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 representative at 877-755-2787 or info@apus.edu.
Degree at a Glance
| Code | Title | Semester Hours |
|---|---|---|
| General Education Requirements | 30 | |
| Major Required | 69 | |
| Select one of the following concentrations: | 18 | |
| Final Program Requirements | 3 | |
| Total Semester Hours | 120 | |
Degree Program Requirements
General Education Requirements (30 semester hours)
| Code | Title | 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 | ||
| Introduction to Cultural Anthropology | ||
| Human Sexuality | ||
| Social Media and Society | ||
| Intercultural Communication | ||
| Microeconomics | ||
| Macroeconomics | ||
| 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) | ||
| COMM120 | Information and Digital Literacy | 3 |
| ENGL110 | Making Writing Relevant | 3 |
| 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) | ||
| MATH225 | Calculus | 3 |
| 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 Hours | 30 | |
| 1 | All literature courses require successful completion of ENGL101 - Proficiency in Writing or ENGL110 - Making Writing Relevant. |
Major Required (69 semester hours)
| Code | Title | Semester Hours |
|---|---|---|
| MATH210 | Discrete Mathematics | 3 |
| MATH220 | Linear Algebra | 3 |
| MATH226 | Calculus II | 3 |
| MATH227 | Calculus III | 3 |
| MATH240 | Differential Equations | 3 |
| DATS200 | Functional Methods and Coding | 3 |
| DATS201 | Analytical Methods I | 3 |
| DATS211 | Introduction to Data Science | 3 |
| DATS221 | Exploratory Data Analysis | 3 |
| DATS225 | Data Visualization | 3 |
| MATH302 | Statistics | 3 |
| MATH328 | Probability Theory with Applications | 3 |
| MATH340 | Multivariate Statistics | 3 |
| MATH410 | Design of Experiments | 3 |
| DATS301 | Analytical Methods II | 3 |
| DATS311 | Intermediate Data Science | 3 |
| DATS371 | Fundamentals of Simulation | 3 |
| DATS411 | Advanced Data Science | 3 |
| DATS442 | Bayesian Methods (Bayesian Inference, Naïve Bays) | 3 |
| DATS443 | Generalized Linear Equations Using R | 3 |
| STEM380 | Coevolution of Society, Culture, and Technology | 3 |
| STEM471 | Analytics, Algorithms, AI, and Humanity | 3 |
| Select 1 course from the following: 1 | 3 | |
| Behind the Data, Our values and beliefs | ||
| Optimization and Machine Learning | ||
| Introduction to Python | ||
| Python and Data Science | ||
| Total Semester Hours | 69 | |
| 1 | Selected Upper Division Computer Science, Computer Technology and Business/Analytics Courses are also available with permission of Advisor. |
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.
Objectives
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)
| Code | Title | Semester Hours |
|---|---|---|
| DATS331 | Machine Learning I | 3 |
| DATS332 | Machine Learning II | 3 |
| DATS351 | Sentiment Analysis | 3 |
| DATS373 | Simulation Techniques | 3 |
| DATS401 | Analytical Methods III | 3 |
| MATH330 | Linear Optimization | 3 |
| Total Semester Hours | 18 | |
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.
Objectives
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)
| Code | Title | Semester Hours |
|---|---|---|
| ACCT105 | Accounting for Non Accounting Majors | 3 |
| BUSN100 | Basics of Business | 3 |
| BUSN250 | Analytics I | 3 |
| BUSN350 | Analytics II | 3 |
| BUSN410 | Critical Thinking Strategies for Business Decisions | 3 |
| Select 1 course from the following: | 3 | |
| Advanced Analytics | ||
| Risk Modeling and Assessment | ||
| Total Semester Hours | 18 | |
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.
Objectives
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)
| Code | Title | Semester Hours |
|---|---|---|
| CSCI381 | Machine Learning | 3 |
| CSCI386 | Advanced Topics in Machine Learning | 3 |
| CSCI484 | Introduction to Artificial Intelligence | 3 |
| CSCI486 | Deep and Reinforcement Learning | 3 |
| DATS344 | Probabilistic Graphical Models | 3 |
| Select 1 course from the following: | 3 | |
| Analytical Methods III | ||
| Artificial Neural Networks using TensorFlow (Recommended) | ||
| Total Semester Hours | 18 | |
Final Program Requirements (3 semester hours)
| Code | Title | Semester Hours |
|---|---|---|
| DATS499 | Senior Capstone Project | 3 |
| Total Semester Hours | 3 | |