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About This Program
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.
What You Will Do
- 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.
Degree at a Glance
Number of Credits
120
Cost Per Credit
$360 | $250*
$324**
$324**
Courses Start Monthly
Online
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Program Requirements Printable Catalog Version
Students must choose a concentration for this degree program:
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.
Must take all courses for this section.
Course ID: 5315
|
This course provides an overview of types of problems that can be used with machine learning, as well as different variations of machine-learned methods such as supervised/unsupervised, batch/online, etc. The course discusses the main challenges of machine learning, notably the issue of data quality, as well as overfitting and underfitting data. (Prerequisite: DATS211 or CSCI345).
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5316
|
This course is focused on more advanced topics in machine learning, including clustering, dimensionality reduction, and the emerging applications of machine learning such as recommender systems, search in unstructured data, and time series analysis. (Prerequisite: DATS331 or CSCI381)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 5325
|
This course provides students with in-depth knowledge and skills in computer science, including the history of AI and the processes involved in training machines to learn. It covers various search methods and why they are important to AI as well as specific AI applications Topics covered enable students to continue their study of artificial intelligence. Through readings, assignments, and laboratories in which they learn to conduct analyses to meet specified objectives, students gain hands-on experience. (Prerequisite: CSCI381)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5326
|
The primary focus of this course is on neural networks and deep learning networks such as CNN, RNN, LSTM, and industry-accepted libraries required to train these models, such as TensorFlow and Keras. It also covers different variations of training and transporting these models using attention and transformers. (Prerequisite: CSCI484)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
Course ID: 5143
|
This course focuses on the use of probabilistic graphical models to represent complex domains using probability distributions. Using probabilistic graphical models to model large collections of random variables with complex interactions. Students will learn the key formalisms and main techniques in building probabilistic graphical models. And, how to use them to make predictions and support decision-making under uncertainty. Bayesian networks, directed and undirected graphical models, as well as their temporal extensions will be covered. Students will be introduced to causation and how it can be modeled. (Prerequisites: MATH302, MATH328, DATS301)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Choose 3 credit hours from this section.
Course ID: 5148
|
This course covers topics in advanced topics and methods such as unstructured data/information and big data. The theoretical background required for the integration of data mining and text analytics or text mining are explored. Students are exposed to additional topics such as the implementation and use of data lakes and ontology evaluation. (Prerequisites: DATS200, DATS201, DATS301)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5151
|
The Google Brain Team originally developed TensorFlow as a proprietary code. Today, TensorFlow is open source and freely available. This course teaches students how to use TensorFlow to build and run artificial neural networks. Convolutional and recurrent networks will be included as well regression. (Prerequisite: DATS431. DATS432 strongly recommended)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
The Business Intelligence Concentration 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.
Must take all courses for this section.
Course ID: 4372
|
This course is an overview of accounting concepts used by managers in a business environment intended for non-accounting majors with no accounting background. Topics include accounting concepts, users of accounting information, elements and purpose of financial statements, accrual accounting, internal control and basic financial analysis. Students must have access to Microsoft Word® and Microsoft Excel® software.
Microsoft Word® Microsoft Excel® are registered trademarks of Microsoft Corporation.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 2887
|
This course is designed to acquaint the student with the terminology, organization, and function of the American business system. The course will give the student a broad background about the contemporary American and global business environments as well as considering different business organizations, management principles and strategies. Topics covered include marketing, finance, personnel, customer relations, production and operations, e-business, world trade ventures, internal information systems and decision-making processes.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 4592
|
This course is designed to provide a foundational knowledge in analytics, and how it is used in business to strengthen the decision-making process. As technology has changed the landscape of business processes, it has also created a necessity for decision-makers to have the ability to use various tools to create, manipulate, and report data. Students will learn operational statistical theories, software options to work with data, and begin to integrate concepts into objective decision-making.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 4593
|
This course is designed to provide a intermediate knowledge in analytics, and how it is used in business to strengthen the decision-making process. Students will focus on managerial level of statistical methods, advanced Excel functionality, and continue to work on applying concept to strengthen the ability to integrate concepts into objective decision-making processes. (Prerequisite: BUSN250)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 4263
|
This course provides students opportunities for analysis, synthesis, and application of critical thinking applied to decision making at all levels in an organization. This course equips students with critical thinking skills to identify problems utilizing rational decision making. Students learn to solve organizational problems and provide strategic direction based on critical thinking.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Choose 3 credit hours from this section.
