MS in Big Data Analytics

Master of Science in Big Data Analytics

Course Delivery

On Campus / Online

Total Credits


Tuition Per Year



2 years

Program Overview

The program is designed to meet the increasing need for highly skilled data analysts who can analyze the growing amount of data confronting in a variety of disciplines and transform it into usable information for use in decision-making. The program delivers rigorous training in computational techniques and provides mastery of data analysis. Students can expect to play a greater role in decision making and strategy setting for their current or future organizations, adding significant value. The program will provide students with frameworks for critically looking at data, interpreting and visualizing data, and applying that knowledge in real-world applications that will shape how 21st century business challenges are addressed.

Learning Goals

  • Organize, manipulate, and summarize data in various formats.
  • Convert a data analytic problem and related information into proper mathematical representation and select appropriate methodologies for analysis based on attributes of the available datasets.
  • Implement security measures and ethical practices for collection and storage of data.
  • Transfer (and transform) data from different platforms into usable contexts.
  • Communicate and summarize results of data analysis in written, oral and visual form.
  • Select the appropriate methods and tools for data analysis in specific organizational contexts.

Who is the Ideal Student for this program?

A data analyst is the one who collects, organizes and analyzes large sets of data (known as Big Data) to discover patterns and some other useful information. Data mining and Data auditing are must have skills to become a Data Analyst. There are certain other skills that a Data Analyst must possess: knowledge about analytical tools, testing skills, basic statistical skills, machine learning, and data visualization.


All courses are conducted on campus. To succeed in this program, students should be self-disciplined, self-directed and comfortable scheduling their own coursework.

Career Outlook

The program will prepare students for job positions such as data analyst, database administrator, database developer, data modeler, data scientist, business intelligence analyst, database manager, data warehouse manager, data architect, big data engineer, and data scientist.

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Meet the Department Chair

PipopPipop Nuangpookka, PhD

[email protected]

Dr. Pipop Nuangpookka is the Chair of Information Management Science Program and Director of Distance Education at Bay Atlantic University.

Dr. Pipop Nuangpookka has been working as an Analyst/Programmer, Infrastructure Technologies, and Infrastructure Security since the year 2005. While working in the IT industry as a technical person, He started teaching in Computer Science, Information Technology, and Cybersecurity disciplines at several universities in Washington, DC, and Northern Virginia area in 2007. The various courses include Introduction to Computer Science, Computer Network, Computer Programming Languages (i.e., Java, Python, C, C++, ML, and Prolog), Structure of Programming Languages, Software Engineering, Data Structures & Algorithms, Database Management System (i.e., MS SQL Server, Oracle, MySQL, and SQL/PLSQL), Web Application Development and Security, Technology Management, and Technical Capstone Project. Dr. Nuangpookka graduated with a Bachelor’s Degree in Business Administration from Payap University, Thailand, in 1999, and a Master’s Degree in Computer Science in 2004 from Marymount University, Arlington, Virginia. He also earned a Doctor of Science Degree in Cybersecurity at Marymount University in 2020. His research interests are Non-Destructive Method of Detecting Hardware Attack, Personally Identifiable Information (PII) Security Controls, Web Server Protection, and Bilingual Translator Programming. He is a co-author of scholarly papers published in the Journal of Computing Sciences in Colleges/ACM digital library, including “Hardware-Tampering Security Risks in the Supply Chain” and “Fileless Malware and Programmatic Method of Detection.”


Students must earn a total of 36 college credit hours to receive this degree. Of these credit hours, 21 credits are core courses, and 15 elective credits.

In addition, students must meet the following criteria:

