Learn to Process and Visualize Data
Available online, on campus, and in a blended format, the Master of Science in Computer Information Systems concentration in Data Analytics at Boston University’s Metropolitan College (MET) is designed to immerse you in the fast-paced world of technological innovation—preparing you for IT leadership and artificial intelligence-integrated positions in a variety of sectors seeking data analysts.
Program at a Glance
- Top 10 Online Program since 2014
- Online, On Campus, and Blended
- Part-Time or Full-Time Study
- STEM Designated
- 32 Units
- 8–16 Months to Completion
- 18 Core Faculty
- No GRE/GMAT
- Tuition & Fees Range—Part-Time Study*: $25,452-$27,204
*Based on 2025–2026 Boston University tuition and fees. Merit scholarship may reduce cost.
Advance Your Career with a Master’s in Computer Information Systems
The ability to harness and interpret vast amounts of data is essential to effective, evidence-based management decision-making. From routine daily purchasing decisions to major investment strategies, the ability to successfully distill data and present it in an intuitive way is critical to an organization’s bottom line. IT professionals who are skilled in data analytics are highly valued and sought after.
By earning BU’s Master of Science in Computer Information Systems program with a concentration in Data Analytics, you will develop the skills required to compete for data analysis jobs amid rising global demand, while exploring the intricacies of data analytics and various topics related to data processing, analysis, and visualization.
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
“Three important skills students can learn from this program are programming, which is a way to communicate with the machines; analytical thinking, which yields valuable insights from the data; and data management, which is important in efficiently storing, processing, and retrieving the data.” Read more.
Sambasiva Rao Gangineni (MET'19)
Senior Software Engineer, Availity Clinical Solutions
MS, Computer Information Systems; Concentration, Data Analytics
Connect with a Student Ambassador
Why BU’s Computer Information Systems Degree Has Been Top 10 since 2014
- Active Learning Environment: BU MET’s Computer Information Systems courses introduce 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 Computer Information Systems master’s program, you benefit from working closely with highly qualified faculty and industry leaders with substantial backgrounds and achievements in data analysis.
- AI-Integrated: Many courses prepare you to leverage artificial intelligence for the analysis, development, and integration of modern information systems.
- Extensive Network: Study information systems alongside peers with solid business experience, 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.
- 24:1 Average 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 the course of its existence, 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.
Rankings & Accreditations
#10, Best Online Master's in Computer Information Technology ProgramsMET’s online master’s degrees in computer information technology are ranked #10 in the nation by U.S. News & World Report for 2025.
Master the Tools to Excel in Computer Information Systems
The Data Analytics concentration is part of BU MET’s MS in Computer Information Systems (MSCIS) degree program. BU’s industry-leading MSCIS curriculum combines in-depth technical skills and emerging technology management. Learn the foundations of applied probability and statistics, and their relevance in day-to-day data analysis. Explore various data visualization techniques and their applications using real-world data sets. Facilitate knowledge discovery using data mining techniques with vast amounts of data. Gain hands-on experience with web analytics, data procuring and processing unstructured text.
Along with probability theory and statistical analysis methods and tools, you will learn how to generate relevant visual presentations of data and will examine concepts and techniques for data mining, text mining, and web mining. In addition to the broad background in the theory and practice of information technology gained from the Computer Information Systems core courses, those who complete this program will have a solid knowledge of data analytics practices accompanied by exposure to the methods and tools for data mining and knowledge discovery.
With eight concentrations, the Computer Information Systems master’s encompasses a number of other fast-growing and well-paid segments of the IT job market, providing the foundation for work as an application analyst, computer and information systems manager, data analyst, data scientist, cybersecurity analyst, IT consultant, network and computer systems administrator, computer systems analyst, database administrator, and many other integral positions in an organization.
Graduate with Expertise
Metropolitan College’s Computer Information Systems master’s degree concentration in Data Analytics will equip you with:
- Possesses a foundation in applied probability and statistics, especially their relevance to day-to-day data analysis.