Course ID: 4594
|
This course is designed to lead students through various projects and business problem scenarios to enable them to apply concepts learned to quantify elements of alternative elimination, assess data pertinent to the overall decision-making process, and to gain and understanding of the different business functions and how data is used differently in each area. This course requires Microsoft Excel® 2010 or higher. (Prerequisite: BUSN350)
Microsoft Excel® is a registered trademark of Microsoft Corporation.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5155
|
This course provides students with knowledge about the complete risk assessment process. It begins with in-depth discussions about the mathematical and computational models supporting risk assessment. Then, covers a variety of situations where risk can have a great impact, e.g. extreme events. Hierarchical holographic modeling and related models are used to analyze subsystem risks and how those risks compound to affect an entire system. (Prerequisites: MATH328, DATS301)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
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.
Must take all courses for this section.
Course ID: 5315
|
This course provides an overview of types of problems that can be used with machine learning, as well as different variations of machine-learned methods such as supervised/unsupervised, batch/online, etc. The course discusses the main challenges of machine learning, notably the issue of data quality, as well as overfitting and underfitting data. (Prerequisite: DATS211 or CSCI345).
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5316
|
This course is focused on more advanced topics in machine learning, including clustering, dimensionality reduction, and the emerging applications of machine learning such as recommender systems, search in unstructured data, and time series analysis. (Prerequisite: DATS331 or CSCI381)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 5148
|
This course covers topics in advanced topics and methods such as unstructured data/information and big data. The theoretical background required for the integration of data mining and text analytics or text mining are explored. Students are exposed to additional topics such as the implementation and use of data lakes and ontology evaluation. (Prerequisites: DATS200, DATS201, DATS301)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5508
|
Sentiment analysis is a specialized form of natural language processing intended to determine opinions expressed in written text. This lab-based course is designed to implement topics covered in labs. The topics covered include the concepts and theories behind sentiment analysis and opinion mining. The course will also discuss the research approaches taken in sentiment analysis, knowledge-based techniques, statistical methods, and hybrid approaches. Tasks in sentiment analysis will be discussed and implemented through labs, such as determining an emotional scale and generating a sentiment score. Students will analyze positive and negative words and evaluate customer sentiment through text analysis and emotion recognition techniques. (Prerequisites: MATH302 and DATS411)
Course ID: 5509
|
This course provides the theoretical foundations, tools, and methods used to implement solutions through discrete event simulation, agent-based simulation, and other simulation techniques. It emphasizes the use of simulation models to explore different scenarios and develop effective processes for complex systems. The course incorporates analytical methods and demonstrates how different types of simulation models can be used to simulate operations and evaluate outcomes. Learners will engage with examples of real-world simulations to understand how to design, implement, and optimize simulation programs. (Prerequisite: DATS371)
Course ID: 4550
|
This course examines various linear optimization concepts and problem solving techniques commonly found in manufacturing, transportation, and military operations. The goal of optimization is to find the best possible solution to a problem given a number of constraints. The emphasis of this course is problem solving. This course includes the construction and analysis of real world problems and the application of various linear optimization techniques to find an optimal solution. An optimization software package will also be presented and used to solve problems. This course covers a wide range of linear optimization techniques. Topics include linear programming; the simplex algorithm and goal programming; sensitivity analysis and duality; problems in transportation and transshipment; network models; and integer programming.
(Prerequisite: MATH220)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
Must take the following in this Section:
Course ID: 5037
|
Information and Digital Literacy is designed to provide students with sustainable and usable skills essential to success in both academic and professional settings. Students will learn best practices to locate and evaluate sources, and effectively communicate using digital literacy to become proficient 21st century learners.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 4951
|
Martin Luther King, Jr, said, “If you can’t fly, then run. If you can’t run, then walk. If you can’t walk, then crawl. But whatever you do, you have to KEEP MOVING.” Making Writing Relevant is a composition course that will definitely keep you moving. It is not merely a required course; it is the type of course you will want to take because it moves you onto the path to success. Over eight-weeks, we will help you learn the most important components of a successful writer-communicator. We will teach you how to research properly, knowing you will need this skill in every course you take here at APUS and also in interactions in your professional and personal life. We will teach you the formatting style you will use in your field. We will provide a supportive community to strengthen and encourage you, and by the end of this nearly-all-you-need-to-know-about-writing course, you will be able to fly.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Must take the following in this Section:
Course ID: 3285
|
This course begins with a study of limits and continuity, which leads into the study of derivatives. Students will be taught to find the derivative of many functions using a variety of methods, including power rule, product rule, and chain rule.
Students will then learn how to tackle several different applications of derivatives, including optimization, curve sketching, approximations, and related rates. Finally, students will be introduced to integration and how it can be used to determine areas. (Prerequisite: MATH111, MATH112, or an equivalent course)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Must take all courses for this section.