  • Students enrolled in the graduate program must maintain a Cumulative Grade Point Average (CGPA) of at least 3.0 (B) out of 4.0 and earn a minimum grade of not less than 2.7 (B-) out of 4.0 on all courses to qualify to graduate.
  • The Maximum Time Frame (MTF) for completion of the Master’s program is 60 credits.
  • A graduate student may transfer up to 6 credit hours earned at accredited institutions.
BGDA 501
Introduction to Big Data
This course will provide insight into the basics of using "Big Data" to quantify operational implications of BAU ACADEMIC CATALOG 123 management choices. You will learn statistical models, mostly using R software, and analyze them to provide insight regarding the assumptions, value drivers, and risks present in a business situation. You will use your statistical models to explore different ways to think about uncertainty, guide decision-making, and persuasively communicate analytical results. Later in the course, by using the statistical tools learned, we will examine simple, introductory methods to text mining, building search engines and recommendation tools.
BGDA 510
Data Mining
(Prerequisite CMPS 514) This course provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth.
BGDA 511
Big Data and Analytics
(Prerequisite CMPS 514) Big data is a general term used to describe the tremendous amount of unstructured, semi-structured and textual data being created on a daily basis. Big data analytics is the process of examining large amounts of data with different types to discover hidden patterns, unknown correlations and potential useful information. This is important for enterprises as it can provide competitive advantages over rivals and other business benefits, such as more effective marketing and increased revenue. In this course, the technologies associated with big data analytics including NoSQL databases, Hadoop and MapReduce will be covered. These technologies form the core of an open source software framework that supports the processing of large data sets across clustered systems.
BGDA 522
Applied Statistics
The course introduces fundamental topics in statistics and implements its applications to industrial, medical, financial, energy, and similar type of very large-size datasets to infer meaningful statistical results. The course is for graduate students with no significant background in this subject. Implementations will be performed on open-source statistical software. An introduction to R programming will be given.
BGDA 555
Business Intelligence
(Prerequisite BGDA 522) The content of this course is composed of introduction to business intelligence, database management systems, data warehouse models and architectures, data mining, preprocessing, driven methodology, guided algorithms and non- guided algorithms.
CAPS 621
Capstone Project
(Prerequisite All Cores) Each student in the MS in Big Data Analytics program is required to complete a capstone project. Each student may choose a project of his or her choice, under the guidance of a capstone advisor. The parameters of the course will be determined by the advisor and the student.
CMPS 514
Management Information Systems
This course studies systems used by companies to accumulate, classify, and organize information to aid managerial decision making. It emphasizes the considerations of upper-level management concerning the development, deployment, and use of information systems.
BGDA 513
Artificial Intelligence
(Prerequisite BGDA 511) The fundamentals and techniques of Artificial Intelligence are discussed in this course. The first part of the course begins with an overview of intelligent agents and agent architectures. We then introduce basic search techniques for problem solving and planning. Adversarial search and the principals of game theory are given. Knowledge representation and logical formalisms using propositional and first order logic are explained. Planning in partial observable environments is introduced. In the second part, we first give a summary of probability theory and then explain probabilistic reasoning including Markov Decision process and Reinforcement Learning. Then some basic concepts of Machine Learning algorithms are discussed. Finally, we give examples of AI applications such as Robotics, Computer Vision and Natural Processing
BGDA 521
Technology Management
(Prerequisite BGDA 510) This course is designed to lead the student to understand the importance and the nature of technological innovations, how they are integrated into business level strategies and how technological innovation process is managed. In this course, the aim is not only to understand the theories of technological innovations but also to discuss the practice of technological innovation. Therefore, case studies are important; most of the theoretical parts are followed by case studies.
BGDA 550
Big Data and Hadoop Environment
(Prerequisite BGDA 511) This course provides an overview of the fields of big data analytics and data science. Topics are covered in the context of data analytics include the terminology and the core concepts behind big data problems, applications, and systems. In this course, the students learn how to use Hadoop and related Big Data Processing tools that are used for scalable big data analysis and have made it easier and more accessible.
BGDA 552
Big Data Analytics and Cloud Computing
(Prerequisite BGDA 511) The course will cover topics in architectures, features, and benefits of Cloud Computing; Cloud Computing technologies such as Virtual Machines, SAAS, IAAS, Cloud Based Networks, Cloud Based Databases. Describe Cloud Computing solutions and identify parameters for managing and monitoring big data infrastructure. Scenarios using sample data will be conducted, to develop skills using Cloud Computing Infrastructure.
CMPS 515
Network Security & Cryptography
(Prerequisite CMPS 514) This is an introductory course where fundamental concepts in cryptography and network security are explained. After completing the course, students will get basic understanding about encryption, decryption, stream ciphers, block ciphers, public-key cryptography, digital signatures, hash functions, 1message authentication codes and key distribution protocols.
CMPS 517
Computer Forensics
(Prerequisite CMPS 514) This is an applied course on techniques for computer forensics in Linux and Windows based systems. In this course, the process of computer forensics investigation will be presented in detail. Details on techniques for evidence collection will be given first. Different techniques for analysing the collected evidence will be explained. Finally,students will learn how to go over the found evidence and present it to authorities. Topics such as custody of chain, evidence preservation and verification will be explained in detail.
CMPS 524
Computer Networks and Mobile Communications
(Prerequisite CMPS 514) This course provides a comprehensive overview of computer networks and mobile communications technologies. The topics include computer networks, Internet, TCP/IP, transport layer protocols, routing layer protocols, medium access control protocols, wireless channel models, packet scheduling, multimedia networks, cellular networks (GSM, GPRS, CDMA, 3G, 4G, etc.), and wireless local area networks. The course aims at equipping students with a deeper understanding of computer and mobile networking technologies and related problem solving discipline using mathematics / engineering principles.
CMPS 530
Machine Learning and Pattern Recognition
(Prerequisite CMPS 524) This course covers fundamental machine learning topics including pattern recognition systems and components; decision theories and classification; discriminant functions; supervised and unsupervised training; clustering; feature extraction and dimensional reduction; sequential and hierarchical classification; applications of training, feature extraction, and decision rules to engineering problems.
CMPS 564
Information Security Management
(Prerequisite CMPS 515) The aim of this course is to learn how information can be held securely in businesses and discuss the information security from managerial perspective. Moreover, the standards and approaches used for information security management are discussed. The standard of information security management which is ISO27001 is discussed in detail.
MKTG 615
Marketing Analytics
(Prerequisite BGDA 555) This course will focus on developing marketing strategies and resource allocation decisions driven by quantitative analysis. Topics covered include market segmentation, market response models, customer profitability,socialmedia, paid search advertising, product recommendation systems, mobile geo-location BAU ACADEMIC CATALOG 125 analysis, media attribution models, and resource allocation. The course will draw on and extend students’ understanding of issues related to integrated marketing communications, pricing, digital marketing, and quantitative analysis. The course will use a combination of cases, lectures, and a hands-on project to develop these skills.

