BS in Data Science

Bachelor of Science in Data Science

Course Delivery

On Campus / Online

Total Credits


Tuition Per Year



4 Years

This program is not accepting new students.

Program Overview

The BS program in Data Science integrates scientific methods from statistics, computer science and data-based business management to extract knowledge from data and drive decision making. Graduates are prepared to meet the challenges at the intersection between big data, business analytics, and other emerging fields.

Learning Goals

    • To meet the growing demand for high-level information systems/science skills
    • To provide a path for individuals from diverse fields to rapidly transition to data science careers
    • Enable established IT and computing professionals to upgrade technical management and development skills
    • Prepare graduates to apply data science techniques for knowledge discovery and dissemination to assist researchers or decision-makers in achieving organizational objectives

Who is the Ideal Student for this program?

A data scientist is someone who makes value out of data. Such individuals proactively fetch information from various sources and analyze it for better understanding about how the business performs, and build AI tools that automate certain processes within the company.


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 undergraduate program provides comprehensive education in data science to further develop the knowledge and skills to prepare for a career in data science.

Students will qualify for jobs such as data analyst, data science/analytics manager, database administrator, big data engineer, data mining engineer, machine learning engineer, data architect, data warehouse architect, commercial intelligence manager, competitive intelligence analyst, consultant, strategic business and technology intelligence, manager of market intelligence, director of enterprise strategy, and director of global intelligence.

Request More Information

Please Click here or just give us a call at (202) 644-7200 to speak with an admissions advisor.

Meet the Department Chair


Pipop 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.”

This program is not accepting new students.


Students must earn a total of 120 college credit hours to receive this degree. Of these credit hours, 60 credits are core courses, 42 general education credits, and 18 elective credits. Students must meet their core requirements as well as their general education requirements.


In addition, students must meet the following criteria:

