Ready to Start Your WGU Journey?
Next Start Date: September 1
Start Dates the 1st of Every Month After September 1
Data Analytics Courses
Program consists of 42 courses
At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you know your stuff.
This unique Bachelor of Science Data Analytics degree program perfectly balances three main skills to help students find success:
- Programming skills: Scripting, data management, data wrangling, Python, R, and machine learning, and systems thinking.
- Math skills: Statistical analysis, probability, discrete math, and data science techniques.
- Business influence skills: Leadership, communication, critical thinking, visualization, change management, design thinking, and storytelling.
The B.S. Data Analytics program is an all-online program. You’ll complete program requirements independently, with instruction and support from WGU faculty. You’ll be expected to complete at least 12 competency units for each 6-month term. Each course is typically three or four units. There’s no limit on the number of units you can complete each term, so the more courses you complete, the quicker you can finish your program. This program was designed, and is regularly updated, with input from key experts on our WGU Analytics Advisory Board—a group committed to ensuring that the courses at WGU are industry-relevant, timely, and impactful.
Advanced Data Management enables learners to extract and analyze raw data. Skillful data management allows organizations to discover and explore data in ways that uncover trends, issues, and their root causes. In turn, businesses are better equipped to capitalize on opportunities and more accurately plan for the future. As organizations continue to extract larger and more detailed volumes of data, the need is rapidly growing for IT professionals who possess data management skills. The skills gained in this course include performing advanced relational data modeling as well as designing data marts, lakes, and warehouses. This course will empower learners with the skills to build business logic at the database layer to employ more stability and higher data-processing speeds. Learners will gain the ability to automate common tasks to summarize and integrate data as they prepare it for analysis. Data Management - Foundations is a prerequisite for this course.
Data Management Foundations offers an introduction in creating conceptual, logical and physical data models. Students gain skills in creating databases and tables in SQL-enabled database management systems, as well as skills in normalizing databases. No prerequisites are required for this course
Data Management - Applications covers conceptual data modeling and introduces MySQL. Students will learn how to create simple to complex SELECT queries, including subqueries and joins, and how to use SQL to update and delete data. Topics covered in this course include exposure to MySQL; creating and modifying databases, tables, views, foreign keys and primary keys (FKs and PKs), and indexes; populating tables; and developing simple Select-From-Where (SFW) queries to complex 3+ table join queries. The following course is a prerequisite: Data Management - Foundations.
Introduction to Programming in Python introduces skills in creating Python scripts with basic programming concepts. Learners will be able to create control flow with functions and loops, and to implement code with packages, modules, and libraries.
Scripting and Programming - Foundations introduces programming basics such as variables, data types, flow control, and design concepts. The course is language-agnostic in nature, ending in a survey of languages, and introduces the distinction between interpreted and compiled languages. Learners will gain skills in identifying scripts for computer program requirements and in using fundamental programming elements as part of common computer programming tasks. Learners will also gain an understanding of the logic and outcome of simple algorithms.
Network and Security - Foundations introduces learners to the basic network systems and concepts related to networking technologies. Learners will gain skills in applying network security concepts for business continuity, data access, and confidentiality, and in identifying solutions for compliance with security guidance.
Version control is critical to maintaining software and enabling scalability solutions. A best practice for any programming project that requires multiple files uses version control. Version control enables teams to have collaborative workflows and enhances the software development lifecycle. This course introduces students to the basics of publishing, retrieving, branching, and cloning. There are no prerequisites for this course.
Data Structures and Algorithms I covers the fundamentals of dynamic data structures, such as bags, lists, stacks, queues, trees, and hash tables, and their associated algorithms. This course discusses object-oriented design and abstract data types as design paradigms. The course emphasizes problem-solving and techniques for designing efficient, maintainable software applications. Students will implement simple applications using the techniques learned. This course has no prerequisites.
Web Development Foundations introduces students to web design and development using HTML, XML, and Cascading Style Sheets (CSS), the foundational languages of the web. This course also covers how to troubleshoot problems using developer tools and integrated development environments commonly employed in web development. There are no prerequisites for this course.
Cloud Foundations introduces learners to real-world issues and practical solutions to cloud computing. This course covers the business value of cloud computing, examining cloud types, the steps to successful cloud adoption, and the effect cloud adoption has on IT service management, as well as the risks and consequences of implementing cloud solutions. This course prepares learners for the AWS Certified Practitioner certification exam. There are no prerequisites for this course.