To apply to Bay Atlantic University, the following materials are required:

Completed Online Application

Undergraduate Transcripts (official or officially notarized copy)

Photocopy of Government-Issued ID (international students need a passport, undocumented students need proof of residency)

Additional Documents for International Students:

Bank Statement (to show proof of adequate financial resources)

*If the bank statement is not in the applicant’s name a Sponsorship Letter is required
*If the applicant has any dependents Passport Copies & Additional Materials may be required

Official Bachelor Degree Transcript (to show Bachelor’s degree credentials)

*If the Bachelor’s degree was issued by a foreign institution of higher education, the applicant must provide an evaluation of the transcript from a NACES member ( or an AICE member ( credential evaluation service to establish U.S. equivalency of a Bachelor’s degree.  The evaluation must be a course-by-course evaluation of the transcript.
*If the Bachelor’s degree transcript is not in English, the applicant must provide a certified English translation.
*If the transcript does not clearly indicate the degree awarded, the applicant must provide a notarized copy of the college or university diploma.

SpanTran is our recommended international transcript evaluation service. They have created a custom application for Bay Atlantic University that will make sure you select the right kind of evaluation at a discounted rate. You can access their application here: SpanTran Application – Bay Atlantic University

Proof of English Language Proficiency (below)

All applicants whose first language is not English must submit proof of English language proficiency. This requirement is waived if the applicant has completed four years of education at an English-language tertiary school. Otherwise, English language proficiency can be established by providing an official score report for one of our approved standardized English proficiency tests. Below are the tests and minimum scores accepted:

IELTS: 7.0
TOEFL: 600, 250, 100
TOEIC: 800
BAU Placement Test: 80 (offered on campus)
Duolingo: 90
Pearson (PTE): 53
Mentora College Intensive English Program: Pass 500C Course

For assistance or information on applying, please contact our Admission team at [email protected]

For our Frequently Asked Questions, please visit

Graduation Requirements

The Master of Science in Big Data Analytics degree is earned by completing the program course requirements of 36 credit hours (12 courses of 3 credit hours), of which 21 credits are core courses and 15 credits are elective courses. To qualify for the Master of Science in Big Data Analytics degree, students must meet all core and concentration elective credit requirements.

Students should meet the following minimum requirements to qualify for a graduate degree:
Minimum Passing Grade Per Course B-
CGPA 3.00
Total Required Credits 36

1.  Students enrolled in the graduate program must maintain a Cumulative Grade Point Average (CGPA) of at least 3.0 out of 4.0 to qualify for the graduate degree, to remain in good standing, and to graduate.
2. The Maximum Time Frame (MTF) for completion of the graduate program is 54 credits.
3. A graduate student may transfer up to 50% of credits earned at accredited institutions.

There is no fixed program cost. The Board has the authority to change tuition and fees for each academic year. Such changes are announced to students via email, on the Academic Catalog, and on the webpage.

In the 2023-2024 academic year, tuition per credit will be $1,200. Students pay the total of the credits they enroll in.
In the 2024-2025 academic year, tuition per credit will be $1,235. Students pay the total of the credits they enroll.

Description Fee
Application/Admissions Fees
Application fee $45
Deferral fee $45
Admission Confirmation Deposit (refundable if the visa is denied) $200
Mandatory Semester Fees
Student activities and services fee $125
Technology fee $135
Mandatory one-time Fees
Student ID card $18
As-applicable Fees
Late registration fee $75
English Proficiency Test $35
Replacement Student ID card $18
Transcript processing fee $10 (per transcript)
Returned check fee $30
Late payment fee $25
Cancellation fee* $100
International postage of documents $130
Cap and Gown Fee $130
Diploma / Graduation fee $100
Diploma Replacement fee $100
Administrative Services Fee** $1,500

*When students cancel their enrollment within 3 business days of the beginning of a semester

**Only students who receive full tuition assistance or scholarship of any kind defined in the tuition assistance and scholarship section are required to pay.

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