Learn to Process and Visualize Data
Available online and on campus, 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 positions in a variety of sectors seeking data analysts.
Program at a Glance
- Online and On Campus
- Part-Time or Full-Time Study
- 40 Credits
- 18–24 Months to Completion
- 17 Core Faculty
- No GRE/GMAT
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.
#8 Best Online Master's in Computer Information Technology Programs
MET’s online master’s degrees in computer information technology are ranked #8 in the nation by U.S. News & World Report for 2021.Learn More
A National Center of Academic Excellence
Boston University has been designated a Center of Academic Excellence (CAE) in Cyber Defense and Research by the National Security Agency and Department of Homeland Security. Our information security programs are certified by the Committee on National Security Systems (CNSS).Learn More
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“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.”—Sambasiva Rao Gangineni (MET’19), Software Engineer at Diameter Health
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.
- 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.
- 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.
- 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, Center for Antiracist Research, 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: All applicants are automatically considered, and admitted students are nominated based on eligibility.
Master the Tools to Excel in Computer Information Systems
The Data Analytics concentration is part of BU MET’s Master of Science in Computer Information Systems (MSCIS). 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.
BU MET’s Computer Information Systems master’s degree prepares you for jobs that are seeing faster-than-average growth and excellent salaries. According to the U.S. Bureau of Labor Statistics, the median annual wage for computer and information systems managers (for instance) is more than $146,000. And with seven concentrations, the Computer Information Systems master’s encompasses several other fast-growing and well-paid segments of the IT job market, providing the foundation for work as an application analyst, 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:
- Familiarity with applied probability and statistics, and their relevance in day-to-day data analysis.
- The ability to explore the various data visualization techniques and their applications using real-world data sets.
- An understanding of web analytics and metrics; how to procure and process unstructured text; and hidden patterns.
- Skills in facilitating knowledge discovery using data mining techniques over vast amounts of data.
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 two additional courses: Introduction to Probability and Statistics (MET CS 546 ) and either Information Structures with Java (MET CS 520) or Information Structures with Python (MET CS 521). To be eligible for the degree, you must apply for admission and be accepted into the degree program. Connect with a graduate admissions advisor at email@example.com to learn more about this option.
Master’s in Computer Information Systems Curriculum
A total of 40 credits is required.
Students who are declaring an MSCIS concentration in Data Analytics must complete the core and required concentration courses.
(Five courses/20 credits)
MET CS 625 Business Data Communication and Networks
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. ]Fall 2021
|A1||IND||Arena||STH 113||M||8:00 am – 10:45 am|
|A2||IND||Arena||EPC 204||T||6:00 pm – 8:45 pm|
|A1||IND||Arena||MCS B31||R||12:30 pm – 3:15 pm|
|A2||IND||Arena||SHA 210||R||6:00 pm – 8:45 pm|
|E1||IND||Arena||CAS 114B||R||6:00 pm – 8:45 pm|
MET CS 669 Database Design and Implementation for Business
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. ]Fall 2021
|A1||IND||Maiewski||CAS 229||W||6:00 pm – 8:45 pm|
|A2||IND||Russo||CAS 226||R||6:00 pm – 8:45 pm|
|A3||IND||Matthews||CAS 229||T||6:00 pm – 8:45 pm|
|E1||IND||Matthews||CAS 229||T||6:00 pm – 8:45 pm|
|A1||IND||Russo||CAS B36||M||6:00 pm – 8:45 pm|
|A2||IND||Maiewski||EPC 204||W||6:00 pm – 8:45 pm|
MET CS 682 Information Systems Analysis and Design
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. ]Fall 2021
|A1||IND||Guadagno||PHO 201||T||6:00 pm – 8:45 pm|
|A2||IND||Guadagno||SAR 300||R||6:00 pm – 8:45 pm|
|E1||IND||Guadagno||SAR 300||R||6:00 pm – 8:45 pm|
|A1||IND||Guadagno||FLR 121||W||6:00 pm – 8:45 pm|
|A2||IND||Guadagno||CAS B36||R||6:00 pm – 8:45 pm|
|E1||IND||Guadagno||CAS 427||W||6:00 pm – 8:45 pm|
MET CS 782 IT Strategy and Management
This course describes and compares contemporary and emerging information technology and its management. Students learn how to identify information technologies of strategic value to their organizations and how to manage their implementation. The course highlights the application of I.T. to business needs. CS 782 is at the advanced Masters (700) level, and it assumes that students understand IT systems at the level of CS 682 Systems Analysis and Design. Students who haven't completed CS 682 should contact their instructor to determine if they are adequately prepared. Prereq: MET CS 682, or instructor's consent. [ 4 cr. ]Fall 2021
|A1||IND||Arakelian||FLR 123||R||6:00 pm – 8:45 pm|
|A1||IND||Arakelian||KCB 103||R||6:00 pm – 8:45 pm|
|E1||IND||Arakelian||CAS 314||R||6:00 pm – 8:45 pm|
And one of the following:
MET CS 520 Information Structures with Java
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. Prereq: MET CS 200 or MET CS 300 or Instructor's Consent. Not recommended for students without a programming background. For undergraduate students: This course may not be taken in conjunction with METCS232. Only one of these courses can be counted towards degree requirements. [ 4 cr. ]Fall 2021
|A1||IND||Donald||HAR 220||M||6:00 pm – 8:45 pm|
|E1||IND||Donald||HAR 220||M||6:00 pm – 8:45 pm|
|A1||IND||Donald||CAS B06A||R||6:00 pm – 8:45 pm|
MET CS 521 Information Structures with Python
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. [ 4 cr. ]
|A1||IND||Lu||CGS 527||M||6:00 pm – 8:45 pm|
|A2||IND||Pinsky||KCB 104||W||8:00 am – 10:45 am|
|A3||IND||Burstein||CAS B36||W||6:00 pm – 8:45 pm|
|A4||IND||Aleksandrov||CAS 201||R||6:00 pm – 8:45 pm|
|A1||IND||Lu||KCB 103||M||6:00 pm – 8:45 pm|
|A2||IND||Burstein||PHO 205||T||6:00 pm – 8:45 pm|
|A3||IND||Pinsky||KCB 107||W||8:00 am – 10:45 am|
|E1||IND||Lu||WED 205||M||6:00 pm – 8:45 pm|
Students who have completed courses on core curriculum subjects as part of their undergraduate degree program or have relevant work-related experience 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.
