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—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.
Offered on campus and online, the Master of Science in Applied Data Analytics (MSADA) program at Boston University’s Metropolitan College (MET) is ideal for mid-career IT professionals or students with a computer science background who seek to train their focus on analytics. Students gain solid knowledge of data analytics and examine 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 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 such as Amazon AWS, Google Cloud, and Mass Open Cloud.
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.
Available on campus and in the following formats:
Admission & Prerequisite Information
MET prioritizes the review and admission of applications submitted earlier in the rolling admission process. You are encouraged to submit your application as soon as possible and no later than the priority application deadlines for each term.
Applicants must have an earned bachelor’s degree, in any field of study, from a regionally accredited college/university (or the international equivalent) prior to enrollment at Metropolitan College. The following materials are required for a complete application:
- Completed Application for Graduate Admission and application fee
- All college transcripts
- Personal statement
- Three letters of recommendation
- Official English proficiency exam results (International students)
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.
Those without prior background in information technology, computer science, and mathematics are expected to take Introduction to Software Development (MET CS 300) (offered online only) and the following prerequisite courses:
One of the following*:
MET CS 520 Information Structures with Java
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. [ 4 cr. ]
|A1||IND||Donald||STH 113||R||6:00 pm – 8:45 pm|
MET CS 521 Information Structures with Python
This course covers the concepts of the object-oriented approach to software design and development using the Python programming language. 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 capable of applying software engineering principles to design and implement Python applications that can be used in conjunction with analytics and big data. Prerequisite: MET CS 300, or instructor's consent. [ 4 cr. ]
|A1||IND||Lu||PSY B53||M||6:00 pm – 8:45 pm|
|A3||IND||Pinsky||STH B19||W||2:30 pm – 5:15 pm|
|A4||IND||Pinsky||CAS B20||W||6:00 pm – 8:45 pm|
*Students are strongly encouraged to take MET CS 521, unless they already have a Python background and want to expand their Java skills.
MET CS 526 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 cr. ]
|A1||IND||Lee||KCB 102||T||6:00 pm – 8:45 pm|
MET CS 546 Introduction to Probability and Statistics
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. [ 4 cr. ]
|A1||IND||Gorlin||MCS B29||M||6:00 pm – 8:45 pm|
|A2||IND||Gorlin||SHA 206||T||6:00 pm – 8:45 pm|
|BHA||IND||Enxing||ROOM||W||6:00 pm – 8:45 pm|
And one of the following:
MET CS 579 Database Management
This course provides a theoretical yet modern presentation of database topics ranging from Data and Object Modeling, relational algebra and normalization to advanced topics such as how to develop Web-based database applications. Other topics covered - relational data model, SQL and manipulating relational data; applications programming for relational databases; physical characteristics of databases; achieving performance and reliability with database systems; object-oriented database systems. Prereq: MET CS 231 or MET CS 232; or instructor's consent. [ 4 cr. ]
|A1||IND||Russo||CAS 218||M||6:00 pm – 8:45 pm|
MET CS 669 Database Design and Implementation for Business
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. Prerequisite: MET CS 200 or MET CS 622. Or, instructor's consent. [ 4 cr. ]
|A1||IND||Matthews||KCB 102||M||6:00 pm – 8:45 pm|
|A2||IND||Maiewski||EPC 203||W||6:00 pm – 8:45 pm|
|A3||IND||Russo||PSY B53||W||6:00 pm – 8:45 pm|
A maximum of two graduate-level courses (8 credits) taken at Metropolitan College before acceptance into the program may be applied towards the degree.
A total of eight courses (32 credits) is required. A minimum passing grade for a course in the graduate program is a C (2.0) but an average grade of B (3.0) must be maintained to be in good academic standing and to be eligible to graduate.
(Six courses/24 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. ]
|A1||IND||Zhang||MET 101||M||2:30 pm – 5:15 pm|
|A2||IND||Kalathur||EPC 204||M||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. ]
|A1||IND||Raghu||PSY B51||M||6:00 pm – 8:45 pm|
|A2||IND||Alaghemandi||HAR 304||W||6:00 pm – 8:45 pm|
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. [ 4 cr. ]
|A1||IND||Belyaev||CAS 204A||M||6:00 pm – 8:45 pm|
|A2||IND||Belyaev||CAS 324||W||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. ]
|A1||IND||Pinsky||MET 101||M||6:00 pm – 8:45 pm|
|A2||IND||Kalathur||CAS B06A||T||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. Google analytics tool is used for collection of web site data and doing the analysis. 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 and game metrics will be extensively investigated. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. [ 4 cr. ]
|A1||IND||Vasilkoski||STH B02B||T||6:00 pm – 8:45 pm|
|A2||IND||Rawassizadeh||CAS 229||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. ]
|A1||IND||Lee||MET 122||W||6:00 pm – 8:45 pm|
|A2||IND||Lee||SAR 104||M||6:00 pm – 8:45 pm|
|E1||IND||Lee||MET 122||W||6:00 pm – 8:45 pm|
(Two courses/8 credits)
Choose two electives from the following list:
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. ]
MET CS 767 Machine Learning
Theories and methods for automating and representing knowledge with an emphasis on learning from input/output data. The course covers a wide variety of approaches, including Supervised Learning, Neural Nets and Deep Learning, Reinforcement Learning, Expert Systems, Bayesian Learning, Fuzzy Rules, Genetic Algorithms, and Swarm Intelligence. 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. ]
|A1||IND||Braude||STH B19||W||6:00 pm – 8:45 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. ]
|A1||IND||Teymourian||MET 122||R||12:30 pm – 3:15 pm|
MET CS 779 Advanced Database Management
This course covers advanced aspects of database management systems including advanced normalization and denormalization, query optimization, object- oriented and object-relational databases, data warehousing, data mining, distributed databases, XML, XSL, and databases for web applications. There is extensive coverage of SQL and database instance tuning. Students learn about the advanced object- relational features in DBMS such as Oracle, including navigational query, BLOBs, abstract data types, and methods. Students learn about unstructured "big data" databases and gain hands-on experience with MongoDB and Spark, which are integrated into the course web site. Prereq: MET CS 579 or MET CS 669; or instructor's consent. [ 4 cr. ]
|A1||IND||Polnar||SOC B65||R||6:00 pm – 8:45 pm|
|E1||IND||Polnar||SOC B65||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. ]
|A2||IND||Weiner||COM 215||R||6:00 pm – 8:45 pm|
To view a full list of Computer Science courses offered by Metropolitan College, visit the Computer Science & IT Graduate Courses page.