Online MS in Applied Data Analytics, Data Engineering

At a Glance

Completion Time

16–20

Months

Credits Required

32–40

Tuition

$27,920–$32,675

Part-Time*

*Based on 2026–2027 Boston University tuition and fees. Merit scholarship may reduce cost.

Build Expertise in Data Engineering and Advanced Analytics

The MS in Applied Data Analytics concentration in Data Engineering prepares you to design, build, and maintain scalable data pipelines, manage large datasets, and ensure data quality and security—skills essential for today’s data-driven organizations.

Through BU MET’s Data Engineering concentration, you will gain expertise in data ingestion, transformation, and processing using cutting-edge tools and techniques. The curriculum covers databases, applied machine learning, large-scale data processing, and data visualization within a rigorous academic framework. This comprehensive training equips you to support data analytics workflows that drive informed business decisions.

As a graduate of the Applied Data Analytics master’s concentration in Data Engineering, you will be prepared for advanced roles such as data engineer, analytics engineer, and data architect. Additionally, you will build strong predictive modeling and analytical skills sought after by employers in diverse industries such as finance, healthcare, technology, and telecom.

The MS in Applied Data Analytics, Data Engineering concentration is also available on campus in Boston. Learn more.

Curriculum

A total of ten courses (40 units) is required. Students exempted from the foundation courses will complete a total of eight courses (32 units).

Students must complete the foundation courses (unless exempted), core courses, and Data Engineering concentration requirements.

Preparatory Labs

Foundation Courses

(Two courses/8 units)

Qualified students may be exempt from one or both foundation courses based on previous academic background in information technology, computer science, and mathematics. Applicants will be notified of their curriculum requirements upon admission. If foundation courses are assigned, they must be completed within the first semester of study.

MET CS 521 Information Structures with Python

4 credits. Fall and Spring

Creativity/Innovation
Critical Thinking
Quantitative Reasoning II

Prerequisite: Programming experience in any language. Or Instructor's consent. Explore the object-oriented approach to software design and development using Python. You will engage in a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceed to more advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams. Upon completion of this course, you will be able to apply software engineering principles to design and implement Python applications that can be used with analytics and big data. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Creativity/Innovation, Critical Thinking, Quantitative Reasoning 2.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Independent	M	06:00:00 PM–08:45:00 PM	COM 217

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Lu	Independent	T	06:00:00 PM–08:45:00 PM	SHA 206

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Mohan	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Pinsky	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 526 Data Structures and Algorithms

4 credits. Fall and Spring

Prerequisites: MET LB 110 lab and either MET CS520 or MET CS521, or consent of instructor. Learn fundamental components of programs using various data structures to solve computational problems, and implement data structures using a high-level programming language. Algorithms will be created, decomposed, and expressed as pseudocode, and you will analyze their running time and computational complexity.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Mellor	Independent	M	06:00:00 PM–08:45:00 PM	SCI 115

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Doucette	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Burstein	Independent	ARR	12:00:00 AM–12:00:00 AM

Core Courses

(Four courses/16 units)

MET CS 555 Foundations of Machine Learning

4 credits. Fall and Spring

Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. You will learn the foundations of statistical machine learning, regression, and classification, and explore the key components of statistical models, including how to construct, interpret, and evaluate them. Topics include data description and visualization, statistical inference, one- and two-sample tests for means and proportions, simple and multiple linear regression, multinomial and logistic regression, analysis of variance (ANOVA), and regression diagnostics. For each topic, you will examine the methodology, underlying assumptions, interpretation of results, and model assessment. The course includes a programming component using R or Python, providing hands-on experience that reinforces theoretical concepts. Methods are presented through real-world examples to help you understand when and how to apply different statistical techniques effectively.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	R	06:00:00 PM–08:45:00 PM	SHA 202

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 577 Data Science with Python

4 credits.

Prerequisite: (MET CS 521) or equivalent or instructor's consent. 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.

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Mohan	Independent	R	06:00:00 PM–08:45:00 PM	EPC 206

Section A4, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Independent	T	09:00:00 AM–11:45:00 AM	STH 113

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Mohan	Independent	ARR	12:00:00 AM–12:00:00 AM

Plus one course from the following:

MET CS 544 Foundations of Analytics and Data Visualization

4 credits. Fall and Spring

Prerequisites: MET LB 103, MET LB 104, and (METCS 520 or METCS 521), or equivalent knowledge, or consent of instructor. 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 methods. Data populations using discrete, continuous, and multivariate distributions are explored. Sampling methods and errors during measurements and computations are analyzed in the course. String manipulations and data wrangling methods are examined in detail. The concepts covered in the course are demonstrated using R. Laboratory Course. Restrictions: This course may not be taken in conjunction with MET CS 550.

