Prepare for Critical Data Analytics Roles
Available online and on campus, the Master of Science in Applied Data Analytics (MSADA) at Boston University’s Metropolitan College (MET) is a hands-on program that exposes you to various database systems, data mining tools, data visualization tools and packages, Python packages, R packages, and cloud services. The knowledge of analytics tools combined with an understanding of data mining and machine learning approaches will enhance your ability to critically analyze real-world problems and understand the possibilities and limitations of analytics applications.
Program at a Glance
- Online and On Campus
- Part-Time or Full-Time Study
- STEM Designated
- 32 Credits
- 8–16 Months to Completion
- 17 Core Faculty
- No GRE/GMAT
- Tuition & Fees—Part-Time Study*: $27,204
*Based on 2025–2026 Boston University tuition and fees. Merit scholarship may reduce cost.
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Advance Your Career with a Master’s in Applied Data Analytics
With data analytics needs influencing every major industry—including healthcare, tech, finance, communication, entertainment, energy, transportation, government, and manufacturing, to name some—there is significant growth in specialized data science, data engineering, automation, AI, and machine learning areas. Yet the demand for skilled talent continues to outpace supply. QuantHub research confirms a shortfall of 250,000 data scientists in 2020, while McKinsey Global Institute anticipates as much as 12 percent annual growth in demand for graduates from data science programs over the next decade.
To harness the potential of this big data revolution, you need advanced techniques.
As a graduate of the MS in Applied Data Analytics program at BU MET, you will be able to demonstrate the ability to create powerful predictions through modeling and machine learning, and drive critical business decisions—skills needed to excel in a growing list of roles such as data scientist, economist, data analyst, business intelligence analyst, systems analyst, chief analytics officer, analytics manager, marketing analyst, business analyst, or financial analyst, among others.
#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.
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Computer Science Career Outlook
Top computer science careers in data science, software development, and other popular areas of IT.
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What Is My Career Outlook as a Graduate of This Program?
404,587
Total number of US Jobs
53,424
Annual job openings
35%
Projected ten-year growth in jobs
(faster than average)
$100.4K
Median annual salary
Common job titles include:
- Data Scientist
- Data Analyst
- Program Analyst
- Statistical Programmer
- Programmer Analyst
Employers seek expertise in:
- Data analysis
- Python
- SQL
- R
Source: Lightcast, U.S. 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
Why BU’s Applied Data Analytics Master’s is Ranked in the Top 10
- Active Learning Environment: BU MET’s Applied Data Analytics courses ensure you get the attention you need, while introducing case studies and real-world projects that ensure you gain in-depth, practical experience with the latest technologies.
- Career Counseling: MET’s Career Development office and BU’s Center for Career Development offer a variety of job-hunting resources, including one-on-one career counseling by appointment for both online and on-campus students.
- Engaged Faculty: In BU MET’s Applied Data Analytics master’s program, you benefit from working closely with 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.
- Extensive Network: Study Applied Data Analytics alongside fellow professionals from all backgrounds, learn from faculty who have valuable IT contacts across several sectors, and benefit from an alumni community with strong professional connections.
- STEM Designated: Eligible graduates on student visas have access to an Optional Practical Training (OPT) of 12 months and an extension for up to 24 additional months.
- 15:1 Class Ratio: Enjoy an exceptional student-to-instructor ratio, ensuring close interaction with faculty and access to support.
- Valuable Resources: Make use of Boston University’s extensive resources, including the Center for Career Development, Educational Resource Center, Fitness & Recreation Center, IT Help Centers, Mugar Memorial Library, Howard Thurman Center for Common Ground, George Sherman Union, Rafik B. Hariri Institute for Computing and Computational Science & Engineering, and many others.
- Flexible Options: Study at the pace that works for you, evenings on campus or fully online. Courses begin fall, spring, and summer; online courses have two starts per term.
- Track Record: 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 its four decades, the department has played an important role in the emergence of IT at the University and throughout the region.
- Merit Scholarships: US citizens and permanent residents are automatically considered during the application process and nominated based on eligibility. Learn more.
Master the Tools to Excel in Applied Data Analytics
Offered through BU MET’s Department of Computer Science, the Master of Science in Applied Data Analytics can set you apart by adding invaluable analytics expertise, skills, and projects to your résumé.
Ideal for mid-career IT professionals or students, BU MET’s Applied Data Analytics curriculum provides solid knowledge of data analytics and examines the presentation and applications of the latest industry tools and approaches within an academically rigorous framework. Emphasizing both data analytics and applied areas—including databases, applied machine learning, and large dataset processing methods—the Applied Data Analytics master’s curriculum provides a thorough immersion in concepts and techniques for organizing, cleaning, analyzing, and representing/visualizing large amounts of data.
Graduate with Expertise
Metropolitan College’s Applied Data Analytics master’s degree will equip you with:
- Knowledge of the foundations of applied probability and statistics and their relevance in day-to-day data analysis.