    • Students enrolled in the undergraduate program must maintain a Cumulative Grade Point Average (CGPA) of at least 2.0 out of 4.0 to qualify for the BA degree, to remain in good standing, and to graduate.
    • The Maximum Time Frame (MTF) for completion of the BA program is 180 credits.
    • An undergraduate student may transfer up to 60 credit hours earned at accredited institutions.
    • No degree credit is received by an undergraduate for any failing grade (a grade less than D, or 1.00 out of 4.00 grade points)
Law & Ethics
3 Credits
In this course, students will learn about the role of ethics in international relations and international law, and areas in which the law fails to cover ethics. The course will begin by examining instances of unethical practices in diplomacy, war, and international business from the nineteenth century to present. It will examine various laws that have been introduced during the last two centuries in efforts to curtail unethical behavior and laws that have allowed nations to exploit weaker world regions. Students will complete major simulations in order to practice applying ethics to international law and diplomacy and will propose new policies to encourage ethical diplomaticrelations.
CMPS 122
Introduction to Programming I
3 Credits
An introductory course in programming, CMPS 122 exposes students to the concepts involved in using higher-level object-oriented programming language. The course will explain the programming process and give students lots of hands-on experience writing small programs during labs.
CMPS 202
Data Structures and Algorithms I
3 Credits
(Prerequisite CMPS 122) The objective of this course is to introduce algorithms, algorithm complexities, basic data structures, data organizations, sorting and searching algorithms. This course will also focus on the implementation details of the algorithms. Students will learn to analyze the efficiency of operations and algorithms executed on various data structures, including array, stack, queue, and linked list. The course will also cover recursion and iteration used in computer programming.
CMPS 205
Data Structures and Algorithms II
3 Credits
(Prerequisite CMPS 202) The objective of this course analyzing time and space requirements of important algorithms and structures. Various data structures such as stacks, queues, trees and graphs will be introduced and analyzed. This course will also focus on the implementation details of the algorithms.
CMPS 211
Computer Networks
3 Credits
An introduction to the design and analysis of computer communication networks. Topics include application layer protocols, Internet protocols, network interfaces, local and wide area networks, wireless networks, bridging and routing, and current topics.
CMPS 222
Programming II
3 Credits
(Prerequisite CMPS 122) This course offers a continuation of the programming skills learned in CMPS 122. Students will learn more advanced applications of a programming language through lab work and independent assignments. Topics include Graphical User Interface, File I/O, Exception, Database Programming, Networking Basics, and Multi-Thread Programming.
CMPS 226
Introduction to Data Science
3 Credits
A first course in data science. Introduces data science as a field, describes the roles and services that various members of the community play and the life cycle of data science projects. Provides an overview of common types of data, where they come from, and the challenges that practitioners face in the modern world of “Big Data.” Provides an introduction to the interdisciplinary mixture of skills that the practice requires
CMPS 230
Information Visualization
3 Credits
This course introduces the foundation and the state of the art of information visualization that explores and reflects on the design, application, and evaluation of a diverse range of information systems. Students will demonstrate how a number of common types of information can be visually, intuitively and interactively represented. The course provides a first-hand experience of visualizing a variety of realistic data types.
CMPS 318
Database Management Systems
3 Credits
Main objective is understanding database management systems and creating efficient database schemas according to normalization theory. This course covers E-R modelling, database design, relational databases, SQL, relational languages, query optimization, query processing and XML.
CMPS 322
Machine Learning and Pattern Recognition
3 Credits
(Prerequisite CMPS 205) Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this course is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The course provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the course covers a wide array of central topics that have not been addressed by previous courses. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
CMPS 337
Information Retrieval Systems
3 Credits
(PREREQUISITES: MATH 110, CMPS 122) The theoretical underpinnings of information retrieval are covered to give the student a solid base for further work with retrieval systems. Emphasis is given to the process of textual information for machine indexing and retrieval. Aspects of information retrieval covered include document description, query formulation, retrieval algorithms, query matching, and system evaluation.
CMPS 438
Exploratory Data Analytics
3 Credits
(Prerequisite CMPS 226) In this course students learn the essential exploratory techniques for summarizing and analyzing data. The course discusses how to install and configure software necessary for a statistical programming environment. It covers practical issues in statistical computing, which includes programming in R and how to use R for effective data analysis. The course covers the plotting systems in R and some of the basic principles of constructing data graphics.
ISIT 112
Introduction to Information Technology
3 Credits
This course introduces basic issues in information science, including the nature of information, information technology, information security, information policy, information ethics, and the relationships between information technologies and the information context.
ISIT 224
Information Systems Analysis and Design
3 Credits
The goal of this course is to examine the system and the concepts of information system. Students learn analysis and design of the information system.
MATH 110
Introduction to Statistics
3 Credits
(Prerequisite: MATH 103 & MATH 104) This is an introductory course that assumes no prior knowledge of statistics but does assume some knowledge of high school algebra. Basic statistical concepts and methods are presented in a manner that emphasizes understanding the principles of data collection and analysis rather than theory. Much of the course will be devoted to discussions of how statistics is commonly used in the real world.
MATH 128
Linear Algebra
3 Credits
Linear algebra is the study of linear systems of equations, vector spaces, and linear transformations. Solving systems of linear equations is a basic tool of many mathematical procedures used for solving problems in science and engineering.
MATH 131
Calculus I
3 Credits
(Prerequisite: MATH 104) This is an introductory course to provide students with an introduction to Calculus. The course covers topics such as rules of differentiation, the chain rule and implicit differentiation; derivatives of trigonometric, exponential, logarithmic, and inverse trigonometric functions; the Mean Value theorem; and indeterminate forms and L’Hopital’s rule.
MATH 132
Calculus II
3 Credits
(Prerequisite MATH 131) This course builds on skills learned in MATH 131. It covers subjects such as techniques of integration; applications of integration; conics, parametric curves, and polar curves; partial differentiation; and multiple integration.
MATH 140
Discrete Mathematics
3 Credits
The aim of the course is to give students the necessary background in discrete mathematical structures. Basic algorithms on discrete structures will be taught.
MATH 212
Numerical Analysis
3 Credits
(Prerequisite MATH 132) Numerical Analysis helps on transforming functions, derivatives, integrals, and differential equations as strings of numbers that can be calculated in the computer. At most important issue in Numerical Analysis is an understanding of the speed of convergence of the series expansions for the method used to approximate or solve a problem.
CMPS 315
Operating Systems
3 Credits
This course examines the important problems in operating system design and implementation. The operating system provides an established, convenient, and efficient interface between user programs and the bare hardware of the computer on which they run. The operating system is responsible for sharing resources (e.g., disks, networks, and processors), providing common services needed by many different programs (e.g., file service, the ability to start or stop processes, and access to the printer), and protecting individual programs from interfering with one another. The course will start with a brief historical perspective of the evolution of operating systems over the last fifty years and then cover the major components of most operating systems. This discussion will cover the tradeoffs that can be made between performance and functionality during the design and implementation of an operating system. Particular emphasis will be given to three major OS subsystems: process management (processes, threads, CPU scheduling, synchronization, and deadlock), memory management (segmentation, paging, swapping), and file systems; and on operating system support for distributed systems.
CMPS 310
Introduction to Artificial Intelligence
3 Credits
(Prerequisite CMPS 202) This course covers fundamental concepts and algorithms of artificial intelligence (AI) and its techniques, including search heuristics, knowledge representation, planning, reasoning, and learning to underline the design of intelligent computer systems. Students will learn to implement autonomous mechanisms that fully or partially observe involved factors for automatic decision-making. The course introduces students to various techniques, including search methods, machine learning, natural language processing, robotic mechanisms, and computer vision.
CMPS 332
Analysis of Algorithms
3 Credits
PREREQUISITE: CMPS 202 The objective of the course is to introduce the fundamental mathematical tools needed to analyze algorithms, basic algorithm design techniques, advanced data structures, and important algorithms from different problem domains.
CMPS 426
3 Credits
(Prerequisite MATH 110) This course covers computational techniques for mining the large amount of information produced by recent advances in molecular biology, such as genome sequencing and microarray technologies. The methods by which computers are used to manipulate and analyze sequences and structures will also be taught. The outline of the course is arranged to give fundamental concepts of bioinformatics to the students.
CMPS 433
Game Programming
3 Credits
(Prerequisite CMPS 205) This course will support students the emerging trends, and frameworks of gamification, why it has a great potential to apply in IT projects, and how to use it effectively. The course allows students to develop a set of practical skills in using game elements using industrial case studies. Students will understand practical ways for improving a software development business particularly by understanding ways of creating an effective IT solution and exploring the intangible value in business landscapes. Unity game engine will be used as the development environment.
CMPS 477
Image Processing
3 Credits
(Prerequisite: CMPS 230) This course is an introduction to the fundamental concepts and techniques in basic digital image processing and their applications to solve real life problems. The topics covered include Digital Image Fundamentals, Image Transforms, Image Enhancement, Restoration and Compression, Morphological Image Processing, Nonlinear Image Processing, and Image Analysis. Application examples are also included.
CMPS 480
Big Data
3 Credits
(Prerequisite CMPS 318) This course will provide insight into the basics of using "Big Data" to quantify operational implications of 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.
ISIT 328
Data Warehouse Design
3 Credits
(Prerequisite: CMPS 318) This course aim is teaching the data warehouse design. At the end of semester, students will learn database concepts and data warehouse concepts.
ISIT 350
Advanced Web Application Design
3 Credits
This course teaches advanced web application design using Java ServerFaces web framework. Understanding managed beans, page navigation rules, expression language, data validation and conversion, AJAX support, application security, building custom components and related topics will be covered within the scope of this course.
ISIT 355
Advanced Mobile Application Development
3 Credits
(Prerequisite: ISIT 248 or ISIT 350 OR CMPS 222) Technology continues to evolve and provide us with increasingly powerful mobile devices. Thus, applications that can run on a browser must also be written such that they are compatible with mobile devices, the majority of which are now web-enabled. Meanwhile, there is an increasing demand for native applications that can be downloaded to and run on mobile devices. This course will address these trends, teaching you to think about the unique design and deployment issues that must be taken into consideration when developing applications for mobile devices.
ISIT 362
Social Network Analysis
3 Credits
The course presents mathematical methods and computational tools for Social Network Analysis (SNA). SNA was pioneered by sociologist, but recently became an interdisciplinary endeavor with contributions from mathematicians, computer scientists, physicists, economists etc., who brought in many new tools and techniques for network analysis. In this course we will start with basic statistical descriptions of networks, analyze network structure, roles and positions of nodes in networks, connectivity patterns and methods for community detection. In the second part of the course we will discuss processes on networks and practical methods of network visualization.
ISIT 370
Agile Project Management
3 Credits
(Prerequisite MGMT 200) This course covers an introduction to agile project management, fundamental principles and practices about agile project development and management.
MGMT 200
Introduction to Project Management
3 Credits
Introduction to Project Management utilizes a simulated team project to manage a project’s life cycle. Emphasis is placed on activity networks, managing resources, and creating control mechanisms that minimize risk. Project leadership is explored in the context of building effective project teams and maintaining stakeholder relationships. Students will learn and apply basic project management concepts including triple constraint, planning, scheduling, work breakdown structures and project control.