In this course, students will build on industry standard concepts, techniques, and processes to develop a comprehensive foundation for project management activities. During a project's life cycle, students will develop the critical skills necessary to initiate, plan, execute, monitor, control, and close a project. Students will apply best practices in areas such as scope management, resource allocation, project planning, project scheduling, quality control, risk management, performance measurement, and project reporting. This course prepares students for the following certification exam: CompTIA Project+.
IT Leadership Foundations is an introductory course that provides students with an overview of organizational structures, communication, and leadership styles specific to information technology in organizations. It also introduces students to some of the power skills that help make successful IT professionals, including time management, problem solving, and emotional intelligence. Students in this course explore their own strengths and passions in relation to the field. There are no prerequisites for this course.
Analytics is the creative use of data and statistical modeling to tell a compelling story that not only drives strategic action, but also results in business value. Introduction to Analytics examines data analytics as a discipline and the various roles and functions within the field. You will expand your knowledge about what analytics is and develop a basic understanding of statistics, analysis, problem solving, and programming concepts.
Data and Information Governance provides learners with the knowledge that establishing rules of engagement, policies, procedures, and data stewardship is essential to exercising organizational control over—and extracting maximum value from—its data assets. Good data governance helps an organization lower costs, create efficiencies, and achieve its strategic goals and objectives. Data governance provides a framework for properly managing information across the entire data lifecycle and establishes strategies in support of disaster recovery and continuity of operations. This course will prepare IT professionals to assist their organization in the definition and implementation of best practices related to the planning and implementation of managed systems that meet business, technical, security, auditing, disaster recovery, and business continuity requirements.
Data Analytics Applications covers advanced concepts across the various phases of the data product lifecycle. You will learn to choose and apply appropriate techniques for data management and data manipulation, statistical analysis, visualization, and data governance concepts to satisfy business needs.
Introduction to Data Science introduces the data analysis process and common statistical techniques necessary for the analysis of data. Students will ask questions that can be solved with a given data set, set up experiments, use statistics and data wrangling to test hypotheses, find ways to speed up their data analysis code, make their data set easier to access, and communicate their findings.
Data Analysis with R focuses on exploratory data analysis (EDA) utilizing R. EDA is an approach for summarizing and visualizing the important characteristics of a data set. In this course you will develop skills in R programming to acquire and load data sets, create appropriate statistical summaries of data, and create data visualizations to help uncover and communicate insights about data using R.
Machine Learning presents the end-to-end process of investigating data through a machine learning lens. Topics covered include: supervised and unsupervised learning algorithms, features that best represent data, commonly-used machine learning algorithms, and methods for evaluating the performance of machine learning algorithms.
Machine Learning DevOps focuses on the software engineering fundamentals needed to successfully streamline the deployment of data and machine learning models in a production-level environment. Students will build the DevOps skills required to automate the various aspects and stages of machine learning model building and monitoring over time.
Big Data Foundations provides an in-depth introduction to big data concepts, terminology, and applications. You will learn the risks and challenges of working with extremely large data sets. The course introduces tools and techniques for working with big data. The course covers selection criteria for relational and non-relational data architectures and cloud-native data storage concepts. It also provides a historical perspective on the evolution of big data storage approaches. Data warehousing, data lakes, and data lakehouses are introduced, and design principles for each are explained. Learners design aspects of big data architecture and big data processing to address given business requirements.
Data Wrangling elaborates on concepts covered in Introduction to Data Science, helping to develop skills crucial to the field of data science and analysis. It explores how to wrangle data from diverse sources and shape it to enable data-driven applications—a common activity in many data scientists' routine. Topics covered include gathering and extracting data from widely-used data formats, assessing the quality of data, and exploring best practices for data cleaning.
Data Visualization covers the application of design principles, human perception, color theory, and effective storytelling in the context of data visualization. It addresses presenting data to others and advancing technology with visualization tools enabling data scientists to share their findings and support organizational decision-making processes. Additionally, this course focuses on how to visually encode and present data to an audience.
Change Management provides an understanding of change and an overview of successfully managing change using various methods and tools. Emphasizing change theories and various best practices, this course covers how to recognize and implement change using an array of other effective strategies, including those related to innovation and leadership. Other topics include approaches to change, diagnosing and planning for change, implementing change, and sustaining change.
Scripting and Programming - Applications for undergraduates explores the various aspects of the Python programming language by examining its syntax, the development environment, and tools and techniques to solve some real-world problems. Introduction to Programming in Python is a prerequisite for this course.
Hardware and Operating Systems prepares learners for concepts in software engineering by providing a foundation of understanding in computer architecture, the history of computing architectures, and operating systems. Additional topics covered include hardware and software stacks and how to choose appropriate hardware and software solutions to meet both functional and non-functional business requirements.