Course ID: 5077
|
This course provides students with the basic knowledge and skills to handle and analyze data using a variety of methods, as well as different programming languages and tools. Students are introduced to current industry standard data analysis packages and tools – such as those in R/RStudio, Python, and Structured Query Language (SQL) – to prepare them for higher-level courses that rely heavily on the use of these analytic tools. (Prerequisites: MATH302 and MATH220)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 5078
|
This course provides students with the basic toolkit of statistical methods and models that practitioners use for regression, analysis of variance, and linear models. This toolkit could be based on Python® or on R. Topics include descriptive statistics/data summaries, inference in simple and multiple linear regression, residual analysis, estimation and testing of hypothesis, transformations, polynomial regression, model building with real data, nonlinear regression and linear models. The course is not mathematically advanced but covers a large volume of material. (Prerequisites: DATS200, MATH302 and MATH220)
Python® is a registered trademark of the Python Software Foundation.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
Course ID: 5079
|
This course provides an overview of data science including a foundation in research methodology. Data science is a data-driven process that provides descriptive, predictive, and prescriptive insight. Whether reporting on historical information or making predictions about future events, the goal of data science is to add value through analysis that informs. To meet this goal this course introduces a range of tools and methods including supervised and unsupervised techniques. These include techniques such as classification, rule-based association techniques, support vector machines, K-nearest neighbor, regression, and clustering techniques such as K-Means. (Prerequisite: DATS201)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
Course ID: 5139
|
Exploratory data analysis (EDA) plays a crucial role in the first stages of analysis. That is, the data determine what the appropriate analysis technique is rather than an analysis technique being applied to the data. During this course students develop skills in all aspects of exploratory data analysis including a wide variety of tools and techniques for pre-processing and cleaning data, including big data. This course also introduces students to evaluating and plotting/graphing data to evaluate the content and integrity of a data set. (Prerequisite: DATS200)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
Course ID: 5140
|
One of the most important functions in data science is the communication of the meaning in data. Data visualization is a core competency that enables that communication. This course introduces students to best practices in the visual and graphical representation of data and meaning. Design principles are emphasized as skills in visual communication develop. The specific tools and methods used in this course will vary depending on current industry standards and preferences. (Prerequisite: DATS200)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5114
|
Whereas Analytical Methods I primarily deals with continuous data, this course deals with methods and tools used to analyze categorical (discrete) data. For example, researchers analyze categorical data, e.g. using logistic regression, to determine the results of tests such as learning if a patient’s tumor cancerous or not, or whether a consumer will purchase a particular product or not. Specific attention will be paid to surveys and survey data. In addition, this course introduces generalized linear modeling. (Prerequisite: DATS211)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 5115
|
This course continues to expand the knowledge, skills and abilities of students by two paths; first through the design of experiments required to acquire specified data, and second using carefully designed experiments to establish causal effects. Students will take a deep dive into the differences between correlation and causality. They will learn the critical thinking skills required to assert the reliability of data acquired. (Prerequisite: DATS301)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
Course ID: 5145
|
This course provides students with an introduction to modeling and simulation at the undergraduate or at the graduate level. It includes an introduction to discrete event simulation (DES) as well as simulation methodology, input data modeling, output data analysis, and a broad overview of DES tools. It also includes an introduction to continuous simulation (CS) as well as simulation methodology, differential equation models, numerical solution techniques, and a broad overview of CS tools. (Prerequisites: MATH240, MATH302, MATH328)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5117
|
This course completes the three-course sequence in Data Science. This advanced course takes students through the application of more advanced methods in regression and time series models. It includes discussions about causal inference, and a wide-range of time series models. This course emphasizes tools and methods used to capture key patterns and generate insight from data. (Prerequisite: DATS311)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5153
|
This course focuses on the Bayesian approach to probability and statistics and applies it to tools and methods used in data science. This course starts with a brief review of the tenets of probability that form the foundation for Bayes Theorem. Next, it discusses Bayes Theorem in-depth. Then, it considers the Bayesian approach to inference, examines the Naïve Bayes model, and develops Bayesian regression. (Prerequisite: MATH328)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 5154
|
This course introduces students to generalized linear models that extend the linear modeling framework to allow response variables that are not normally distributed. Estimation and inference are included. Continuous data is considered. However, the primary focus is on binary or count data. Poisson and quasi-Poisson models are discussed. (Prerequisites: MATH227, MATH328, DATS301)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 4086
|
This course introduces students to the fundamental concepts of discrete mathematics. The course provides a foundation for the development of many computer related concepts and more advanced mathematical concepts found in electrical engineering or computer science courses. Important applications in the computer science and engineering disciplines will be presented. Topics include: fundamentals (basic tools for discrete math); logic; methods of proof; graphs and sets; functions; relations and equivalences; recursive relations; polynomial sequences; induction; combinatorics; counting; and probability. (Prerequisites: MATH110, MATH111, or MATH225)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 4538
|
This course presents vectors, matrices, determinants, eigenvalues, and eigenvectors; and how these concepts may be used and applied. The emphasis of the course will be on understanding the concepts and methods of linear algebra, as well as solving problems and understanding how linear algebra is used in real world applications. (Prerequisite: MATH225)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 4085
|
This course builds on the concepts introduced in Calculus I and will expand students’ ability to integrate more functions using a variety of techniques, including substitution, integration by parts, and trigonometric substitutions. These skills are then used to find the area between curves, the volume of shapes created by rotating area, arc length, and surface area. The course also introduces sequences and series and includes several methods for determining when the series and sequences converge. (Prerequisite: MATH225)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 4081
|
This course is designed to extend calculus concepts to functions with more than two variables. Students will perform vector calculations encompassing operations such as the dot product and cross product. They will also use these skills in several applications, including arc length and motion.
