Online Master of Science in Applied Data Analytics
With data analytics needs influencing every major industry—including health care, tech, finance, communication, entertainment, energy, transportation, government, and manufacturing, to name some—there is significant growth in specialized data science and machine learning areas. The demand for skilled talent continues to outpace supply, with McKinsey Global Institute anticipating a shortfall of up to 250,000 data scientists through the decade.
Ideal for mid-career IT professionals or students with a computer science background who seek to train their focus on analytics, the Master of Science in Applied Data Analytics (MSADA) program 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 MSADA curriculum provides a thorough immersion in concepts and techniques for organizing, cleaning, analyzing, and representing/visualizing large amounts of data. Students will be exposed 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 enable students to critically analyze real-world problems and understand the possibilities and limitations of analytics applications.
Students who complete the master’s degree in Applied Data Analytics will be able to demonstrate:
- Knowledge of the foundations of applied probability and statistics and their relevance in day-to-day data analysis.
- Comprehension of computing concepts and applications 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.
Awards & Accreditations
Newsweek magazine ranked Boston University’s online programs #4 in the nation in its 2023 survey.
Why Choose BU’s Master of Science in Applied Data Analytics?

- In 2025, the MSADA ranked #10 among the Best Online Master's in Computer Information Technology Programs (U.S. News & World Report).
- Hands-on class projects enable students to build a portfolio of analytics-focused work while providing practical data analytics skills that are immediately applicable on the job in a variety of industries.
- Students benefit from a supportive online network as well as research opportunities, with courses developed and taught by PhD-level, full-time faculty and professionals with years of expertise in the industry.
- Small course sections ensure that students get the attention they need, while case studies and real-world projects provide in-depth, practical experience with the latest technologies.
Meet Dr. Suresh Kalathur, director of analytics programs, and one of the faculty members with whom you’ll work in the Applied Data Analytics program.
Career Outlook
Computer and Information Research Scientists
23% increase in jobs through 2032
$136,620 median annual pay in 2022
Computer Systems Analysts
10% increase in jobs through 2032
$102,240 median annual pay in 2022
Database Administrators and Architects
8% increase in jobs through 2032
$112,120 median annual pay in 2022
Data Scientists
35% increase in jobs through 2032
$103,500 median annual pay in 2022
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, at https://www.bls.gov/ooh/computer-and-information-technology/home.htm (visited October 03, 2023).
Best Technology Jobs, 2025 U.S. News & World Report
- #1 IT Manager
- #2 Software Developer
- #3 Information Security Analyst
- #4 Data Scientist
- #5 Actuary
- #6 Computer Network Architect
- #7 Operations Research Analyst
- #8 Computer Systems Analyst
- #9 Statistician
- #10 Web Developer
- #11 Database Administrator
- #12 Computer Support Specialist
- #13 Mathematician
- #14 Computer Systems Administrator
- #15 Computer Programmer
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 Applied Data Analytics consists of eight courses (32 credits).
Courses
Core Curriculum
(Six courses/24 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]
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]
METCS566 Analysis of Algorithms
Undergraduate Prerequisites: (CS341 or CS342 or CS526) or instructor's consent - earn basic methods for designing and analyzing efficient computer algorithms and practice hands-on programming skills. Topics include sorting, searching, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, string matching, and NP-completeness. [4 credits]
METCS677 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 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]
General Electives
(Two courses/8 credits)
Choose two electives from the following list:
METCS550 Computational Mathematics for Machine Learning
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 credits]
METCS689 Designing and Implementing a Data Warehouse
Graduate Prerequisites: CS 579 or CS 669 or consent of the instructor - 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 credits]
METCS767 Advanced Machine Learning and Neural Networks
Graduate 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, Recurrent Neural Nets, Rule-learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptrons, backpropagation, attention, and transformers. Each student focuses on two of these approaches and creates a term project. [4 credits]
METCS777 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 credits]
METCS779 Advanced Database Management
Graduate Prerequisites: (METCS579 OR METCS669) or consent of the instructor - 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 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.
Applicants without prior background in information technology, computer science, and mathematics are expected to take Introduction to Software Development (MET CS 300) and the following prerequisite courses:
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]
*Students are strongly encouraged to take MET CS 521, unless they already have a Python background and want to expand their Java skills.
Plus:
METCS526 Data Structures and Algorithms
This course covers and relates fundamental components of programs. Students use various data structures to solve computational problems, and implement data structures using a high-level programming language. Algorithms are created, decomposed, and expressed as pseudocode. The running time of various algorithms and their computational complexity are analyzed. Prerequisite: MET CS300 and either MET CS520 or MET CS521, 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]
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]
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)
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
Suresh Kalathur
Assistant Professor, Computer Science; Director, Analytics
PhD, Brandeis University; MS, Indian Institute of Technology; BS, Regional Engineering College (Warangal, India)
View all Faculty
Jae Young Lee
Assistant Professor, Computer Science; Coordinator, Databases
PhD, MS, University of Texas at Arlington; BS, Seoul National University (Korea)
Eugene Pinsky
Associate Professor of the Practice, Computer Science; Coordinator, Software Development
PhD, Columbia University; BA, Harvard University
Robert Schudy
Associate Professor, Computer Science
PhD, MS, University of Rochester; BA, University of California San Diego
Anatoly Temkin
Assistant Professor Emeritus, Computer Science
PhD, Kazan University (Russia); MS, Moscow University
Kia Teymourian
Assistant Professor, Computer Science; Coordinator, Programming Languages
PhD, Freie Universität Berlin; MS, BS, Berlin University of Technology (TU-Berlin)
Guanglan Zhang
Associate Professor and Chair, Computer Science; Coordinator, Health Informatics
PhD, MEng, Nanyang Technological University, Singapore; BS, Luoyang Institute of Technology
Shengzhi Zhang
Associate Professor, Computer Science
PhD, Penn State University; BS, Tongji University (Shanghai, China)
Yuting Zhang
Assistant Professor, Computer Science; Coordinator, Information Security
PhD, Boston University; MS, BS University of Science and Technology Beijing
Getting Started
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