The Bachelor of Science in Data Science offers students technical depth in data science. Students pursuing this degree will cover the foundational aspects of data science, then progress through more difficult tools, techniques, and methodologies used in data science. Students can tailor their program of study through concentrations including deep learning and business intelligence. Upon completion of this program, students will be able to confidently approach problems or challenges in virtually any discipline: business, finance or economics, engineering, healthcare, or the physical or social sciences. Graduates will be able to deliver reproducible data analyses and solutions. Graduates will understand ethical, privacy, and security considerations in the conduct of data analyses. Graduates will be able to communicate the story in the data through the use of data visualization techniques. 

This program has specific admission requirements.

Degree Program Objectives

Upon completion of this program of study, students will be able to:

  • Recognize requirements for data.  Efficiently collect the required data from a variety of sources and organize it appropriately.
  • Determine the best method to conduct an analysis for a specified situation and given data.  Conduct the analysis or analyses and completely evaluate all aspects of the results.
  • Deliver reproducible analyses and results.
  • Effectively communicate any or all aspects of an analysis and all aspects of the results of that analysis to either or both a technical or non-technical audience. Information communicated could include the method used for analysis, any parameter settings that would affect the analysis or results, and an error analysis, etc.
  • Explain the ethical, privacy, and security issues related to data science analyses and communication.
  • Obtain real-world experience through project-based coursework and the Senior Project.
  • Stand out with a specific technical area of expertise by completing a concentration in any of the available concentrations. 

Programmatic Admission Requirements

For admission to the BS of Data Science, applicants must have completed preparation in mathematics equivalent to pre-calculus or higher. A review of high school or college transcripts showing completion of this requirement will be conducted during the admission process.

Please visit our AMU or APU undergraduate admission page for more information on institutional admission requirements.

Need help?

If you have questions regarding a program’s admission requirements, please contact an Admissions Coach at 877-755-2787 or

Degree at a Glance

General Education Requirements30
Major Required69
Select one of the following concentrations:18
Final Program Requirements3
Total Semester Hours120

Degree Program Requirements

General Education Requirements (30 semester hours)

Arts and Humanities (6 semester hours) 1
Select 2 courses from the following:6
Arabic I
Arabic II
Art Appreciation
Survey of Photography
Film and Literature
Image Enhancement using Adobe Photoshop®
French I
French II
German I
German II
Introduction to Japanese
Literature of American Encounters, Revolution, and Rebellion
From Abolition to #MeToo: Literature of the American Civil Rights Movement
Pivotal Figures in Early British Literature
British Literature from Wordsworth through the Wasteland
Leadership in World Literature: Antiquity to the Early Modern Period
Literature of the Newly Globalized World: The Individual’s Struggle to Adapt
Music Appreciation
Jazz and Rock
World Music and Cultures
Introduction to Philosophy
Critical Thinking
Introduction to Ethics
Philosophy of Science
Introduction to Brazilian Portuguese
Introduction to the Study of Religion
Introduction to World Religions
Russian I
Spanish I
Spanish II
Thinking and Acting Ethically
Civics, Political and Social Sciences (6 semester hours)
Select 2 courses from the following:6
Introduction to Anthropology
World Archaeology
World Archaeology
Introduction to Cultural Anthropology
Human Sexuality
Social Media and Society
Intercultural Communication
Humane Education: A Global Interdisciplinary Perspective
Introduction to Geography
Practical Food Safety and Awareness
International Relations I
Forgotten America--Under Represented Cultures in American Literature
Introduction to Political Science
American Government I
Introduction to Psychology
Death and Dying
Race & Religion
Hope and Resilience
Introduction to Sociology
Social Problems
American Popular Culture
Exploring Society and Cultures via Science Fiction
Communication: Writing, Oral, and Multimedia (9 semester hours)
COMM120Information and Digital Literacy3
ENGL110Making Writing Relevant3
Select 1 course from the following:3
Public Speaking
Proficiency in Writing
Argumentation and Rhetoric
Introduction to Literature
Technical Writing
Scientific Writing
Effective Business Communication
Human Relations Communication
Information Literacy and Global Citizenship
Introduction to Information Technology Writing
Human Relations
History (3 semester hours)
Select 1 course from the following:3
American History to 1877
American History since 1877
World Civilization before 1650
World Civilization since 1650
Western Civilization before The Thirty Years War
Western Civilization since The Thirty Years War
African-American History before 1877
African-American History since 1877
History of the American Indian
History of Science
The History and Context of STEM
Mathematics and Applied Reasoning (3 semester hours)
Natural Sciences (3 semester hours)
Select 1 course from the following:3
Introduction to Biology
Introduction to Human Anatomy and Physiology
Introduction to Chemistry
Introduction to Meteorology
Introduction to Geology
Introduction to Environmental Science
Introduction to Physics
Introduction to Astronomy
Introduction to STEM Disciplines
Total Semester Hours30