- Comprehension of computing concepts and application requirements involving massive computing needs and data storage.
- The ability to apply various data visualization techniques using real-world data sets and analyze the graphs and charts.
- Understanding of web analytics and metrics, procuring and processing unstructured text/data, and the ability to investigate hidden patterns.
- Knowledge-discovery skills using data mining techniques and tools over large amounts of data.
- The ability to implement machine learning algorithms and recognize their pertinence in real-world applications.
- Comprehensive knowledge of data analytics techniques, skills, and critical thinking, and an understanding of the possibilities and limitations of their applications.
Certificate-to-Degree Pathway
BU MET graduate certificate programs can serve as building blocks to a master’s degree. The Graduate Certificate in Data Analytics and Graduate Certificate in Database Management & Business Intelligence share specific courses with the master’s in Applied Data Analytics program, giving you the option to take the certificate on your path to a master’s degree. Students currently enrolled in a graduate certificate who are interested in transitioning into a master’s degree should contact their academic advisor to declare their interest in this pathway. A new master’s degree application is not required. Connect with a graduate admissions advisor at csadmissions@bu.edu to learn more about this option.
Master’s in Applied Data Analytics Curriculum
A total of 32 credits is required.
Core Curriculum
(Six courses/24 credits)
MET CS 544 Foundations of Analytics and Data Visualization
Fall ‘25
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: MET CS546 and (MET CS520 or MET CS521), or equivalent knowledge, or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Kalathur |
CAS 203 |
M |
6:00 pm – 8:45 pm |
A2 |
IND |
Diwania |
CAS 233 |
T |
6:00 pm – 8:45 pm |
A3 |
IND |
Pan |
CAS 218 |
W |
6:00 pm – 8:45 pm |
O1 |
IND |
Kalathur |
|
ARR |
12:00 am – 12:00 am |
O2 |
IND |
Kalathur |
|
ARR |
12:00 am – 12:00 am |
MET CS 555 Foundations of Machine Learning
Fall ‘25
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: MET CS 544 or equivalent knowledge, or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A3 |
IND |
Alizadeh-Shabdiz |
SOC B63 |
M |
2:30 pm – 5:15 pm |
A4 |
IND |
Alizadeh-Shabdiz |
KCB 104 |
W |
6:00 pm – 8:45 pm |
O2 |
IND |
Alizadeh-Shabdiz |
|
ARR |
12:00 am – 12:00 am |
MET CS 566 Analysis of Algorithms
Fall ‘25
Prerequisites: MET CS 342 or MET CS 526 or consent of instructor. Learn methods for designing and analyzing algorithms while practicing hands-on programming skills. Topics include divide-and-conquer, sorting, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, and NP-completeness. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Zhang |
HAR 408 |
M |
6:00 pm – 8:45 pm |
A2 |
IND |
Chertushkin |
SOC B57 |
T |
6:00 pm – 8:45 pm |
A3 |
IND |
Belyaev |
CAS 326 |
W |
6:00 pm – 8:45 pm |
A4 |
IND |
Belyaev |
CAS 426 |
R |
6:00 pm – 8:45 pm |
O1 |
IND |
Zhang |
|
ARR |
12:00 am – 12:00 am |
MET CS 677 Data Science with Python
Fall ‘25
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. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Pinsky |
CAS 226 |
M |
6:00 pm – 8:45 pm |
A2 |
IND |
Mohan |
CAS 222 |
R |
6:00 pm – 8:45 pm |
A3 |
IND |
Pinsky |
CAS B36 |
W |
6:00 pm – 8:45 pm |
A4 |
IND |
Enxing |
MCS B37 |
T |
6:00 pm – 8:45 pm |
O2 |
IND |
Mohan |
|
ARR |
12:00 am – 12:00 am |
MET CS 688 Web Mining and Graph Analytics
Fall ‘25
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. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Hajiyani |
CAS B06A |
T |
6:00 pm – 8:45 pm |
A2 |
IND |
Vasilkoski |
SHA 206 |
R |
6:00 pm – 8:45 pm |
A3 |
IND |
Vasilkoski |
EPC 206 |
T |
6:00 pm – 8:45 pm |
O1 |
IND |
Rawassizadeh |
|
ARR |
12:00 am – 12:00 am |
MET CS 699 Data Mining
Fall ‘25
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. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Lee |
SCI 115 |
W |
6:00 pm – 8:45 pm |
A2 |
IND |
Lee |
EPC 209 |
R |
6:00 pm – 8:45 pm |
O2 |
IND |
Joner |
|
ARR |
12:00 am – 12:00 am |
General Electives
(Two courses/8 credits)
Choose two electives from the following list (some courses may not be available in the online format):
MET CS 550 Computational Mathematics for Machine Learning
Fall ‘25
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. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Pinsky |
CAS 324 |
T |
6:00 pm – 8:45 pm |
A2 |
IND |
Pinsky |
EPC 206 |
W |
8:00 am – 10:45 am |
MET CS 689 Designing and Implementing a Data Warehouse
Fall ‘25
Graduate Prerequisites: CS 579 or CS 669 or consent of the instructor - This course surveys state-of-the art technologies in DW and Big Data. It describes logical, physical and semantic foundation of modern DW infrastructure. Students will create a cube using OLAP and implement decision support benchmarks on Hadoop/Spark vs Vertica database. Upon successful completion, students will be familiar with tradeoffs in DW design and architecture. Prereq: MET CS 579 or MET CS 669 and either MET CS 520 or MET CS 521. Or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Polnar |
