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
- 32 Credits
- 8–16 Months to Completion
- 17 Core Faculty
- No GRE/GMAT
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.
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’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.
- 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.
- 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 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.
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. 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.
Master’s in Applied Data Analytics Curriculum
A total of 32 credits is required.
(Six courses/24 credits)
MET CS 544 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 cr. ]
|SA1||IND||Zhang||CGS 515||MW||6:00 pm – 9:30 pm|
|A1||IND||Kalathur||CAS 326||M||6:00 pm – 8:45 pm|
|A2||IND||Kalathur||CAS 237||T||6:00 pm – 8:45 pm|
|A3||IND||Staff||EPC 208||T||6:00 pm – 8:45 pm|
MET CS 555 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 cr. ]
|SB1||IND||Raghu||MET 122||MW||6:00 pm – 9:30 pm|
|A1||IND||Staff||EPC 208||M||6:00 pm – 8:45 pm|
|A2||IND||Alizadeh-Sha||CAS 213||R||6:00 pm – 8:45 pm|
|A3||IND||Alizadeh-Sha||MET 122||R||9:00 am – 11:45 am|
MET CS 566 Analysis of Algorithms
Discusses basic methods for designing and analyzing efficient algorithms emphasizing methods used in practice. Topics include sorting, searching, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, string matching, NP completeness. Prereq: MET CS248 and either MET CS341 or MET CS342. Or METCS 521 and METCS 526. Or instructor's consent. [ 4 cr. ]Sum1 2022
|SC1||IND||Naidjate||BRB 122||R||6:00 pm – 9:30 pm|
|A1||IND||Belyaev||CAS 213||W||6:00 pm – 8:45 pm|
|A2||IND||Belyaev||CAS 227||R||6:00 pm – 8:45 pm|
|A3||IND||Staff||FLR 152||R||6:00 pm – 8:45 pm|
MET CS 677 Data Science with Python
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. ]Sum1 2022
|SC1||IND||Alizadehshab||CAS 233||R||6:00 pm – 9:30 pm|
|A1||IND||Staff||CAS 226||M||6:00 pm – 8:45 pm|
|A2||IND||Enxing||CAS 233||T||6:00 pm – 8:45 pm|
|A3||IND||Pinsky||EPC 208||W||6:00 pm – 8:45 pm|
MET CS 688 Web Mining and Graph Analytics
Formerly titled 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. ]
|SC1||IND||Vasilkoski||CAS 237||R||6:00 pm – 9:30 pm|
|A1||IND||Vasilkoski||MET 122||T||6:00 pm – 8:45 pm|
|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. ]Sum1 2022
|SC1||IND||Lee||HAR 302||T||6:00 pm – 9:30 pm|
|A1||IND||Lee||EPC 204||W||6:00 pm – 8:45 pm|
|A2||IND||Lee||PSY B33||R||6:00 pm – 8:45 pm|
(Two courses/8 credits)
Choose two electives from the following list (some courses may not be available in the online format):
MET CS 689 Designing and Implementing a Data Warehouse
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. ]
|A1||IND||Russo||STH 113||M||6:00 pm – 8:45 pm|
MET CS 767 Advanced Machine Learning and Neural Networks
Formerly titled CS767 Machine Learning
Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Neural Nets and Deep Learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. Each student focuses on two of these approaches and creates a term project. Laboratory course. Prerequisite: MET CS 521 and either MET CS 622, MET CS 673 or MET CS 682. MET CS 677 is strongly recommended. Or, instructor's consent. [ 4 cr. ]
|SC1||IND||Alizadeh-Sha||SOC B57||T||6:00 pm – 9:30 pm|
|A1||IND||Djordjevic||CAS 229||R||6:00 pm – 8:45 pm|
|A2||IND||Rawassizadeh||FLR 121||T||12:30 pm – 3:15 pm|
MET CS 777 Big Data Analytics
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. ]
|SB1||IND||Trajanov||CAS 208||TR||6:00 pm – 9:30 pm|
|A1||IND||Alizadeh-Sha||HAR 212||W||6:00 pm – 8:45 pm|
MET CS 779 Advanced Database Management
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. ]Sum2 2022
|A1||IND||Polnar||CAS 204A||R||6:00 pm – 8:45 pm|
MET MA 582 Mathematical Statistics
Interval estimation. Point estimation including sufficiency, Rao-Blackwell theorem, completeness, uniqueness, Rao-Cramer inequality, and maximum likelihood estimation. Tests of hypothesis: uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, chi-squared test, comparison of means and variances, ANOVA, regression, and some nonparametric tests. [ 4 cr. ]
Computer Science Faculty
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Senior 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 and Associate Chair 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, Center for Reliable Information Systems & Cyber Security