- Apply various data visualization techniques and their applications to real-world data sets.
- Understand web analytics and metrics, procure and process unstructured text, and uncover hidden patterns.
- Possesses knowledge discovery skills using data mining techniques over vast amounts of data.
Certificate-to-Degree Pathway
You can also earn the master’s in Computer Information Systems with a concentration in Data Analytics by completing the BU MET Graduate Certificate in Information Technology and Graduate Certificate in Data Analytics, plus one additional course—either Information Structures with Java (MET CS 520) or Information Structures with Python (MET CS 521). 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 Computer Information Systems Curriculum
Data Analytics Concentration
A total of 32 units is required.
Students must complete the core courses and Data Analytics concentration requirements.
Prerequisites and Corequisites
Applicants to the program are required to have a bachelor’s degree in any discipline from a regionally accredited institution. Students with limited academic background in information technology, computer science, and mathematics may be required to enroll in one or more of the following complimentary labs. Recommendations will be provided upon admission.
Prerequisite (open to all students):
- MET LB 102 Introduction to Computer Information Systems
Corequisites (open to all students):
- MET LB 103 Core Mathematical Concepts
- MET LB 104 Foundations of Probability
Core Courses
(Four courses/16 units)
One of the following:
MET CS 520 Information Structures with Java
Fall ‘25
Prerequisites: METCS 200 or 300 or Instructor's Consent. Not recommended for students without a programming background. - This course covers the concepts of object-oriented approach to software design and development using the Java programming language. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, applets, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, interfaces, creating user interfaces, exceptions, and streams. Upon completion of this course the students will be able to apply software engineering criteria to design and implement Java applications that are secure, robust, and scalable [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Donald |
PSY B53 |
M |
6:00 pm – 8:45 pm |
E1 |
IND |
Donald |
PSY B53 |
M |
6:00 pm – 8:45 pm |
O1 |
IND |
Zhang |
|
ARR |
12:00 am – 12:00 am |
MET CS 521 Information Structures with Python
Fall ‘25
HUB
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. [ 4 cr. ]
BU Hub Learn More - Creativity/Innovation
- Critical Thinking
- Quantitative Reasoning II
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Lu |
KCB 102 |
M |
6:00 pm – 8:45 pm |
A2 |
IND |
Mohan |
PHO 202 |
T |
6:00 pm – 8:45 pm |
A3 |
IND |
Pinsky |
SHA 202 |
W |
9:00 am – 11:45 am |
O1 |
IND |
Zhang |
|
ARR |
12:00 am – 12:00 am |
O2 |
IND |
Bond |
|
ARR |
12:00 am – 12:00 am |
And these three courses:
MET CS 625 Business Data Communication and Networks
Fall ‘25
Undergraduate Prerequisites: On Campus Prerequisites: MET CS 200 Fundamentals of Information Techno logy. Or instructor^s consent. ; Undergraduate Corequisites: Restrictions: MS CIS only. This course may not be taken in conjunction with CS 425 (undergraduate) or CS 535. Only CS 535 or CS 625 can be c ounted towards degree requirements. - This course presents the foundations of data communications and takes a bottom-up approach to computer networks. The course concludes with an overview of basic network security and management concepts. Prereq: MET CS 200, or instructor's consent. This course may not be taken in conjunction with MET CS 425 (undergraduate) or MET CS 535. Only one of these courses can be counted towards degree requirements. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Arena |
CGS 515 |
T |
12:30 pm – 3:15 pm |
A2 |
IND |
Arena |
SHA 206 |
T |
6:00 pm – 8:45 pm |
O1 |
IND |
Rizinski |
|
ARR |
12:00 am – 12:00 am |
O2 |
IND |
Rizinski |
|
ARR |
12:00 am – 12:00 am |
MET CS 669 Database Design and Implementation for Business
Fall ‘25