Fundamentals of Spreadsheets and Data Presentations offers learners an overview of the use of spreadsheet functions and methods for presenting data within spreadsheets. Learners will have the opportunity to explore features and uses of MS Excel and apply the tools to situations they may encounter while studying in their program. They will also be introduced to real world uses and tools to collect, organize and present data.
This is Introduction to Physical and Human Geography, a three-module course that addresses the question of what geography really is in today's complex world; how migration affects—and has been affected by—geography; and one of the biggest present problems related to geography: climate change. Because the course is self-paced, you may move through the material as quickly or as slowly as you need to, with the goal of demonstrating proficiency in the five competencies covered in the final assessment. If you have no prior knowledge of this material, you can expect to spend 30–40 hours on the course content.
Influential Communication through Visual Design and Storytelling provides learners with foundational visual design and storytelling techniques to influence and create a lasting impression on audiences. Learners will first explore how human behavior is influenced by visuals and when to apply visual techniques to better communicate with audiences. Next, learners will learn techniques for creating compelling stories that create memorable images within the audience's mind. Ultimately, learners who master these skills will be well-positioned to apply their visual and storytelling techniques to not only better communicate their thoughts and ideas to an audience, but to also influence or motivate them.
Introduction to Systems Thinking provides learners with the skills required to engage in a holistic systems-based approach to analyzing complex problems and solutions. This course introduces the foundational concepts and principles of systems thinking and provides opportunities to use a systems thinking approach to analyze and evaluate real-world case studies. The course will culminate with using systems thinking to develop a solution to an authentic complex problem. This course has no prerequisites, but general education math (C955 or C957) is preferred. Because the course is self-paced, learners may move through the material as quickly or as slowly as needed, with the goal of demonstrating proficiency in the five competencies covered in the final assessment. If learners have no prior knowledge of this material, they can expect to spend 30 to 40 hours on the course content.
Welcome to Introduction to Communication: Connecting with Others! It may seem like common knowledge that communication skills are important, and that communicating with others is inescapable in our everyday lives. While this may appear simplistic, the study of communication is actually complex, dynamic, and multifaceted. Strong communication skills are invaluable to strengthening a multitude of aspects of life. Specifically, this course will focus on communication in the professional setting, and present material from multiple vantage points, including communicating with others in a variety of contexts, across situations, and with diverse populations. Upon completion, you will have a deeper understanding of both your own and others’ communication behaviors, and a toolbox of effective behaviors to enhance your experience in the workplace.
Welcome to Composition: Writing with a Strategy! In this course, you will focus on three main topics: understanding purpose, context, and audience, writing strategies and techniques, and editing and revising. In addition, the first section, will offer review on core elements of the writing process, cross-cultural communication, as well as working with words and common standards and practices. Each section includes learning opportunities through readings, videos, audio, and other relevant resources. Assessment activities with feedback also provide opportunities to check your learning, practice, and show how well you understand course content. Because the course is self-paced, you may move through the material as quickly or as slowly as you need to gain proficiency in the seven competencies that will be covered in the final assessment. If you have no prior knowledge or experience, you can expect to spend 30-40 hours on the course content.
American Politics and the U.S. Constitution examines the evolution of representative government in the United States and the changing interpretations of the civil rights and civil liberties protected by the Constitution. This course will give candidates an understanding of the powers of the branches of the federal government, the continual tensions inherent in a federal system, the shifting relationship between state and federal governments, and the interactions between elected officials and the ever-changing electorate. This course will focus on such topics as the role of a free press in a democracy, the impact of changing demographics on American politics, and the debates over and expansion of civil rights. Upon completion of the course, candidates should be able to explain the basic functions of the federal government, describe the forces that shape American policy and politics, and be better prepared to participate in America’s civic institutions. This course has no prerequisite.
Applied Probability and Statistics is designed to help students develop competence in the fundamental concepts of basic statistics including: introductory algebra and graphing; descriptive statistics; regression and correlation; and probability. Statistical data and probability are often used in everyday life, science, business, information technology, and educational settings to make informed decisions about the validity of studies and the effect of data on decisions. This course discusses what constitutes sound research design and how to appropriately model phenomena using statistical data. Additionally, the content covers simple probability calculations, based on events that occur in the business and IT industries. No prerequisites are required for this course.
Applied Algebra is designed to help you develop competence in working with functions, the algebra of functions, and using some applied properties of functions. You will start learning about how we can apply different kinds of functions to relevant, real-life examples. From there, the algebra of several families of functions will be explored, including linear, polynomial, exponential, and logistic functions. You will also learn about relevant, applicable mathematical properties of each family of functions, including rate of change, concavity, maximizing/minimizing, and asymptotes. These properties will be used to solve problems related to your major and make sense of everyday living problems. Students should complete Applied Probability and Statistics or its equivalent prior to engaging in Applied Algebra.