Major Required (69 semester hours)

MATH210Discrete Mathematics3
MATH220Linear Algebra3
MATH226Calculus II3
MATH227Calculus III3
MATH240Differential Equations3
DATS200Functional Methods and Coding3
DATS201Analytical Methods I3
DATS211Introduction to Data Science3
DATS221Exploratory Data Analysis3
DATS225Data Visualization3
MATH328Probability Theory with Applications3
MATH340Multivariate Statistics3
MATH410Design of Experiments3
DATS301Analytical Methods II3
DATS311Intermediate Data Science3
DATS371Fundamentals of Simulation3
DATS411Advanced Data Science3
DATS442Bayesian Methods (Bayesian Inference, Naïve Bays)3
DATS443Generalized Linear Equations Using R3
STEM380Coevolution of Society, Culture, and Technology3
STEM471Analytics, Algorithms, AI, and Humanity3
Select 1 course from the following: 13
Behind the Data, Our values and beliefs
Optimization and Machine Learning
Introduction to Python
Python and Data Science
Total Semester Hours69

You must choose a concentration for this degree program and may select from the Flex Concentration, the Concentration in Business Intelligence or the Concentration in Deep Learning.

Flex Concentration (18 semester hours)

The Flex Concentration offers students breadth in data science. Students will learn foundational material in machine learning, sentiment analysis, advanced methods in data science, and simulation. This concentration is a good option for students intending to go onto a master's program in data science where they can pursue in-depth knowledge.


Upon successful completion of this concentration, the student will be able to:

  • Conduct a variety of data analyses using appropriate tools and methods for specified problems or challenges.
  • Explain why the method and tools selected to conduct an analysis are the best for that specific analysis. 
  • Develop and present final reports on data analyses.

Concentration Requirements (18 semester hours)

DATS331Machine Learning I3
DATS332Machine Learning II3
DATS351Sentiment Analysis3
DATS373Simulation Techniques3
DATS401Analytical Methods III3
MATH330Linear Optimization3
Total Semester Hours18

Concentration in Business Intelligence (18 semester hours)

The Concentration in Business Intelligence is intended for students with professional interests in business analytics and prediction/optimization. The courses included in this concentration provide the foundation for this path. Students will study relevant aspects of business as well as data analysis tools and methods required to transform data into knowledge that supports actionable decision-making. 


Upon successful completion of this concentration, the student will be able to:

  • Explain how data is used to form knowledge in business applications.
  • Describe the use of data analytics to generate descriptive and predictive analyses.
  • Explain how optimization can be used to create regions of solutions for business problems. 
  • Evaluate risk associated with predictive analytics. 

Concentration Requirements (18 semester hours)

ACCT105Accounting for Non Accounting Majors3
BUSN100Basics of Business3
BUSN250Analytics I3
BUSN350Analytics II3
BUSN410Critical Thinking Strategies for Business Decisions3
Select 1 course from the following:3
Advanced Analytics
Risk Modeling and Assessment
Total Semester Hours18

Concentration in Deep Learning (18 semester hours)

The Concentration in Deep Learning first provides foundational knowledge. Probabilistic graphical models provide the basis for designing artificial neural networks. The tools and methods of machine learning covered continue developing foundational knowledge. Next, deep learning, that has grown from the study of artificial neural networks, is studied in detail. Last, students can choose to learn about advanced methods in data science or today’s state-of-the-art programs in artificial neural networks, e.g. TensorFlow® by the Google Brain Team operated by Jupyter® notebooks in Python®.

TensorFlow® is a registered trademark of Google, Inc.

Jupyter® is a registered trademark of NumFOCUS, Inc.

Python® is a registered trademark of Python Software Foundation.


Upon successful completion of this concentration, the student will be able to:

  • Conduct analyses using appropriate machine learning tools.
  • Design, develop, and utilize a variety of artificial neural networks including recurrent and convolutional networks.
  • Explain the basic principles of deep learning, e.g. the use of multiple types of layers, optimization, and hyperparameters. 

Concentration Requirements (18 semester hours)

CSCI381Machine Learning3
CSCI386Advanced Topics in Machine Learning3
CSCI484Introduction to Artificial Intelligence3
CSCI486Deep and Reinforcement Learning3
DATS344Probabilistic Graphical Models3
Select 1 course from the following:3
Analytical Methods III
Artificial Neural Networks using TensorFlow (Recommended)
Total Semester Hours18

Final Program Requirements (3 semester hours)

DATS499Senior Capstone Project3
Total Semester Hours3