Online Masters in Data Analytics | WGU
Source: http://www.wgu.edu/online-it-degrees/data-analytics-masters-program.html
Archived: 2026-04-23 17:15
Online Masters in Data Analytics | WGU
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Master of Science
Online Master's in Data Analytics
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OVERVIEW
Lead the Analytics Lifecycle with a Master's Degree in Data Analytics
Make your next career move and maximize your earning potential with WGU’s Master of Science, Data Analytics (MSDA) program. This online degree program provides a depth of skills for your data analytics career in the following areas:
Data Analytics Lifecycle
Data Science and Advanced Analytics Applications
Data Mining Techniques
Machine Learning
Data Management
Business Influence
We use cutting-edge technology to help you learn about machine learning, modern analytic tools & languages
(Python, R, SQL, and Tableau)
, and more! Additionally, this unique program allows you to choose from a range of datasets representing different industry-specific themes. Learn exactly what you want and need for your career!
Choose Your Specialization
The WGU data analytics program features 3 specialization options
for students to choose from. The concentration areas allow students to dive deeper into their area of interest, better preparing them for their career goals.
Data Science:
This specialization focuses on machine learning, optimization of data, and advanced analytics understanding.
Data Engineering:
This specialization focuses on cloud engineering, the processing of data, and analytics at scale.
Decision Process Engineering:
This concentration focuses on project management, process optimization, and decision intelligence related to data analytics.
At WGU, we use a ‘three-lever’ approach to data analytics, surgically incorporating programming, math, and business influence skills throughout the program. Different roles in analytics will have different combinations of these levers. You will navigate to the role that best fits your interests & passions by selecting the concentration of your choice. Our game-changing approach to data analytics means that you will be equipped with the experience you need to create change and make an impact in any industry.
Have questions?
Attend our prospective student webinar to learn more about our programs.
Listen to 'Which MSDA Concentration is Right for Me?' Podcast
Unsure Which Specialization to Choose?
The data science specialization
is ideal for those who want to focus on statistical and programming approaches in machine learning, neural networks, and numerical optimization.
Learn more about this specialization through our Data Analytics podcast.
The data engineering specialization
is ideal for those who want to support analytics through cloud-native databases, data processing, and analysis approaches.
Learn more about this specialization through our Data Analyics podcast.
The decision process engineering specialization
is ideal for those who want to implement business changes through project management, business process engineering, and decision intelligence.
Learn more about this specialization through our Data Analytics podcast.
View Data Science Specialization
View Data Engineering Specialization
View Decision Process Engineering Specialization
This data analytics degree program focuses on both
theory
and
application
, allowing you to “learn by doing” as you complete data analytics projects in stages, known as the
data analytics lifecycle
.
The Master of Science in Data Analytics program's core courses dive deeply into each of these lifecycle stages
(sourcing data, cleaning data, data mining, descriptive & predictive analytics, and visualization).
70% of graduates finish within
23 Months*
WGU lets you move more quickly through material you already know and advance as soon as you're ready. The result: You may finish faster.
*WGU Internal Data
Flexible Schedule
Tuition per six-month term is
$4,565
Tuition charged per term—rather than per credit—helps students control the ultimate cost of their degree. Finish faster, pay less!
Cost & Time
Certifications that may transfer
1
Your Oracle SQL Expert certification may waive course requirements.
Certifications
Ready to Start Your WGU Journey?
Next Start Date:
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Start Dates the 1st of Every Month
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COURSES
Data Analytics Courses
Program consists of 11 courses
At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you know your stuff.
View Data Science Program Guide
View Data Engineering Program Guide
View Decision Process Engineering Program Guide
The WGU M.S. Data Analytics degree program was designed, and is regularly updated, with input from the experts on our Data Analytics Advisory Board. This ensures that you learn best practices for the latest developments in data analytics.
This data analytics degree program is composed of the following courses. You will typically complete them one at a time as you make your way through your program, working with your Program Mentor each term to build your personalized Degree Plan. You’ll work through each course as quickly as you can study and learn the material. As soon as you’re ready, you’ll pass the assessment, complete the course, and move on. This means you can finish as many courses as you're able in a term at no additional cost.
The M.S. Data Analytics degree program is an all-online program that you will complete through independent study with the support of WGU faculty. You will be expected to complete at least 8 competency units (WGU's equivalent of the credit hour) each 6-month term. (Each course is typically 3 or 4 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 features 3 specializations for you to choose from, with unique courses and capstone in each focus area. This allows you to gain specific skills and gain experience in your chosen area, preparing you to enhance your résumé and meet your career goals.
Foundational Courses Taken Across All Specializations
Data Analytics
The Data Analytics Journey
The Data Analytics Journey uses the analytics life cycle to conceptualize the processes, tools, and techniques for implementing data analysis, data engineering, and decision process engineering. Students gain fluency in gathering requirements, asking business questions, establishing evaluation metrics, identifying communication models, and aligning the analytics project outcomes to business goals. This course presents an overview of the various tracks offered in the program and the career options in these specializations. There is no prerequisite for this course.
Data Management
Data Management builds proficiency in using both relational and non-relational databases. Topics include selection of a data storage architecture, data types, data structures, normalization and denormalization, and querying databases. Structured Query Language (SQL) topics such as Data Definition Language (DDL) and Data Manipulation Language (DML) are covered, including joins, aggregations, and transactions. Non-relational approaches to organizing and querying data are contrasted with relational approaches to build competency in adapting data storage architectures to business needs.