This course provides students an introduction to using the scientific method and engaging in scientific research to reach conclusions about the natural world. Students will design and carry out an experiment to investigate a hypothesis by gathering quantitative data. They will also research a specific ecosystem using academic sources and draw conclusions from their findings.
In this course you will learn key critical thinking concepts and how to apply them in the analysis and evaluation of reasons and evidence. The course examines the basic components of an argument, the credibility of evidence sources, the impact of bias, and how to construct an argument that provides good support for a claim. The course consists of an introduction and four major sections. Each section includes learning opportunities through readings, videos, audio, and other relevant resources. Assessment activities with feedback also provide opportunities to check your learning, practice, and show how well you understand course content. Because the course is self-paced, you may move through the material as quickly or as slowly as you need to gain proficiency in the four competencies that will be covered in the final assessment. If you have no prior knowledge or experience, you can expect to spend 30-40 hours on the course content.
Design Thinking for Business examines the design thinking methodology for solving complex problems. This course introduces students to design thinking as a human-centered approach to problem-solving and innovation that draws upon empathy and creativity to develop solutions to complex problems. Students will explore the principles and stages of design thinking and analyze the use of design thinking in developing solutions through real-world scenarios.
Health, Fitness, and Wellness focuses on the importance and foundations of good health and physical fitness—particularly for children and adolescents—addressing health, nutrition, fitness, and substance use and abuse.
Discrete Math-Logic is designed to help students develop competence in the use of logic and proofs and Boolean Algebra and Boolean functions. Applied Probability and Statistics and Applied Algebra are prerequisites for this course.
Ethics in Technology examines the ethical considerations of technology use in the 21st century and introduces students to a decision-making process informed by ethical frameworks. Students will study specific cases related to important topics such as surveillance, social media, hacking, data manipulation, plagiarism and piracy, artificial intelligence, responsible innovation, and the digital divide. This course has no prerequisites.
Discrete Math: Functions and Relations is designed to help students develop competence in the use of abstract discrete structures fundamental to systems networking. In particular, this course will introduce students to set theory, finite sequences, series, and relations. Discrete Math: Logic, Applied Probability and Statistics, and Applied Algebra are prerequisites for this course.
Program consists of 42 courses
At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you know your stuff.
Special requirements for this program
At the end of your program, you will complete a capstone project that represents the culmination of all your hard work—a research project applicable to the field of data analytics that aims to expand the body of knowledge in the profession.
WGU vs. Traditional Universities
Compare the Difference
On Your Schedule
No class times, no assignment deadlines. You are in charge of your learning and schedule. You can move through your courses as quickly as you master the material, meaning you can graduate faster.
The data analytics bachelor's degree at WGU is 100% online, which means it works wherever you are. You can do your coursework at night after working at your full-time job, on weekends, while you're traveling the world or on vacation—it's entirely up to you.
One important measure of a degree’s value is the reputation of the university where it was earned. When employers, industry leaders, and academic experts hold your alma mater in high esteem, you reap the benefits of that respect. WGU is a pioneer in reinventing higher education for the 21st century, and our quality has been recognized.
3rd Party Data Analytics Certifications Included
Industry-recognized certifications from organizations such as CompTIA, Udacity, and AWS are included in the degree program. Udacity Nanodegree holders enjoy ongoing support from Udacity that includes employment placement, résumé support, networking and LinkedIn guidance, and more.
The B.S. in Data Analytics will also spotlight student choice to balance business influence skills, expand occupation set and ensure strong market alignment. These optional industry certifications are in design thinking and change management.
Earning certifications on the path to your degree gives you the knowledge, skills, and credentials that will immediately boost your résumé—even before you complete your degree program—without adding additional cost or time.
Additionally, this program includes two WGU certificates, Data Analyst Practitioner and Data Engineering Practitioner. These WGU certificates are embedded within your coursework and can be added to your résumé before you even finish your degree program.
- Cloud Practitioner
- MSI Change Management
COST & TIME
An Affordable Online Data Analytics Degree
By charging per six-month term rather than per credit—and empowering students to accelerate through material they know well or learn quickly—WGU helps students control the ultimate cost of their degrees. The faster you complete your program, the less you pay for your degree.