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

Completed online application

Copies of high school transcripts (must be in English)

Official evaluation of high school transcript (if the transcript is from a foreign institution)

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

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

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 secondary 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:

TOEFL (PBT, CBT, IBT): 525, 194, 70
IELTS: 5.5
TOEIC: 650
BAU Placement Test:70 (offered on campus)
Duolingo: 75
Pearson (PTE): 48
Mentora College Intensive English Program: Pass 400C level

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

For our Frequently Asked Questions, please visit

This program is not accepting new students.

Graduation Requirements

The Bachelor of Science in Data Science is earned by completing the program course requirements of 120 credit hours. Of these credit hours, 63 credits are major or core courses, 42 general education credits, and 18 pure elective credits. Students must meet their core requirements as well as their general education requirements. In addition, students must meet the following criteria:

1.  Students enrolled in the undergraduate program must maintain a Cumulative Grade Point Average (CGPA) of at least 2.0 out of 4.0 to qualify for the BA degree, to remain in good standing, and to graduate.
2. The Maximum Time Frame (MTF) for completion of the BA program is 180 credits.
3. An undergraduate student may transfer up to 60 credit hours earned at accredited institutions.
4. No degree credit is received by an undergraduate for any failing grade (a grade less than D, or 1.00 out of 4.00 grade points).


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 $620. Students pay the total of the credits they enroll in.
In the 2024-2025 academic year, tuition per credit will be $635. Students pay the total of the credits they enroll in.

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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