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Rizinski	Independent	T	06:00:00 PM–08:45:00 PM	CAS 233

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Kalathur	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Kalathur	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 550 Computational Mathematics for Machine Learning

4 credits. Fall and Spring

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. Restrictions: This course may not be taken in conjunction with MET CS 544.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Independent	T	06:00:00 PM–08:45:00 PM	CAS 204A

And one course from the following*:

MET CS 688 Web Mining and Graph Analytics

4 credits. Fall and Spring

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.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Hajiyani	Independent	T	06:00:00 PM–08:45:00 PM	CAS 228

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Vasilkoski	Independent	R	06:00:00 PM–08:45:00 PM	HAR 220

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 699 Data Mining

4 credits. Fall and Spring

Prerequisites: MET CS 521, MET LB 103 and MET LB 104; and either 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. You will learn underlying theories of data mining algorithms in the class and practice those algorithms through assignments and a semester-long class project using R. After finishing this course, you will be able to independently perform data mining tasks to solve real-world problems.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Lee	Independent	W	06:00:00 PM–08:45:00 PM	SOC B63

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Lee	Independent	ARR	12:00:00 AM–12:00:00 AM

*If choosing to take both MET CS 688 and MET CS 699, one will be counted as a core course and the other as a concentration requirement.

Concentration Requirements

(Four courses/16 units)

Choose four courses from the following:

MET CS 669 Database Design and Implementation for Business

4 credits.

Learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. You will gain extensive hands-on experience using Oracle or Microsoft SQL Server as you learn the Structured Query Language (SQL) and design and implement databases. You will 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.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Diwania	Independent	W	06:00:00 PM–08:45:00 PM	CAS 315

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Russo	Independent	R	06:00:00 PM–08:45:00 PM	FLR 121

Section E1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Diwania	Independent	W	06:00:00 PM–08:45:00 PM	CAS 315

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Lee	Independent	ARR	12:00:00 AM–12:00:00 AM

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Mansur	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 674 Database Security

4 credits. Fall and Spring

The course provides a strong foundation in database security and auditing by utilizing Oracle scenarios and step-by-step examples. The following topics are covered: security, profiles, password policies, privileges, roles, Virtual Private Databases, and auditing. The course also covers advanced topics such as SQL injection, database management, and security issues, such as securing the DBMS, enforcing access controls, and related issues.

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 688 Web Mining and Graph Analytics

4 credits. Fall and Spring

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Hajiyani	Independent	T	06:00:00 PM–08:45:00 PM	CAS 228

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Vasilkoski	Independent	R	06:00:00 PM–08:45:00 PM	HAR 220

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Rawassizadeh	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 689 Designing and Implementing a Data Warehouse

4 credits. Fall and Spring

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.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Polnar	Independent	M	06:00:00 PM–08:45:00 PM	SOC B57

MET CS 699 Data Mining

4 credits. Fall and Spring

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Lee	Independent	W	06:00:00 PM–08:45:00 PM	SOC B63

Section O2, FALL 2026 Oct 27th to Dec 14th
Instructor	Type	Days	Times	Location
Lee	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 777 Big Data Analytics

4 credits. Fall and Spring

Prerequisites: (MET CS 521 & MET CS 544 & MET CS 555) or MET CS 577 or consent of instructor. An overview of the principles and practice of large-scale data analytics. You will examine methods for extracting meaningful insights from large, complex, and distributed datasets, learning about core technologies for storing and processing high-volume data. This course emphasizes distributed computing frameworks based on the MapReduce paradigm, including Hadoop MapReduce and Apache Spark, along with programming models, parallel data processing, and performance considerations in cluster-based environments. Through hands-on assignments and projects, you will implement data processing algorithms and deploy them on cloud platforms such as Amazon Web Services (AWS) and Google Cloud, developing the practical skills required for data engineering and large-scale analytics in real-world environments. Educational cloud accounts and credits are provided.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Alizadeh-Shabdiz	Independent	W	06:00:00 PM–08:45:00 PM	CAS 218

Section O1, FALL 2026 Sep 1st to Oct 19th
Instructor	Type	Days	Times	Location
Trajanov	Independent	ARR	12:00:00 AM–12:00:00 AM

MET CS 779 Advanced Database Management

4 credits. Fall and Spring

Prerequisites: METCS579 or METCS669 or consent of the instructor - Investigate 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. 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, are covered. You will learn about unstructured "big data" architectures and databases, gain hands-on experience with Spark and MongoDB, and complete a term project exploring an advanced database technology of your choice.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Polnar	Independent	R	06:00:00 PM–08:45:00 PM	CAS 324

Master’s Thesis Option

(Two courses/8 units)

Students have the option to complete a master’s thesis by taking two Master Thesis courses (8 units) in addition to the program’s ten course (40 units) requirement. The thesis must be completed within 12 months and is available to MS in Applied Data Analytics candidates who have completed at least four courses toward their degree (not including foundation courses) and have a grade point average (GPA) of 3.7 or higher. Students are responsible for finding a thesis advisor and principal readers within the department. The advisor must be a full-time faculty member; the principal readers may be part-time faculty. Department approval is required.