Students will then apply their calculus skills to multivariable equations to find the partial derivatives and double and triple integrals. Finally, they will apply their vector skills to vector calculus and work with vector fields, line integrals, and surface integrals. (Prerequisite: MATH226)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 4057
|
MATH240 is introduction to differential equations. It is designed to introduce students to the basic concepts and techniques of differential equations. The course covers the standard materials addressed in the first semester of college differential equations to include: first and second order differential equations, Laplace transforms and differential equations with variable coefficients. Problems have been selected to illustrate the applications of these techniques across a wide range of areas of science, technology, and economics. It is essential for engineering, science, and economics. Increasingly, applications in business management and related fields also employ the calculus. (Prerequisite: MATH227 or equivalent)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 01/26/26 - 07/03/26 | 07/06/26 - 10/25/26 | Summer 2026 Session A | 16 Week session |
| 03/30/26 - 09/04/26 | 09/07/26 - 12/27/26 | Summer 2026 Session C | 16 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 02/21/27 | Fall 2026 Session K | 16 Week session |
Course ID: 3291
|
This is an interactive course designed to help students achieve a greater understanding of the statistical methods and models available to analyze and solve the wide variety of problems encountered in business, science, medicine, education, the social sciences, and other disciplines. Successful completion of this course will provide students with a working knowledge of the principles of both descriptive and inferential statistics, probability, averages and variations, normal probability distributions, sampling distributions, confidence intervals, statistical hypothesis tests, and correlation and regression analyses. The emphasis of the course will be on the proper use of statistical techniques and their application in real life -- not on mathematical proofs. This course will use Microsoft Excel for some of the work. Students should have a basic familiarity with Excel and have access to this software application. MATH120 is the recommended mathematics general education course for students who will be required to take additional statistics courses such as MATH302 Statistics as part of their program of study. (Prerequisites: MATH110, MATH111, MATH120, or MATH225)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 4542
|
This course introduces students to the basic concepts and applications of probability theory. An understanding of probability theory is essential to statistics, which is the fundamental basis of how all research is done, from science to medicine to business, marketing, and governmental politics. Probability theory is also essential to such disciplines as mathematics, finance, artificial intelligence, and even legalized gambling (such as state lotteries). Examples of applications problems from these areas are included in the course, with a focus on understanding the concepts and methods of probability theory, as well as solving problems taken from real world applications.
(Prerequisite: MATH226)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 4551
|
This course will introduce to students analysis of categorical data, log linear models for two- and higher-dimensional contingency tables, and logistic regression models. Also, students will analyze aspects of multivariate analysis to include random vectors, random sampling, multivariate normal distribution, inferences about the mean vector and MANOVA. (Prerequisites: MATH328 and MATH302)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 4554
|
This course is delivered online and is organized into distinct parts. This course will begin with Design of Experiments (DOE) methodology and statistical inference. The design of single factor, factorial, nested and nested factorial experiments will be taught. Quantitative and qualitative factors will be introduced to simulate real situations that are encountered in operations being explored. Students will learn how to set up and solve fixed, random, and mixed models with two or more factors. Practical applications are provided throughout the course.