CAS 222 |
M |
6:00 pm – 8:45 pm |
MET CS 767 AI and Cybersecurity
Fall ‘25
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. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Rawassizadeh |
SOC B57 |
R |
6:00 pm – 8:45 pm |
A2 |
IND |
Alizadeh-Shabdiz |
CDS 264 |
W |
2:30 pm – 5:15 pm |
O2 |
IND |
Braude |
|
ARR |
12:00 am – 12:00 am |
MET CS 777 Big Data Analytics
Fall ‘25
This course is an introduction to large-scale data analytics. Big Data analytics is the study of how to extract actionable, non-trivial knowledge from massive amount of data sets. This class will focus both on the cluster computing software tools and programming techniques used by data scientists, as well as the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, we will cover the basics systems and techniques to store large-volumes of data, as well as modern systems for cluster computing based on Map-Reduce pattern such as Hadoop MapReduce, Apache Spark and Flink. Students will implement data mining algorithms and execute them on real cloud systems like Amazon AWS, Google Cloud or Microsoft Azure by using educational accounts. On the data mining models side, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side.
Prerequisite: MET CS 521, MET CS 544 and MET CS 555. Or, MET CS 677. Or, Instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Pham |
EPC 205 |
W |
6:00 pm – 8:45 pm |
O1 |
IND |
Trajanov |
|
ARR |
12:00 am – 12:00 am |
MET CS 779 Advanced Database Management
Fall ‘25
Graduate Prerequisites: (METCS579 OR METCS669) or consent of the instructor - This course covers advanced aspects of database management including normalization and denormalization, query optimization, distributed databases, data warehousing, and big data. There is extensive coverage and hands on work with SQL, and database instance tuning. Course covers various modern database architectures including relational, key value, object relational and document store models as well as various approaches to scale out, integrate and implement database systems through replication and cloud based instances. Students learn about unstructured "big data" architectures and databases, and gain hands-on experience with Spark and MongoDB. Students complete a term project exploring an advanced database technology of their choice. Prereq: MET CS 579 or MET CS 669; or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Polnar |
CAS 306 |
R |
6:00 pm – 8:45 pm |
Computer Science Faculty
Tuition & Financial Assistance
Competitive Tuition
Our part-time rates are substantially lower than those of the traditional, full-time residential programs yet provide access to the same high-quality BU education.
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Comprehensive Financial Assistance
Our services include
scholarships, graduate loans, and payment plans.
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How Much Does This Program Cost?
BU MET programs offer the flexibility of part-time or full-time study, either on campus or online. Tuition, fees, and total program cost are determined by enrollment status. If you enroll in 1–2 courses (4–8 credits) in a semester, you are charged the part-time per-credit rate. If you enroll in 3–4 courses (12–16 credits) in a semester, you are charged the full-time semester rate.
MS in Applied Data Analytics (Online and On Campus)
Enrollment Status |
Part Time |
Full Time |
Courses per Semester |
2 courses (8 credits) |
4 courses (16 credits) |
3 courses (12 credits) |
Time to Degree |
4 semesters (16 months) |
2 semesters (8-12 months)*** |
3 semesters (12-16 months)*** |
Tuition* |
$567-$1,005 per credit** |
$34,935 per semester |
$34,935 per semester |
Fees per Semester* |
$75 |
$501 |
$501 |
Total Degree Cost* |
$27,204 |
$70,872 |
$78,987 |
*Based on 2025–2026 Boston University tuition & fee rates.
**Cost per credit is determined by course number (100–599 = $567/credit, 600–999 = $1,005/credit).
***Summer semester enrollment is not required for international students to maintain F-1 visa status. Enrollment in summer semester coursework will expedite completion of program and reduce total program cost.
International students seeking an F-1 visa for on-campus study must enroll full time and demonstrate availability of funds to cover the Estimated Cost of Graduate Study; those who wish to study online may enroll part-time but are not eligible for a visa. Learn more about International Student Tuition & Fees.
Questions? Please contact us to hear from an Admissions Advisor who can help you determine the best enrollment pathway. For information regarding financial aid, visit BU MET’s Financial Aid page.
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Please visit the BU MET admissions page for details on how to apply, financial assistance, tuition and fees, requirements for international students, and more.
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