Online Master of Science in Computer Information Systems Concentration in Data Analytics
The MS in Computer Information Systems (MSCIS) concentration in Data Analytics will provide professionals with the skills required to compete for jobs as data scientists, analysts, architects, and engineers amid rising global demand. The concentration will explore the intricacies of data analytics and expose students to various topics related to data processing, analysis, and visualization. Along with probability theory and statistical analysis methods and tools, students 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 of practice of information technology gained from the Computer Information Systems core courses, individuals 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.
Students who complete the MSCIS degree concentration in Data Analytics will be able to demonstrate:
- 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.
Why Choose BU’s Master of Science in Computer Information Systems?
In 2024, the MSCIS ranked #8 among the Best Online Master's in Computer Information Technology Programs (U.S. News & World Report).- Students benefit from a supportive online network, with courses developed and taught by PhD-level full-time faculty and professionals with hands-on expertise in the industry.
- Small course sections ensure that students get the attention they need, while case studies and real-world projects ensure that they gain in-depth, practical experience with the latest technologies.
Meet Dr. Suresh Kalathur, one of the faculty members you’ll work with in the Computer Information Systems program.
Career Outlook
Computer and Information Systems Managers
10% increase in jobs through 2029
$146,360 median annual pay in 2019
Computer and Information Research Scientists
15% increase in jobs through 2029
$122,840 median annual pay in 2019
Computer Systems Analysts
7% increase in jobs through 2029
$90,920 median annual pay in 2019
Database Administrators
10% increase in jobs through 2029
$93,750 median annual pay in 2019
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2020
Best Technology Jobs, 2024 U.S. News & World Report
- #1 Software Developer
- #2 IT Manager
- #3 Information Security Analyst
- #4 Data Scientist
- #5 Web Developer
- #6 Computer Systems Analyst
- #7 Computer Network Architect
- #8 Database Administrator
- #9 Computer Support Specialist
- #10 Computer Systems Administrator
- #11 Computer Programmer
By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
McKinsey & Company Big data: The Next Frontier for Innovation, Competition, and Productivity, 2011
Tuition & Financial Assistance
Money Matters
Boston University Metropolitan College (MET) offers competitive tuition rates that meet the needs of part-time students seeking an affordable education. These rates are substantially lower than those of the traditional, full-time residential programs yet provide access to the same high-quality BU education. To learn more about current tuition rates, visit the MET website.
Financial Assistance
Comprehensive financial assistance services are available at MET, including scholarships, graduate loans, and payment plans. There is no cost to apply for financial assistance, and you may qualify for a student loan regardless of your income. Learn more.
Curriculum
The online Master of Science in Computer Information Systems consists of ten courses (40 credits).
Courses
Students pursuing the concentration in Data Analytics must complete the following courses:
MSCIS Core Courses
(Five courses/20 credits)
METCS625 Business Data Communication and Networks
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 credits]
METCS669 Database Design and Implementation for Business
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 credits]
METCS682 Information Systems Analysis and Design
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 credits]
METCS782 IT Strategy and Management
Undergraduate Prerequisites: Restrictions: Only for MS CIS students. - 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 credits]
And one of the following*:
METCS520 Information Structures with Java
Undergraduate Prerequisites: Prerequisites: MET CS 201, Introduction to Programming (On Campus and Blended); MET CS 200, Fundamentals of Information Technology (Online O nly) - 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 credits]
METCS521 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.
Prerequisite: Programming experience in any language. Or Instructor's consent. [4 credits]
*If a student chooses to take both MET CS 520 and MET CS 521, the first course completed will fulfill the core requirement and the second course completed will count as an elective.
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 the MET CS Academic Policies Manual for further details.
Concentration Requirements
(Five courses/20 credits)
METCS544 Foundations of Analytics and Data Visualization
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 credits]
METCS546 Introduction to Probability and Statistics
Undergraduate Prerequisites: Academic background that includes the material covered in a standard c ourse on college algebra. - 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 credits]
METCS555 Foundations of Machine Learning
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 credits]
METCS688 Web Mining and Graph Analytics
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 credits]
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 credits]
Admission & Prerequisite Information
Admissions
Visit the Metropolitan College Graduate application page to learn more and apply.
Prerequisites
Applicants are not required to have a degree in computer science for entry to a program within the Department of Computer Science. Upon review of your application, the department will determine if the completion of prerequisite coursework will be required, based on your academic and professional background. The following prerequisite courses may be required:
METCS200 Introduction to Computer Information Systems
This course is a technically-oriented introductory survey of information technology. Students learn about basic computer information, different types of business systems and basic systems analysis, design and development. Students also study basic mathematics, software development and create simple Java programs. [4 credits]
A maximum of two graduate-level courses (8 credits) taken at Metropolitan College before acceptance into the program may be applied towards the degree.
Eric Braude
Associate Professor and Director of Digital Learning, Computer Science
PhD, Columbia University; MS, University of Miami; MS, University of Illinois; BS, University of Natal (South Africa)
Areas of Specialization: Software Engineering with Object-Oriented Methods; Software Design
Lou Chitkushev
Associate Dean, Academic Affairs; Associate Professor, Computer Science; Director, Health Informatics and Health Sciences
PhD, Boston University; MS, Medical College of Virginia; MS, BS, University of Belgrade
John Day
Lecturer, Computer Science
MSEE, BSEE, University of Illinois
View all Faculty
Stu Jacobs
Lecturer, Computer Science
MS, Southern Connecticut State University; BS, University of Wisconsin, Madison
Suresh Kalathur
Assistant Professor, Computer Science; Director, Analytics
PhD, Brandeis University; MS, Indian Institute of Technology; BS, Regional Engineering College (Warangal, India)
Vijay Kanabar, PMP
Associate Professor, Computer Science and Administrative Sciences; Director, Project Management
PhD, University of Manitoba (Canada); MS, Florida Institute of Technology; MBA, Webber College; BS, University of Madras (India)
Jae Young Lee
Assistant Professor, Computer Science; Coordinator, Databases
PhD, MS, University of Texas at Arlington; BS, Seoul National University (Korea)
Robert Schudy
Associate Professor, Computer Science
PhD, MS, University of Rochester; BA, University of California San Diego
Victor Shtern
Associate Professor Emeritus, Computer Science
PhD, Leningrad Aluminum Institute (Russia); MS, Leningrad Institute of Technology; MBA, Boston University
Anatoly Temkin
Assistant Professor Emeritus, Computer Science
PhD, Kazan University (Russia); MS, Moscow University
Guanglan Zhang
Associate Professor and Chair, Computer Science; Coordinator, Health Informatics
PhD, MEng, Nanyang Technological University, Singapore; BS, Luoyang Institute of Technology
Yuting Zhang
Assistant Professor, Computer Science; Coordinator, Information Security
PhD, Boston University; MS, BS University of Science and Technology Beijing
Tanya Zlateva
Dean, Metropolitan College; Professor of the Practice, Computer Science and Education; Director, Information Security
PhD, Dresden University of Technology (Germany); MS, Dresden University of Technology; BS, Dresden University of Technology
Getting Started
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Master of Science