Analytics Programming
Analytics Programming builds algorithmic thinking using both the Python and R programming languages. This course builds from the foundations of programming. Learners use libraries and packages to perform common analytics tasks, including acquiring, organizing, and manipulating datasets. The course also presents methods for applying statistical functions and graphical user interfaces to perform basic analysis and to present findings. There is no pre-requisite for this course.
Data Preparation and Exploration
Data Preparation and Exploration applies analytical programming skills to the early steps of the data analytics life cycle. This course covers cleaning data to ensure the structure, accuracy, and quality of the data; interpretation of descriptive and inferential statistics as well as visualizations of data; and wrangling data to prepare it for further analysis. The course introduces hypothesis testing, focusing on application for parametric tests, and addresses communication skills and tools to explain an analyst’s findings to others within an organization. The following courses are prerequisites: The Data Analytics Journey, Data Management, and Analytics Programming.
Statistical Data Mining
Statistical Data Mining focuses on concepts in data preparation and supervised and unsupervised machine learning techniques. The course helps students gain basic knowledge in statistics, data preparation, regression, and dimensional reduction. Learners implement supervised models—specifically classification and prediction data mining models—to identify relationships among variables that are not apparent with more surface-level techniques. The course also explains when, how, and why to use unsupervised models to best meet organizational needs. The following course is a prerequisite: Data Preparation and Exploration.
Data Storytelling for Varied Audiences
Data Storytelling for Varied Audiences focuses on communicating observations and patterns to diverse stakeholders, a key aspect of the data analytics life cycle. This course helps learners gain communication and storytelling skills in order to motivate change and answer business problems. It also covers data visualizations, audio representations, interactive dashboards, interpersonal communication, and presentation skills. There is no pre-requisite for this course.
Deployment
Deployment is the practice of operationalizing data analysis within a business environment. Given an analysis, learners determine the business functional and non-functional requirements for wider use and implement pipelines and functions to deploy analyses at scale. This course discusses topics such as security, scalability, usability, and availability. The following courses are prerequisites: Analytics Programming, Data Management, Data Preparation, and Statistical Data Mining.
Courses in the Data Science Specialization
Data Science
Advanced Analytics
Advanced Analytics extends analytics techniques from machine learning to artificial intelligence more broadly, including topics in neural networks, deep learning, and natural language processing. The course discusses approaches to developing these models including PyTorch and TensorFlow. Students learn to apply a combination of techniques to solve complex business challenges including computer vision and sentiment analysis. There is no prerequisite for this course.
Optimization
Optimization is a large class of business problems requiring the iterative algorithmic maximization or minimization or one or more variables. Students in this course will select and use a variety of optimization approaches to address various business needs. The course covers classes of optimization problems at a foundational level (continuous/discrete, linear/nonlinear, and bounded/unbounded) and the solving of linear optimization problems in both Python and R through the use of gradient and non-gradient-based algorithms. Analytics Programming is a prerequisite.
Machine Learning
Machine Learning comprises the broad discipline of developing algorithms and statistical models to predict, classify, or cluster data and iteratively improve over time. Machine Learning focuses on building, training, running, and testing supervised and unsupervised models and quantifying the accuracy and precision of those models to determine which may best be used in a particular business situation. Supervised methods discussed include k-nearest neighbors, decision trees, and support vector machines. Unsupervised models discussed include k-means clustering, hierarchical clustering, and t-distributed stochastic neighbor embedding (t-SNE). Ensemble methods are also presented. The following courses are prerequisites: Analytics Programming and Statistical Data Mining.
Data Science Capstone
Data Science Capstone integrates the key concepts from the MSDA core with the knowledge gained in the three courses within the Data Science specialization. In this course, students will evaluate various needs and opportunities in an organization or marketplace. In addition, students will identify business requirements and translate those business requirements into technical requirements. Finally, students will create a comprehensive project plan to solve a problem in a way that satisfies customer or business needs. Projects within this specialization may include the design and construction of machine learning approaches, optimization, and the use of advanced analytics techniques as the project requires. There is no prerequisite for this course.
Courses in the Data Engineering Specialization
Data Engineering
Cloud Databases
Cloud Databases covers the application of cloud architectures to large-scale data systems. The course discusses the differences between cloud-native approaches to data architectures and smaller-scale systems. Students in this course apply cloud computing concepts to address specific business scenarios. There is no prerequisite for this course.
Data Engineering Capstone
The Data Engineering Capstone has learners utilize the skills learned throughout the MDSA core courses and the data engineering courses to examine a problem where data engineering is a solution and to build a cloud-native infrastructure that allows for data processing. Learners are asked to implement their solutions and tell a story using the data. Course material introduces the project and reminds learners of relevant learning resources from previous courses that will prove helpful in completing the performance assessment.
Data Analytics at Scale
Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, and cloud–native solutions for developing, automating, and scaling data analytics. Also discussed are methods for integrating data processing pipelines and data stores to create comprehensive data analytics architectures.