(Prerequisite: MATH340)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5177
|
This course will expose students to how since the earliest days of mankind, technology and technological advances have helped to mold our society and guide our prosperity, but also to the goals of the society in driving the development and progression of technology as well. This course will review the various periods of time, where the world leaped forward in their understanding of the world around them and how they could harvest the world around them to advance their own world. This course will also reveal the potential conflicts of complex relationships between technology and society, looking at such topics as global warming, artificial intelligence, nuclear weapons, and many other potentially dangerous technological advances.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Course ID: 5179
|
This course offers students a unique peek into the world of artificial intelligence and analytics without all of the mathematics. The course will review the advancement of artificial intelligence technology in such fields as facial recognition, neural networks, self-driving vehicles, and the controversial Deep Fakes. The course will then continue to delve into the analytics and algorithms that drive our daily lives and that we willing feed more and more data to. From Facebook Likes to shopping cart analysis and prediction, this course will review the ways we interact knowingly and unknowingly with these technological advances and the effects that they are having on humanity as a whole.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Choose 3 credit hours from this section.
Course ID: 5506
|
This course introduces Python to students with very little programming experience. It covers the basics of programming, including core elements of programs, data types, and functions. Topics also include Python syntax, working with variables, and creating simple Python code. Additional areas of focus include debugging, runtime, and an overview of computational complexity. (Prerequisite: DATS200 or chair override)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 02/23/26 - 07/31/26 | 08/03/26 - 09/27/26 | Summer 2026 Session I | 8 Week session |
| 05/25/26 - 10/30/26 | 11/02/26 - 12/27/26 | Fall 2026 Session I | 8 Week session |
Course ID: 5147
|
This course discusses the legal, policy and ethical implications of data including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Examines these issues over the full data-science life cycle; data collection, storage, processing, analysis, and use. Includes current topics such as: legal and policy constraints; data collection methods and institutions; as well as technical, legal, and market approaches to mitigating and managing concerns.
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5507
|
This course explores how Python, one of the most popular programming languages today, is applied in data analysis, image processing, machine learning, and parallelism. As a lab-based course, it emphasizes significant time spent programming. Topics include handling data structures such as Python lists, NumPy arrays, and Pandas DataFrames. The course also focuses on functions, flow control, and building on data visualization concepts introduced in DATS225 – Data Visualization. (Prerequisite: DATS281)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 04/27/26 - 10/02/26 | 10/05/26 - 11/28/26 | Fall 2026 Session B | 8 Week session |
Course ID: 5152
|
This course provides an introduction to optimization and machine learning, i.e. how machines learn. It takes an in-depth look at objective, or loss, functions and how they are used to reduce error through feedback. It also takes a look at how that feedback enables machines to learn. Students gain an appreciation of the similarities in optimization and machine learning, as well as the differences. It also takes a look at all the challenges in training machine models including the local minima or maxima that affect machine learning. In addition, it discusses different methods that are applied to optimization and machine learning, e.g. gradient methods. (Prerequisites: MATH240, MATH328, DATS301)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Select any courses that have not been used to fulfill major requirements. Credits applied toward a minor or certificate in an unrelated field may be used to fulfill elective credit for the major.
Must take all courses for this section.
Course ID: 5158
|
This Capstone course is intended to be the last course taken by seniors prior to completing their bachelor’s degree. It is intended to give seniors the opportunity to demonstrate the knowledge and skills they have gained throughout their program of study. At the start of this course, students will be presented with a complex, real-world problem. As they consider this problem, they will restate it in a format consistent with conducting a data analysis. They will acquire the data needed and pre-process that data as required. They will determine the best method of analysis depending on the problem and the data. They will conduct the analysis obtaining preliminary results, and evaluate the analysis and the results to determine if any adjustments to parameters or hyperparameters need to be made. They will obtain the final results, i.e. the solution to the problem. Last, they will generate a written report and an oral presentation that discusses the problem, the data, the analysis including any parameters or hyperparameters used, the results, and their evaluation of the analysis and results. It is intended that the report and presentation be of sufficiently high quality to serve as part of the student’s portfolio for job interviews or applications to graduate school. (Intended to be the last course in the program)
| Registration Dates | Course Dates | Session | Weeks |
|---|---|---|---|
| 06/29/26 - 12/04/26 | 12/07/26 - 01/31/27 | Fall 2026 Session D | 8 Week session |
Admission Requirements
- 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.
- All APU undergraduate programs require a minimum of a high school diploma or equivalent (i.e., GED). Please read all undergraduate admission requirements before applying to this program and be prepared to submit the required documentation.
- There is no fee to complete the APU admission application. View steps to apply.
Need Help?
Selecting the right program to meet your educational goals is a key step in ensuring a successful outcome. If you are unsure of which program to choose, or need more information, please contact an APU admissions representative at 877-755-2787 or [email protected].
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