Online Master of Science in Applied Data Analytics
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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.
Why Choose BU’s Master of Science in Applied Data Analytics?
- In 2023, 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.
Computer and Information Research Scientists
15% increase in jobs through 2029
$122,840 median annual pay in 2019
Computer Systems Analysts
7% increase in jobs through 2029
$90,920 median annual pay in 2019
10% increase in jobs through 2029
$93,750 median annual pay in 2019
Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, September 2020
Best Technology Jobs, 2023 U.S. News & World Report
- #1 Software Developer
- #2 Information Security Analyst
- #3 IT Manager
- #4 Web Developer
- #5 Computer Systems Analyst
- #6 Data Scientist
- #7 Database Administrator
- #8 Computer Network Architect
- #9 Computer Systems Administrator
- #10 Computer Support Specialist
- #11 Computer Programmer
The growing role of big data in the economy and business will create a significant need for statisticians and data analysts, for example; we estimate a shortfall of up to 250,000 data scientists in the US in a decade.
McKinsey & Company
What’s now and next in analytics, AI, and automation
. . . we’re seeing an explosion of machine learning roles and continuing growth of data science roles.
LinkedIn 2018 Emerging Jobs Report
By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000.
Burning Glass Technologies, Business-Higher Education Forum (BHEF), and IBM
The Quant Crunch: How The Demand For Data Science Skills Is Disrupting The Job Market
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.
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.
The online Master of Science in Applied Data Analytics consists of eight courses (32 credits).
(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
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 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
Formerly titled CS 688 Web Analytics and Mining.
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. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. [4 credits]
METCS699 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 JMP Pro. Prereq: CS 546 and either CS 579 or CS 669. Or instructor's consent. [4 credits]
(Two courses/8 credits)
Choose two electives from the following list:
METCS689 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 credits]
METCS767 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 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
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
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
- Two 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.
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
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.
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
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
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.
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)
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
Assistant Professor, Computer Science; Director, Analytics
PhD, Brandeis University; MS, Indian Institute of Technology; BS, Regional Engineering College (Warangal, India)
Jae Young Lee
Assistant Professor, Computer Science; Coordinator, Databases
PhD, MS, University of Texas at Arlington; BS, Seoul National University (Korea)
Associate Professor of the Practice, Computer Science; Coordinator, Software Development
PhD, Columbia University; BA, Harvard University
Associate Professor, Computer Science
PhD, MS, University of Rochester; BA, University of California San Diego
Assistant Professor and Chair, Computer Science
PhD, Kazan University (Russia); MS, Moscow University
Assistant Professor, Computer Science; Coordinator, Programming Languages
PhD, Freie Universität Berlin; MS, BS, Berlin University of Technology (TU-Berlin)
Assistant Professor, Computer Science; Coordinator, Health Informatics
PhD, MEng, Nanyang Technological University, Singapore; BS, Luoyang Institute of Technology
Assistant Professor, Computer Science
PhD, Penn State University; BS, Tongji University (Shanghai, China)
Assistant Professor, Computer Science; Coordinator, Information Security
PhD, Boston University; MS, BS University of Science and Technology Beijing
To learn more or to contact an enrollment advisor before you get started, request information using the button below and tell us a little about yourself. Someone will be in touch to answer any questions you may have about the program and detail the next steps in earning your degree. You can also start your application or register for a course at Metropolitan College.