Data Processing
Data Processing includes the practice of automating data flow into and out of components of an analytics system. Data processing comprises a major part of the analytics life cycle in modern organizations. This course covers concepts in extract, transform, and load (ETL) pipeline operations on data at scale and variations of ETL as a function of data repositories, including data warehouses and data lakes. Streaming and batch data operations and their differences are discussed, and students implement pipeline solutions in cloud-native environments. There is no prerequisite for this course.
Courses in the Decision Process Engineering Specialization
Decision Process Engineering
Business Process Engineering
Processes form the core of any organization and involve both manual and automated steps. Business Process Engineering introduces how to identify processes, visualize them, and how to design and implement operational methods that promote organizations’ overall efficiency. The course covers common process engineering frameworks, the stages of process engineering present in common frameworks, and introduces tools used to conduct business process reengineering.
Decision Intelligence
Decision Intelligence is a domain that optimizes decision-making by balancing technology, processes, and people. In this course students learn the core principles of Decision Intelligence, exploring the augmentation of decision processes with machine learning, comprehensive decision modeling, and the pivotal role of a “human-in-the-loop” design. Students will navigate decision theories and multi-criteria decision analysis, gaining insight into how biases and heuristics influence decision outcomes. The course emphasizes framing decisions using causal decision diagrams (CDD), implementing decision intelligence, evaluating the outcome using key performance indicators and determining the return on investment of the change, and using change management techniques to help the organization adapt to new decision making strategies.
Decision Process Engineering Capstone
The Decision Process Engineering capstone integrates the learning in the MSDA core and the three courses within the specialization. The learner evaluates various needs and opportunities in an organization or marketplace; identifies the business requirements; translates the business requirements into technical requirements; and creates a comprehensive project plan to solve the problem in a way that satisfies the customer or business needs. Projects within this specialization include a project management plan, decision intelligence plan, or process engineering plan to deliver on the business need or opportunity.
IT Management
Project Management
Project Management is a thorough exploration of the inputs, tools, techniques, and outputs across the Project Management Body of Knowledge (PMBOK). The essential concepts and practical scenarios included enable students to build the competencies of an effective project manager. There is no prerequisite for this course.
11 Courses
Program consists of 11 courses
At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you are competent in your area of study. In this program you will have unique course options depending on your specialization choice.
View Data Science Program Guide
View Data Engineering Program Guide
View Decision Process Engineering Program Guide
Capstone Project
Special requirements for this program
At the end of your unique program, you will complete a capstone project that represents the culmination of all your hard work—this project consists of a technical work proposal, the proposal’s implementation, and a post-implementation report that describes the graduate’s experience.
Request Info
Skills For Your Résumé
As part of this program, you will develop a range of valuable skills that employers are looking for.
Communication:
Utilized storytelling techniques to effectively influence, inform, or motivate target audiences.
Python (Programming Language) :
Developed modular scripts using Python.
SQL (Programming Language):
Manipulated data effectively using structured query language (SQL) statements, facilitating data retrieval, manipulation, and analysis.
Data Analysis:
Proposed innovative data analytics approaches for resolving complex problems, leveraging data-driven insights to inform decision-making and achieve desired outcomes.
Critical Thinking:
Applied logical reasoning skills to address real-world problem-based inquiries.
Data Engineering:
Engineered efficient and scalable data pipeline solutions.
“I just completed my master's program at WGU, and overall I had a very positive experience. The online degree program was extremely flexible, and that was critical for my success in the program because of other work and family demands. The program mentors were just as flexible and provided helpful guidance along the way, and I feel like I learned a lot during the program. Also, the cost for the program was very reasonable, and they even worked with my employer to provide a tuition discount and deferral program. I would definitely recommend WGU!!”
—Andrew
M.S. Data Analytics
WGU vs. Traditional Universities
Compare the Difference
Traditional Universities
AVG. cost
For 3RD PARTY IT CERTIFICATIONS
$350*
Included with your tuition cost
TUITION STRUCTURE
Per credit hour
Flat rate per 6-month term
SUPPORT
Schedule and wait days or even weeks to meet with one of many counselors
Simply email or call to connect with your designated Program Mentor who supports you from day one
EXAMS
Scheduled time
Whenever you feel ready
SCHEDULE
Professor led lectures at a certain time and place
Courses available anytime, from anywhere
TIME TO FINISH
Approximately 2 years, minimal acceleration options
As quickly as you can master the material, typically less than 2 years
*The cost of valuable industry certification exams can range from $150 to $400. At WGU, we offer vouchers for certification exams, so the cost is included in your tuition price. Students may have to pay some fees for membership to complete their certification requirements.
Apply Now
Traditional Universities
AVG. cost
For 3RD PARTY IT CERTIFICATIONS
$350*
AVG. cost
For 3RD PARTY IT CERTIFICATIONS
Included with your tuition cost
TUITION STRUCTURE
Per credit hour
TUITION STRUCTURE
Flat rate per 6-month term
SUPPORT
Schedule and wait days or even weeks to meet with one of many counselors
SUPPORT
Simply email or call to connect with your designated Program Mentor who supports you from day one
EXAMS
Scheduled time
EXAMS
Whenever you feel ready
SCHEDULE
Professor led lectures at a certain time and place
SCHEDULE
Courses available anytime, from anywhere
TIME TO FINISH
Approximately 2 years, minimal acceleration options
TIME TO FINISH
As quickly as you can master the material, typically less than 2 years
Apply Now
*The cost of valuable industry certification exams can range from $150 to $400. At WGU, we offer vouchers for certification exams, so the cost is included in your tuition price. Students may have to pay some fees for membership to complete their certification requirements.
