Program at a Glance
- Online and On Campus
- Part-Time Study
- 16 Credits
- 8–12 Months
- 17 Core Faculty
- No GRE/GMAT
Analyze Big Data to Inform Decision-Making
Available on campus and online, the Graduate Certificate in Data Analytics at Boston University’s Metropolitan College (MET) is designed to provide professionals with the skills required to compete for data analysis jobs amid rising global demand.
Given the tremendous growth of digital data—and the desire of enterprises to derive more and more value from it—data analytics talent is increasingly sought-after by industry. A joint report by PwC and the Business-Higher Education Forum indicates that, by 2021, 69 percent of employers will seek candidates with data science and analytics skills.
BU MET’s Data Analytics graduate certificate program will prepare you to take on roles as a data analyst or data scientist, among other opportunities. The program will explore the intricacies of data analytics and expose you to various topics related to data processing, analysis, and visualization. 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. Those who complete this program will be able to demonstrate solid knowledge of data analytics practices and the methods and tools for data mining and knowledge discovery.
Explore Careers in Data Analytics
Use the Career Insights tool to explore jobs that are the right fit for you. Filter by career area and job title or by industry sector to explore employment demand and average salaries. Select “Learn More” for a downloadable career report, or “Explore Other Options” to find the BU MET degree or certificate program that will prepare you for the job you want.
Why BU Should Be Your Top Choice for Computer Science Graduate Study?
- Recognized & Certified: Boston University is recognized by the National Security Agency and the Department of Homeland Security as a Center of Academic Excellence (CAE) in Cyber Defense and Research. BU MET’s information security programs are certified by the Committee on National Security Systems (CNSS).
- Active Learning Environment: BU MET’s computer science courses ensure you get the attention you need, while introducing case studies and real-world projects that emphasize technical and theoretical knowledge—combining in-depth, practical experience with the critical skills needed to remain on the forefront of the information technology field. In addition, you have access to BU MET’s Health Informatics Research Lab (HILab), which focuses on collaborative research and development in health informatics, bioinformatics, and clinical research.
- Engaged Faculty: In BU MET’s computer science graduate programs, you benefit from working closely with highly qualified faculty and seasoned industry leaders in a wide range of technology fields who are committed to teaching the latest technologies within the framework of ideas, concepts, and methods that drive innovation.
- Extensive Network: Study alongside peers and professionals with solid IT experience, learn from faculty who have valuable 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.
Prepare for the Future of Technology with BU MET
The four-course Data Analytics graduate certificate is part of BU MET’s portfolio of computer science and IT graduate programs. For over forty years, the Department of Computer Science at Metropolitan College has prepared students to tackle contemporary challenges in the field. Our programs are uniquely flexible—we offer courses evenings on campus, fully online, or in a blended format that combines online study with occasional campus visits—so you can balance graduate school with your career, family, and other obligations. We take pride in providing training in critical specialization areas and emphasizing practical, insightful, and adaptable knowledge that can be immediately applied on the job while informing your career growth for years to come. We also offer extensive advising to help you identify the subjects you’ll need to achieve your career goals.
Our degree programs are certified by the Committee on National Security Systems (CNSS)—the MS in Computer Information Systems has additional accreditation from the Project Management Institute Global Accreditation Center for Project Management Education Programs (GAC) and the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM). Boston University is designated a Center of Academic Excellence (CAE) in Cyber Defense and Research by the National Security Agency and Department of Homeland Security.
Gain Expertise in Data Analytics
Metropolitan College’s Graduate Certificate 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 earn the master’s in Computer Information Systems with a concentration in Data Analytics by completing the Graduate Certificate in Data Analytics and the Graduate Certificate in Information Technology, 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 firstname.lastname@example.org to learn more about this option.
Data Analytics Graduate Certificate Curriculum
(Four courses/16 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||M||6:00 pm – 8:45 pm|
|A2||IND||Kalathur||EPC||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||M||6:00 pm – 8:45 pm|
|A2||IND||Alizadehshab||EPC||T||6:00 pm – 8:45 pm|
|A3||IND||Alizadeh-Sha||EPC||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||STH||T||12:30 pm – 3:15 pm|
|A2||IND||Staff||STH||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||M||6:00 pm – 8:45 pm|
|A2||IND||Lee||CAS||W||6:00 pm – 8:45 pm|
|E1||IND||Lee||WED||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 Coordinator, Information Security
Dean, Metropolitan College & Extended Education Professor of the Practice, Computer Science and Education Director, Information Security