Undergraduate Prerequisites: Restrictions: Only for MS CIS. This course may not be taken in conjunc tion with MET CS 469 (undergraduate) or MET CS 579. Only one of these courses can be counted towards degree requirements. - Students learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Students gain extensive hands- on experience using Oracle or Microsoft SQL Server as they learn the Structured Query Language (SQL) and design and implement databases. Students design and implement a database system as a term project. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 579. Only one of these courses can be counted towards degree requirements. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Saunders |
HAR 211 |
W |
6:00 pm – 8:45 pm |
A2 |
IND |
Lee |
CAS 225 |
R |
6:00 pm – 8:45 pm |
E1 |
IND |
Saunders |
HAR 211 |
W |
6:00 pm – 8:45 pm |
O1 |
IND |
Lee |
|
ARR |
12:00 am – 12:00 am |
O2 |
IND |
Mansur |
|
ARR |
12:00 am – 12:00 am |
MET CS 682 Information Systems Analysis and Design
Fall ‘25
Undergraduate Prerequisites: Basic programming knowledge or instructor's consent. - Object-oriented methods of information systems analysis and design for organizations with data- processing needs. System feasibility; requirements analysis; database utilization; Unified Modeling Language; software system architecture, design, and implementation, management; project control; and systems-level testing. Prerequisite: Basic programming knowledge or instructor's consent. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Guadagno |
PSY B55 |
T |
6:00 pm – 8:45 pm |
O1 |
IND |
Williams |
|
ARR |
12:00 am – 12:00 am |
O2 |
IND |
Polnar |
|
ARR |
12:00 am – 12:00 am |
Students who have completed courses on core curriculum subjects as part of their undergraduate degree program may request permission from the Department of Computer Science to replace the corresponding core courses with graduate-level computer information systems electives. Please refer to MET CS Academic Policies Manual for further details.
Concentration Requirements
(Four courses/16 units)
One of the following:
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 |
O1 |
IND |
Kalathur |
|
ARR |
12:00 am – 12:00 am |
O2 |
IND |
Kalathur |
|
ARR |
12:00 am – 12:00 am |
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 |
And:
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 |
Plus two courses selected from the following (some courses may not be available in the online format):
MET CS 577 Data Science with Python
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. [ 4 cr. ]
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 |
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 |
O2 |
IND |
Joner |
|
ARR |
12:00 am – 12:00 am |
MET CS 767 Advanced Machine Learning and Neural Networks
Fall ‘25
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. [ 4 cr. ]
Section |
Type |
Instructor |
Location |
Days |
Times |
A1 |
IND |
Rawassizadeh |
MET 122 |
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 |
MET 101 |
W |
6:00 pm – 8:45 pm |
O1 |
IND |
Trajanov |
|
ARR |
12:00 am – 12:00 am |
Master’s Thesis Option
(8 units)
Students have the option to complete a master’s thesis in addition to the program’s eight course (32 unit) requirements. The thesis option is to be completed within twelve months and is available to Master of Science in Computer Information Systems candidates who have completed at least four 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 with a PhD (unless waived by the department).
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.
Learn More
Comprehensive Financial Assistance
Our services include
scholarships, graduate loans, and payment plans.
Learn More
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 units) in a semester, you are charged the part-time per-unit rate. If you enroll in 3–4 courses (12–16 units) in a semester, you are charged the full-time semester rate.
MS in Computer Information Systems, Data Analytics Concentration (Online and On Campus)
Enrollment Status |
Part Time |
Full Time |
Courses per Semester |
2 courses (8 units) |
4 courses (16 units) |
3 courses (12 units) |
Time to Degree |
4 semesters (16 months) |
2 semesters (8-12 months)*** |
3 semesters (12-16 months)*** |
Tuition* |
$567–$1,005 per unit** |
$34,935 per semester |
$34,935 per semester |
Fees per Semester* |
$75 |
$501 |
$501 |
Total Degree Cost* |
$25,452–$27,204 |
$70,872 |
$78,987 |
*Based on 2025–2026 Boston University tuition and fee rates.
**Cost per unit is determined by course number (100–599 = $567/unit, 600–999 = $1,005/unit).
***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.
Get Started
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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