Why WGU?
Earning Potential
According to a 2024
Harris Poll
, just two years after graduation, WGU grads report earning $22,200 more per year, and that amount increases to $30,300 four years after graduation.
PAYING FOR SCHOOL
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.
A FLEXIBLE SCHEDULE
Entirely Online
The data analytics master'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.
ADMISSIONS INFO
Accredited, Respected,
Recognized™
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 is institutionally recognized.
“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”
-Joe Dery, Dean of Data Analytics, Computer Science, and Software Engineering | WGU
CERTIFICATIONS
Data Analytics Certificates Included in this Degree
Completing this degree program includes several WGU certificates, including one in each of our specialization areas. Earning certificates 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.
SEE MORE ABOUT CERTIFICATIONS
Data Analytics Professional Certificate
Data Operations Certificate
Data Science Professional Specialization (in the Data Science concentration)
Decision Process Engineering Professional Specialization (in the Decision Process Engineering concentration)
Data Engineering Professional Specialization (in the Data Engineering concentration)
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.
Apply FREE
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A College Degree Within Reach
There is help available to make paying for school possible for you:
The average student loan debt of WGU graduates in 2022 (among those who borrowed) was less than half* the national average.
Responsible Borrowing Initiative
Most WGU students qualify for financial aid, and WGU is approved for federal financial aid and U.S. veterans benefits.
Financial Aid
Many scholarship opportunities are available. Find out what you might be eligible for.
Scholarships
*
WGU undergraduate students have approximately half the debt at graduation compared to the national average, according to the Institute for College Access and Success (2022).
FLEXIBLE SCHEDULE
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.
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"I have dreamed about completing my master's degree for many years now and it became a reality only because of WGU. I am so thankful to WGU administration. Many thanks to my mentors and course instructors along the way. Thank you so much WGU!!!”
—Naga Satya Srivani
M.S. Data Analytics
CAREER OUTLOOK
Data Leadership Careers Start with a Master's Degree in Data Analytics
When properly analyzed, every transaction—commercial, medical, social, or academic—can help lead to better business decisions and outcomes in your industry. WGU is key in helping you
gain the critical skills and experience
you need to thrive in your professional sector. Increase your earning potential,
boost your résumé
with valuable credentials, and find a career you love with the help of a data analytics master's degree.
From healthcare to entertainment, every industry is going digital. Organizations need experts who can maximize that data. When you’ve completed your data analytics degree program online,
your skills will already be in high demand
. The knowledge and techniques you’ll gain as you complete your degree will provide you with all the tools necessary for a successful career.
MORE ABOUT CAREERS
Return on Your Investment
Many WGU graduates may see an increase in income post-graduation
Average income increase in annual salary vs. pre-enrollment salary from all degrees. Source:
2024 Harris Poll
of 1,655 WGU graduates.
Survey was sent to a representative sample of WGU graduates from all colleges. Respondents received at least one WGU degree since 2017.
36%
The number of positions for data scientists is projected to
grow by 36% through 2033
, much faster than the average national rate for all occupations.
—U.S. Bureau of Labor Statistics
Learn About All the Job Opportunities in Data Analytics
Students who earn a master’s degree in data analytics will be prepared to maximize leadership opportunities in a variety of careers. Choosing a specialization will allow students to focus on their specific career goals, gaining skills and experience that will prepare them to meet industry needs. Learn more about specific career opportunities within each specialization.
Data Science
Data Scientist
Data Analyst
Machine Learning Data Scientist
Machine Learning Engineer
Optimization Analyst
Data Engineering
Data Analytics Architect
Analytics Engineer
Data Quality Analyst
Data Engineer
Data Analyst
Decision Process Engineering
Data Analyst
Business Analyst
Data Analytics Consultant
Decision Analyst
Program Management Analyst
View Data Science Specialization
View Data Engineering Specialization
View Decision Process Engineering Specialization
WGU Grads Hold Positions With Top Employers
Explore More
ADMISSIONS
Data Analytics Admission Requirements
What We Look For in Applicants
We evaluate each application holistically, considering a range of factors that reflect academic readiness, alignment with our mission, and potential for success in the program. While no single element guarantees admission, strong applications typically demonstrate a combination of the following:
1. Possess a bachelor’s degree in a STEM field (preferably in the areas of Technology and Mathematics).
OR
2. Possess any bachelor’s degree plus ONE of the following:
Completed college-level coursework in statistics and computer programming with a grade of B- or better. Students may complete the Foundations of Coding and Data Decisions: Applied Statistics courses at
WGU Academy
for a $25 course fee prior to enrolling in the program.
At least two years of work experience in a data analytics, data science, data engineering, or database administration role
A current and active third-party certification in data analytics, data science, or data engineering from this list:
CompTIA Data+
DASCA Associate Big Data Engineer
DASCA Senior Big Data Engineer
Udacity Data Analyst Nanodegree
Udacity Data Scientist Nanodegree
Udacity Data Engineering with AWS Nanodegree
Associate Certified Analytics Professional (aCAP)
Certified Analytics Professional (CAP)
Cloudera Data Platform (CDP) Data Analyst
Microsoft Certified: Azure Data Scientist Associate
SAS Certified Advanced Analytics Professional
WGU Academy Data Analytics Skills Certificate
NOTE: You do not need to take the GRE or GMAT to be admitted to this program.