Data Analytics Concentration Requirements
(Five courses/20 credits)
MET 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. ]Fall 2021
|A1||IND||Kalathur||CAS B20||M||6:00 pm – 8:45 pm|
|A2||IND||Kalathur||PHO 211||T||6:00 pm – 8:45 pm|
|A1||IND||Kalathur||EPC 205||M||6:00 pm – 8:45 pm|
|A2||IND||Kalathur||EPC 207||T||6:00 pm – 8:45 pm|
MET CS 546 Introduction to Probability and Statistics
The goal of this course is to provide students with the mathematical fundamentals required for successful quantitative analysis of problems. The first part of the course introduces the mathematical prerequisites for understanding probability and statistics. Topics include combinatorial mathematics, functions, and the fundamentals of differentiation and integration. The second part of the course concentrates on the study of elementary probability theory, discrete and continuous distributions. Prereq: Academic background that includes the material covered in a standard course on college algebra or instructor's consent. For undergraduate students: This course may not be taken in conjunction with MET MA 213, only one of these courses will count toward degree program requirements. Students who have taken MET MA 113 as well as MET MA 123 will also not be allowed to count MET CS 546 towards degree requirements. [ 4 cr. ]Fall 2021
|A1||IND||Gorlin||CAS 214||M||6:00 pm – 8:45 pm|
|A2||IND||Gorlin||HAR 316||T||6:00 pm – 8:45 pm|
|E1||IND||Gorlin||HAR 316||T||6:00 pm – 8:45 pm|
|A1||IND||Gorlin||CAS 326||M||6:00 pm – 8:45 pm|
|A2||IND||Gorlin||PSY B53||T||6:00 pm – 8:45 pm|
MET 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. ]Fall 2021
|A1||IND||Alizadeh-Sha||CAS 213||W||6:00 pm – 8:45 pm|
|A2||IND||Raghu||CAS 233||R||6:00 pm – 8:45 pm|
|A3||IND||Zhang||MET 122||R||9:00 am – 11:45 am|
|A1||IND||Alizadehshab||EPC 207||M||6:00 pm – 8:45 pm|
|A2||IND||Alizadehshab||EPC 205||T||6:00 pm – 8:45 pm|
|A3||IND||Alizadeh-Sha||EPC 205||R||6:00 pm – 8:45 pm|
MET CS 688 Web Analytics and Mining
The Web Analytics and Mining course covers the areas of web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of web sites, their content, user behavior, and reporting. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module studies how web crawlers process and index the content of web sites, how search works, and how results are ranked. Application areas mining the social web will be extensively investigated. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. [ 4 cr. ]Fall 2021
|A1||IND||Rawassizadeh||MET 122||T||9:00 am – 11:45 am|
|A2||IND||Vasilkoski||MUG 205||R||6:00 pm – 8:45 pm|
|A1||IND||Rawassizadeh||KCB 104||T||9:00 am – 11:45 am|
|A2||IND||Vasilkoski||STH 113||R||6:00 pm – 8:45 pm|
MET CS 699 Data Mining
The goal of this course is to study basic concepts and techniques of data mining. The topics include data preparation, classification, performance evaluation, association rule mining, and clustering. We will discuss basic data mining algorithms in the class and students will practice data mining techniques using data mining software. Students will use Weka and SQL Server or Oracle. Prereq: CS 546 and either CS 579 or CS 669. Or instructor's consent. [ 4 cr. ]Fall 2021
|A1||IND||Lee||SAR 102||W||6:00 pm – 8:45 pm|
|A1||IND||Lee||CGS 527||M||6:00 pm – 8:45 pm|
|A2||IND||Lee||CAS B36||W||6:00 pm – 8:45 pm|
|E1||IND||Lee||WED 210||M||6:00 pm – 8:45 pm|
Computer Science Faculty
View all Faculty
Associate Dean for Academic Affairs Associate Professor, Computer Science Director, Health Informatics & Health Sciences
Master Lecturer, Computer Science
Assistant Professor, Computer Science Director, Analytics
Associate Professor, Computer Science and Administrative Sciences Director, Project Management
Jae Young Lee
Assistant Professor, Computer Science Coordinator, Databases
Associate Professor of the Practice, Computer Science Coordinator, Software Development
Assistant Professor, Computer Science
Associate Professor Emeritus, Computer Science
Associate Professor Emeritus, Computer Science
Associate Professor, Computer Science Coordinator, Health Informatics
Assistant Professor, Computer Science
Assistant Professor, Computer Science Director, Cybersecurity
Dean, Metropolitan College & Extended Education Professor of the Practice, Computer Science and Education Director, Information Security