MET CS 810 Master's Thesis 1

4 credits. Fall and Spring

This is the first course of the two-part thesis option available to Master’s degree program candidates in the Department of Computer Science. You must have completed at least four courses toward your degree and have a grade point average (GPA) of 3.7 or higher. You are responsible for finding a thesis advisor and a principal reader within the department. Please refer to the Department for further details on the application process. Both MET CS 810 Master’s Thesis 1 and MET CS 811 Master’s Thesis 2 must be completed within 12 months.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Rawassizadeh	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A3, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Pinsky	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A4, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A5, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

MET CS 811 Master's Thesis 2

4 credits. Fall and Spring

This is the second course of the two-part thesis option available to Master’s degree program candidates in the Department of Computer Science. You must have completed at least four courses toward your degree and have a grade point average (GPA) of 3.7 or higher. You are responsible for finding a thesis advisor and a principal reader within the department. Please refer to the Department for further details on the application process. Both METCS 810 Master’s Thesis 1 and METCS 811 Master’s Thesis 2 must be completed within 12 months.

Section A1, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Section A2, FALL 2026 Sep 2nd to Dec 10th
Instructor	Type	Days	Times	Location
Zhang	Directed Study	ARR	12:00:00 AM–12:00:00 AM

Dates & Deadlines

View BU MET’s academic calendar for online programs, including important dates and deadlines.

Application Requirements

Learn about application requirements for BU MET graduate degree and certificate programs.

Students at Metropolitan College Commencement lining up to graduate

How You Benefit from a
Boston University Education

A BU credential can help lay the foundation for career advancement and personal success.

Benefit from an average 24:1 student-to-instructor ratio.
Learn from 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.
Gain in-depth, practical experience with the latest technologies through case studies and real-world projects.
Experience a supportive online network, with courses developed and taught by PhD-level full-time faculty and professionals with hands-on expertise in the industry.
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 the course of its existence, the department has played an important role in the emergence of IT at the University and throughout the region.

All graduate students are automatically considered for merit scholarships during the application process and nominated based on eligibility. Learn more.

Rankings & Accreditations

U.S. News & World Report - Best Online Programs - Grad Information Technology - 2026

#12, Best Online Master’s in Computer Information Technology Programs

MET’s online master’s degrees in computer information technology are ranked #12 in the nation by U.S. News & World Report.

#2 Best Online Master’s in Data Analytics of 2026

BU MET’s MS in Applied Data Analytics is ranked #2 Best Online Master’s in Data Analytics Degree Programs for 2026 by TechGuide.

Graduate with Data Engineering Expertise

In addition to the learning outcomes derived from Metropolitan College’s Applied Data Analytics master’s degree program, the concentration in Data Engineering will equip you with:

The know-how of database modeling and design, implementation, distributed databases, object-oriented and object-relational databases, databases for web applications, and typical data-mining methods.
Proficiency in designing, implementing, and performance-tuning different types of databases, as well as performing data-mining tasks on various data types.
The comprehension of computing concepts and applications requirements involving massive computing needs and data storage.

$103,500

Median annual wage for Data Scientists, May 2023

Source: Bureau of Labor Statistics

“This program led me to my current position as a data scientist at Boston’s Massachusetts General Hospital, where I implement machine learning models to improve the hospital’s operational efficiency and support physicians with clinical research. I am immensely happy with my investment in graduate school, as it led me to many amazing opportunities!” Read more.

Melissa Viator (MET’23)
Data Scientist, Massachusetts General Hospital
MS, Applied Data Analytics

Advance Your Career

BU MET’s Applied Data Analytics master’s concentration in data engineering prepares you for a wealth of different roles, such as data engineer, analytics engineer, data architect, big data engineer, ETL (extract, transform, load) developer, data pipeline engineer, database administrator (DBA), cloud data engineer, machine learning engineer (with a focus on data infrastructure), business intelligence engineer, data operations specialist, and data platform engineer, among others.

Recent graduates have found job opportunities and career paths at companies such as:

Fidelity Investments
Akamai Technologies
Amazon Web Services (AWS)
Chi Alpha Campus Ministries
Deloitte Consulting

Drew University
McKesson
Olympus Americas
Travelers
Turiyatree Technologies

Take Advantage of Career Resources at BU MET

You will find the support you need in reaching your career goals through MET’s Career Development office, which offers a variety of job-hunting resources, including one-on-one career counseling by appointment for online students. You can also take advantage of tools and resources available online through BU’s Center for Career Development.

Computer Science Faculty

Guanglan Zhang

Associate Professor, Computer Science

Coordinator, Health Informatics Programs

Chair, Computer Science

Shengzhi Zhang

Associate Professor

Associate Chair, Computer Science

Coordinator, Web Application Development

Suresh Kalathur

Assistant Professor, Computer Science

Director, Analytics

Farshid Alizadeh Shabdiz

Professor of the Practice, Computer Science

View All Faculty

Interested in Learning More?

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Online Master of Science in Applied Data Analytics, Data Engineering Concentration

At a Glance

Build Expertise in Data Engineering and Advanced Analytics