Learn why we don't require these tests.
GENERAL ADMISSION REQUIREMENTS
Get Your Enrollment Checklist
Download your step-by-step guide to enrollment.
VIEW CHECKLIST
Get Your Questions Answered
Talk to an WGU Enrollment Counselor.
CONTACT AN ENROLLMENT COUNSELOR
Transfer Credits
TRANSFER INFORMATION
FAQs about Master's in Data Analytics Programs
General IT Program Questions
Master in 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.
Absolutely. Data makes our world go round, and every business in every industry needs data to help make decisions. You can work in any field with the help of a degree in data analytics. A master's in data analytics will prepare you to convert raw data into meaningful information that can help business leaders make decisions. Become a great influence with the help of an master's degree in data analytics.
An MS in data analytics is a Master of Science in Data Analytics. This degree helps students learn how to do the many tasks associated with taking raw data and turning it into meaningful information. This includes data mining, programming, analyzation, and more.
A master's degree in data science or data analytics is a great option for IT professionals who are looking to take their career to the next level. Experience in the IT field will be extremely helpful as you pursue a master's degree in data analytics. If you're looking to start a career in IT, a bachelor's degree or certifications can help you begin.
Common careers for those with a master's degree in data analytics include:
Data analyst
Business analyst
Data engineer
Business intelligence analyst
Information research scientist
Advanced analytics expert
A master's degree in data science or data analytics can be challenging, but if you have a solid background and understanding in IT, you'll be able to excel. Experience, education, and certifications in IT can help you be better prepared for this master's degree.
Data Analytics Advisory Board Members
Boaz Hillebrand,
Senior Data Manager, Talent Acquisition – 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,
former Senior Director of Global Analytics – eBay
Mohican Laine
, Customer Engineer, Google
Nolan Hill,
SVP of HR Analytics & Data Governance – Bank of America
Stephen Gatchell,
Director of Data Advisory – BigID
Martijn Theuwissen,
CCO and Co-Founder of DataCamp
Ready to Start Your WGU Journey?
Apply Now
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Master of Science
Online Master's in Data Analytics
Apply Now
OVERVIEW
Lead the Analytics Lifecycle with a Master's Degree in Data Analytics
Make your next career move and maximize your earning potential with WGU’s Master of Science, Data Analytics (MSDA) program. This online degree program provides a depth of skills for your data analytics career in the following areas:
Data Analytics Lifecycle
Data Science and Advanced Analytics Applications
Data Mining Techniques
Machine Learning
Data Management
Business Influence
We use cutting-edge technology to help you learn about machine learning, modern analytic tools & languages
(Python, R, SQL, and Tableau)
, and more! Additionally, this unique program allows you to choose from a range of datasets representing different industry-specific themes. Learn exactly what you want and need for your career!
Choose Your Specialization
The WGU data analytics program features 3 specialization options
for students to choose from. The concentration areas allow students to dive deeper into their area of interest, better preparing them for their career goals.
Data Science:
This specialization focuses on machine learning, optimization of data, and advanced analytics understanding.
Data Engineering:
This specialization focuses on cloud engineering, the processing of data, and analytics at scale.
Decision Process Engineering:
This concentration focuses on project management, process optimization, and decision intelligence related to data analytics.
At WGU, we use a ‘three-lever’ approach to data analytics, surgically incorporating programming, math, and business influence skills throughout the program. Different roles in analytics will have different combinations of these levers. You will navigate to the role that best fits your interests & passions by selecting the concentration of your choice. Our game-changing approach to data analytics means that you will be equipped with the experience you need to create change and make an impact in any industry.
Have questions?
Attend our prospective student webinar to learn more about our programs.
Listen to 'Which MSDA Concentration is Right for Me?' Podcast
Unsure Which Specialization to Choose?
The data science specialization
is ideal for those who want to focus on statistical and programming approaches in machine learning, neural networks, and numerical optimization.
Learn more about this specialization through our Data Analytics podcast.
The data engineering specialization
is ideal for those who want to support analytics through cloud-native databases, data processing, and analysis approaches.
Learn more about this specialization through our Data Analyics podcast.
The decision process engineering specialization
is ideal for those who want to implement business changes through project management, business process engineering, and decision intelligence.
Learn more about this specialization through our Data Analytics podcast.
View Data Science Specialization
View Data Engineering Specialization
View Decision Process Engineering Specialization
This data analytics degree program focuses on both
theory
and
application
, allowing you to “learn by doing” as you complete data analytics projects in stages, known as the
data analytics lifecycle
.
The Master of Science in Data Analytics program's core courses dive deeply into each of these lifecycle stages
(sourcing data, cleaning data, data mining, descriptive & predictive analytics, and visualization).
70% of graduates finish within
23 Months*
WGU lets you move more quickly through material you already know and advance as soon as you're ready. The result: You may finish faster.
*WGU Internal Data
Flexible Schedule
Tuition per six-month term is
$4,565
Tuition charged per term—rather than per credit—helps students control the ultimate cost of their degree. Finish faster, pay less!
