepts within the dynamic realm of computer vision. The curriculum will focus on cutting-edge applications of deep neural networks in computer vision. Through hands-on experiences and practical exercises, students will learn to leverage computer vision and machine learning techniques to solve real-world challenges. This course not only equips students with theoretical knowledge but empowers them to apply these concepts effectively, fostering a deep understanding of how computer vision can be harnessed to address complex problems in diverse industries.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
Zhang	Independent	T	06:00:00 PM–08:45:00 PM	CGS 113

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 MS Thesis 1

4 credits. Fall and Spring

This is the first course of the two-part thesis option available to Master’s degree program candidates in the Department of Computer Science. You must have completed at least four courses toward your degree and have a grade point average (GPA) of 3.7 or higher. You are responsible for finding a thesis advisor and a principal reader within the department. Please refer to the Department for further details on the application process. Both MET CS 810 Master’s Thesis 1 and MET CS 811 Master’s Thesis 2 must be completed within 12 months.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A2, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A3, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
	Directed Study	ARR	12:00:00 AM–12:00:00 AM

MET CS 811 Master's Thesis 2

4 credits. Fall and Spring

This is the second course of the two-part thesis option available to Master’s degree program candidates in the Department of Computer Science. You must have completed at least four courses toward your degree and have a grade point average (GPA) of 3.7 or higher. You are responsible for finding a thesis advisor and a principal reader within the department. Please refer to the Department for further details on the application process. Both METCS 810 Master’s Thesis 1 and METCS 811 Master’s Thesis 2 must be completed within 12 months.

Section A1, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A2, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A3, SPRG 2026 Jan 20th to Apr 30th
Instructor	Type	Days	Times	Location
	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.

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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 - 2026

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

MET’s online master’s degrees in computer information technology are ranked #12 in the nation by U.S. News & World Report.

#2 Best Online Master’s in Data Analytics of 2026

BU MET’s MS in Applied Data Analytics is ranked #2 Best Online Master’s in Data Analytics Degree Programs for 2026 by TechGuide.

Graduate 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

Coordinator, Web Application Development

Suresh Kalathur

Assistant Professor, Computer Science

Director, Analytics

Farshid Alizadeh Shabdiz

Professor of the Practice, Computer Science

View All Faculty

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

At a Glance

Master Intelligent Data Analytics and Innovative AI Technologies