A College Degree Within Reach
There is help available to make paying for school possible for you:
A Different Way to Learn: Degree Programs Designed to Fit Your Life—and All the Demands on Your Time
Professional responsibilities. Family obligations. Personal commitments. At WGU, we understand schedules are tight and often unpredictable for adult students. That’s why we offer a flexible, personalized approach to how education should be. No rigid class schedules. Just a solid, career-focused teaching program that meshes with your current lifestyle. You'll be challenged. You'll work hard. But if you commit yourself and put in the hours needed, WGU makes it possible for you to earn a highly respected degree as a busy working adult.
Return on Your Investment
On average, WGU graduates see an increase in income post-graduation
Average income increase from all degrees in annual salary vs. pre-enrollment salary. Source: 2022 Harris Poll Survey of 1,542 WGU graduates.
Survey was sent to a representative sample of WGU graduates from all colleges. Respondents received at least one WGU degree since 2017.
Get Your Enrollment Checklist
Download your step-by-step guide to enrollment.
Get Your Questions Answered
Talk to an WGU Enrollment Counselor.
FAQs about Data Analytics
- General IT Program Questions
- Data Analytics Questions
- Data Analytics Advisory Board
You should speak with an Enrollment Counselor. WGU can often provide advice or resources to help a prospective student fulfill enrollment prerequisites.
When you enroll in a WGU degree program, our goal is to see you through to graduation. Admission requirements are designed to increase your likelihood of success. Years of data and experience with the nontraditional students WGU serves have shown us how various types of academic and professional experience can be highly important in helping a student persist to graduation. Industry certifications are one of many ways a student can meet eligibility.
WGU has an obligation to our graduates—and their current and future employers—to ensure WGU alumni have mastered the most up-to-date, current competencies and skills needed in the workplace. Recency of certifications helps us ensure that students have demonstrated competency in skills as they are needed in today's working world.
As a full-time student, you will be required to maintain a minimum pace of 12 competency units (CUs) per term for undergraduate programs or 8 CUs per term for graduate programs. However, there is no maximum speed—once you complete a course, you move immediately to the next, and you complete a course not by waiting for the syllabus, the professor, or the rest of the class. You progress by learning the material and proving it—so you can move through your coursework at the speed of your own learning and experience.
Instructors are highly educated, experienced experts in the subject matter of a course. Unlike in a traditional university where going to class means listening to an instructor lecture while you take notes and try to learn in a group setting, WGU's Instructors provide one-on-one instruction and support when you need it—tailoring the instruction to your precise needs when you need it. Instructors also provide additional resources, lead topical discussions in online communities, and find countless other ways to bring a specific course to life for students.
The best data analytics course will entirely depend on what you are hoping to learn! When you pursue a degree in data analysis you will learn a wide variety of things, including:
- IT Leadership
- Data Governance
- Data Systems Administration
- Advanced Data Management
- Introduction to Data Science
- Data Wrangling
- Data Analysis with R
- Machine Learning
- Data Visualization
- Introduction to Programming in Python
- Scripting and Programming
- Web Development Foundations
A degree in data analytics is well worth the time and cost for students. When you earn a bachelor's degree in data analytics you gain valuable credentials and experience that can prepare you for a wide variety exciting careers. Earning a degree can help you earn more money, be prepared for a promotion or career move, and give you increased stability in your career.
On average data analysts earn more than $70,000 per year, and the industry is expected to grow 20% by the year 2029. Both of these factors point to strong salary levels and high stability in the career, making it a good choice for many.
Data science and data analytics as IT fields are expected to grow 15-20% in the next few years. These professions are in high demand, especially when compared to the national average for job growth which is about 5%. More industries and organizations are understanding the need for highly trained data analysts and scientists to help drive decision-making, contributing to the high growth of these professions.
A bachelor’s degree in data analytics and management will qualify you for a number of entry-level data analyst positions. These positions include data engineer, operations analyst, data analyst, analytics engineer, and digital marketing analyst. You will need strong analytical skills with competencies in areas including statistics, finance, math, computer science, and economics. A data analyst degree will give you the skills and expertise you need to excel and progress in your career in data analytics.
A data analytics degree may qualify you for the following jobs:
- Data engineer
- Operations analyst
- Data analyst
- Analytics engineer
- Digital marketing analyst
Data Analytics Advisory Board Members
• Boaz Hillebrand, Senior Analytics Manager, People Insights – Expedia Group
• John Smits, VP of Worldwide Revenue Operations – Juniper Networks
• Ken Yu Zhang, PhD, Executive Director of Research Technology Data Science – Morgan Stanley
• Mandy Plante, Senior Director of Global Analytics – eBay
• Mohican Laine, VP of Data & Analytics – Daz 3D
• Nolan Hill, SVP of HR Analytics & Data Governance – Bank of America
• Stephen Gatchell, Director of Data Advisory – BigID
Ready to Start Your WGU Journey?