Cost & Time
Certifications that may transfer
1
Your Oracle SQL Expert certification may waive course requirements.
Certifications
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COURSES
Data Analytics Courses
Program consists of 11 courses
At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you know your stuff.
View Data Science Program Guide
View Data Engineering Program Guide
View Decision Process Engineering Program Guide
The WGU M.S. Data Analytics degree program was designed, and is regularly updated, with input from the experts on our Data Analytics Advisory Board. This ensures that you learn best practices for the latest developments in data analytics.
This data analytics degree program is composed of the following courses. You will typically complete them one at a time as you make your way through your program, working with your Program Mentor each term to build your personalized Degree Plan. You’ll work through each course as quickly as you can study and learn the material. As soon as you’re ready, you’ll pass the assessment, complete the course, and move on. This means you can finish as many courses as you're able in a term at no additional cost.
The M.S. Data Analytics degree program is an all-online program that you will complete through independent study with the support of WGU faculty. You will be expected to complete at least 8 competency units (WGU's equivalent of the credit hour) each 6-month term. (Each course is typically 3 or 4 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 features 3 specializations for you to choose from, with unique courses and capstone in each focus area. This allows you to gain specific skills and gain experience in your chosen area, preparing you to enhance your résumé and meet your career goals.
Foundational Courses Taken Across All Specializations
Data Analytics
The Data Analytics Journey
The Data Analytics Journey uses the analytics life cycle to conceptualize the processes, tools, and techniques for implementing data analysis, data engineering, and decision process engineering. Students gain fluency in gathering requirements, asking business questions, establishing evaluation metrics, identifying communication models, and aligning the analytics project outcomes to business goals. This course presents an overview of the various tracks offered in the program and the career options in these specializations. There is no prerequisite for this course.
Data Management
Data Management builds proficiency in using both relational and non-relational databases. Topics include selection of a data storage architecture, data types, data structures, normalization and denormalization, and querying databases. Structured Query Language (SQL) topics such as Data Definition Language (DDL) and Data Manipulation Language (DML) are covered, including joins, aggregations, and transactions. Non-relational approaches to organizing and querying data are contrasted with relational approaches to build competency in adapting data storage architectures to business needs.
Analytics Programming
Analytics Programming builds algorithmic thinking using both the Python and R programming languages. This course builds from the foundations of programming. Learners use libraries and packages to perform common analytics tasks, including acquiring, organizing, and manipulating datasets. The course also presents methods for applying statistical functions and graphical user interfaces to perform basic analysis and to present findings. There is no pre-requisite for this course.
Data Preparation and Exploration
Data Preparation and Exploration applies analytical programming skills to the early steps of the data analytics life cycle. This course covers cleaning data to ensure the structure, accuracy, and quality of the data; interpretation of descriptive and inferential statistics as well as visualizations of data; and wrangling data to prepare it for further analysis. The course introduces hypothesis testing, focusing on application for parametric tests, and addresses communication skills and tools to explain an analyst’s findings to others within an organization. The following courses are prerequisites: The Data Analytics Journey, Data Management, and Analytics Programming.
Statistical Data Mining
Statistical Data Mining focuses on concepts in data preparation and supervised and unsupervised machine learning techniques. The course helps students gain basic knowledge in statistics, data preparation, regression, and dimensional reduction. Learners implement supervised models—specifically classification and prediction data mining models—to identify relationships among variables that are not apparent with more surface-level techniques. The course also explains when, how, and why to use unsupervised models to best meet organizational needs. The following course is a prerequisite: Data Preparation and Exploration.
Data Storytelling for Varied Audiences
Data Storytelling for Varied Audiences focuses on communicating observations and patterns to diverse stakeholders, a key aspect of the data analytics life cycle. This course helps learners gain communication and storytelling skills in order to motivate change and answer business problems. It also covers data visualizations, audio representations, interactive dashboards, interpersonal communication, and presentation skills. There is no pre-requisite for this course.
Deployment
Deployment is the practice of operationalizing data analysis within a business environment. Given an analysis, learners determine the business functional and non-functional requirements for wider use and implement pipelines and functions to deploy analyses at scale. This course discusses topics such as security, scalability, usability, and availability. The following courses are prerequisites: Analytics Programming, Data Management, Data Preparation, and Statistical Data Mining.
Courses in the Data Science Specialization
Data Science
Advanced Analytics
Advanced Analytics extends analytics techniques from machine learning to artificial intelligence more broadly, including topics in neural networks, deep learning, and natural language processing. The course discusses approaches to developing these models including PyTorch and TensorFlow. Students learn to apply a combination of techniques to solve complex business challenges including computer vision and sentiment analysis. There is no prerequisite for this course.
Optimization
Optimization is a large class of business problems requiring the iterative algorithmic maximization or minimization or one or more variables. Students in this course will select and use a variety of optimization approaches to address various business needs. The course covers classes of optimization problems at a foundational level (continuous/discrete, linear/nonlinear, and bounded/unbounded) and the solving of linear optimization problems in both Python and R through the use of gradient and non-gradient-based algorithms. Analytics Programming is a prerequisite.
Machine Learning
Machine Learning comprises the broad discipline of developing algorithms and statistical models to predict, classify, or cluster data and iteratively improve over time. Machine Learning focuses on building, training, running, and testing supervised and unsupervised models and quantifying the accuracy and precision of those models to determine which may best be used in a particular business situation. Supervised methods discussed include k-nearest neighbors, decision trees, and support vector machines. Unsupervised models discussed include k-means clustering, hierarchical clustering, and t-distributed stochastic neighbor embedding (t-SNE). Ensemble methods are also presented. The following courses are prerequisites: Analytics Programming and Statistical Data Mining.
Data Science Capstone
Data Science Capstone integrates the key concepts from the MSDA core with the knowledge gained in the three courses within the Data Science specialization. In this course, students will evaluate various needs and opportunities in an organization or marketplace. In addition, students will identify business requirements and translate those business requirements into technical requirements. Finally, students will create a comprehensive project plan to solve a problem in a way that satisfies customer or business needs. Projects within this specialization may include the design and construction of machine learning approaches, optimization, and the use of advanced analytics techniques as the project requires. There is no prerequisite for this course.
Courses in the Data Engineering Specialization
Data Engineering
Cloud Databases
Cloud Databases covers the application of cloud architectures to large-scale data systems. The course discusses the differences between cloud-native approaches to data architectures and smaller-scale systems. Students in this course apply cloud computing concepts to address specific business scenarios. There is no prerequisite for this course.
Data Engineering Capstone
The Data Engineering Capstone has learners utilize the skills learned throughout the MDSA core courses and the data engineering courses to examine a problem where data engineering is a solution and to build a cloud-native infrastructure that allows for data processing. Learners are asked to implement their solutions and tell a story using the data. Course material introduces the project and reminds learners of relevant learning resources from previous courses that will prove helpful in completing the performance assessment.
Data Analytics at Scale
Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, and cloud–native solutions for developing, automating, and scaling data analytics. Also discussed are methods for integrating data processing pipelines and data stores to create comprehensive data analytics architectures.
Data Processing
Data Processing includes the practice of automating data flow into and out of components of an analytics system. Data processing comprises a major part of the analytics life cycle in modern organizations. This course covers concepts in extract, transform, and load (ETL) pipeline operations on data at scale and variations of ETL as a function of data repositories, including data warehouses and data lakes. Streaming and batch data operations and their differences are discussed, and students implement pipeline solutions in cloud-native environments. There is no prerequisite for this course.
Courses in the Decision Process Engineering Specialization
Decision Process Engineering
Business Process Engineering
Processes form the core of any organization and involve both manual and automated steps. Business Process Engineering introduces how to identify processes, visualize them, and how to design and implement operational methods that promote organizations’ overall efficiency. The course covers common process engineering frameworks, the stages of process engineering present in common frameworks, and introduces tools used to conduct business process reengineering.
Decision Intelligence
Decision Intelligence is a domain that optimizes decision-making by balancing technology, processes, and people. In this course students learn the core principles of Decision Intelligence, exploring the augmentation of decision processes with machine learning, comprehensive decision modeling, and the pivotal role of a “human-in-the-loop” design. Students will navigate decision theories and multi-criteria decision analysis, gaining insight into how biases and heuristics influence decision outcomes. The course emphasizes framing decisions using causal decision diagrams (CDD), implementing decision intelligence, evaluating the outcome using key performance indicators and determining the return on investment of the change, and using change management techniques to help the organization adapt to new decision making strategies.
Decision Process Engineering Capstone
The Decision Process Engineering capstone integrates the learning in the MSDA core and the three courses within the specialization. The learner evaluates various needs and opportunities in an organization or marketplace; identifies the business requirements; translates the business requirements into technical requirements; and creates a comprehensive project plan to solve the problem in a way that satisfies the customer or business needs. Projects within this specialization include a project management plan, decision intelligence plan, or process engineering plan to deliver on the business need or opportunity.
IT Management
Project Management
Project Management is a thorough exploration of the inputs, tools, techniques, and outputs across the Project Management Body of Knowledge (PMBOK). The essential concepts and practical scenarios included enable students to build the competencies of an effective project manager. There is no prerequisite for this course.
11 Courses
Program consists of 11 courses
At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you are competent in your area of study. In this program you will have unique course options depending on your specialization choice.
View Data Science Program Guide
View Data Engineering Program Guide
View Decision Process Engineering Program Guide
Capstone Project
Special requirements for this program
At the end of your unique program, you will complete a capstone project that represents the culmination of all your hard work—this project consists of a technical work proposal, the proposal’s implementation, and a post-implementation report that describes the graduate’s experience.
Request Info
Skills For Your Résumé
As part of this program, you will develop a range of valuable skills that employers are looking for.
Communication:
Utilized storytelling techniques to effectively influence, inform, or motivate target audiences.
Python (Programming Language) :
Developed modular scripts using Python.
SQL (Programming Language):
Manipulated data effectively using structured query language (SQL) statements, facilitating data retrieval, manipulation, and analysis.
Data Analysis:
Proposed innovative data analytics approaches for resolving complex problems, leveraging data-driven insights to inform decision-making and achieve desired outcomes.
Critical Thinking:
Applied logical reasoning skills to address real-world problem-based inquiries.
Data Engineering:
Engineered efficient and scalable data pipeline solutions.
“I just completed my master's program at WGU, and overall I had a very positive experience. The online degree program was extremely flexible, and that was critical for my success in the program because of other work and family demands. The program mentors were just as flexible and provided helpful guidance along the way, and I feel like I learned a lot during the program. Also, the cost for the program was very reasonable, and they even worked with my employer to provide a tuition discount and deferral program. I would definitely recommend WGU!!”
—Andrew
M.S. Data Analytics
WGU vs. Traditional Universities
Compare the Difference
Traditional Universities
AVG. cost
For 3RD PARTY IT CERTIFICATIONS
$350*
Included with your tuition cost
TUITION STRUCTURE
Per credit hour
Flat rate per 6-month term
SUPPORT
Schedule and wait days or even weeks to meet with one of many counselors
Simply email or call to connect with your designated Program Mentor who supports you from day one
EXAMS
Scheduled time
Whenever you feel ready
SCHEDULE
Professor led lectures at a certain time and place
Courses available anytime, from anywhere
TIME TO FINISH
Approximately 2 years, minimal acceleration options
As quickly as you can master the material, typically less than 2 years
*The cost of valuable industry certification exams can range from $150 to $400. At WGU, we offer vouchers for certification exams, so the cost is included in your tuition price. Students may have to pay some fees for membership to complete their certification requirements.
Apply Now
Traditional Universities
AVG. cost
For 3RD PARTY IT CERTIFICATIONS
$350*
AVG. cost
For 3RD PARTY IT CERTIFICATIONS
Included with your tuition cost
TUITION STRUCTURE
Per credit hour
TUITION STRUCTURE
Flat rate per 6-month term
SUPPORT
Schedule and wait days or even weeks to meet with one of many counselors
SUPPORT
Simply email or call to connect with your designated Program Mentor who supports you from day one
EXAMS
Scheduled time
EXAMS
Whenever you feel ready
SCHEDULE
Professor led lectures at a certain time and place
SCHEDULE
Courses available anytime, from anywhere
TIME TO FINISH
Approximately 2 years, minimal acceleration options
TIME TO FINISH
As quickly as you can master the material, typically less than 2 years
Apply Now
*The cost of valuable industry certification exams can range from $150 to $400. At WGU, we offer vouchers for certification exams, so the cost is included in your tuition price. Students may have to pay some fees for membership to complete their certification requirements.
Why WGU?
Earning Potential
According to a 2024
Harris Poll
, just two years after graduation, WGU grads report earning $22,200 more per year, and that amount increases to $30,300 four years after graduation.
PAYING FOR SCHOOL
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.
A FLEXIBLE SCHEDULE
Entirely Online
The data analytics master'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.
ADMISSIONS INFO
Accredited, Respected,
Recognized™
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 is institutionally recognized.
“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”
-Joe Dery, Dean of Data Analytics, Computer Science, and Software Engineering | WGU
CERTIFICATIONS
Data Analytics Certificates Included in this Degree
Completing this degree program includes several WGU certificates, including one in each of our specialization areas. Earning certificates 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.
SEE MORE ABOUT CERTIFICATIONS
Data Analytics Professional Certificate
Data Operations Certificate
Data Science Professional Specialization (in the Data Science concentration)
Decision Process Engineering Professional Specialization (in the Decision Process Engineering concentration)
Data Engineering Professional Specialization (in the Data Engineering concentration)
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.
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A College Degree Within Reach
There is help available to make paying for school possible for you:
The average student loan debt of WGU graduates in 2022 (among those who borrowed) was less than half* the national average.
Responsible Borrowing Initiative
Most WGU students qualify for financial aid, and WGU is approved for federal financial aid and U.S. veterans benefits.
Financial Aid
Many scholarship opportunities are available. Find out what you might be eligible for.
Scholarships
*
WGU undergraduate students have approximately half the debt at graduation compared to the national average, according to the Institute for College Access and Success (2022).
FLEXIBLE SCHEDULE
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.
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"I have dreamed about completing my master's degree for many years now and it became a reality only because of WGU. I am so thankful to WGU administration. Many thanks to my mentors and course instructors along the way. Thank you so much WGU!!!”
—Naga Satya Srivani
M.S. Data Analytics
CAREER OUTLOOK
Data Leadership Careers Start with a Master's Degree in Data Analytics
When properly analyzed, every transaction—commercial, medical, social, or academic—can help lead to better business decisions and outcomes in your industry. WGU is key in helping you
gain the critical skills and experience
you need to thrive in your professional sector. Increase your earning potential,
boost your résumé
with valuable credentials, and find a career you love with the help of a data analytics master's degree.
From healthcare to entertainment, every industry is going digital. Organizations need experts who can maximize that data. When you’ve completed your data analytics degree program online,
your skills will already be in high demand
. The knowledge and techniques you’ll gain as you complete your degree will provide you with all the tools necessary for a successful career.
MORE ABOUT CAREERS
Return on Your Investment
Many WGU graduates may see an increase in income post-graduation
Average income increase in annual salary vs. pre-enrollment salary from all degrees. Source:
2024 Harris Poll
of 1,655 WGU graduates.
Survey was sent to a representative sample of WGU graduates from all colleges. Respondents received at least one WGU degree since 2017.
36%
The number of positions for data scientists is projected to
grow by 36% through 2033
, much faster than the average national rate for all occupations.
—U.S. Bureau of Labor Statistics
Learn About All the Job Opportunities in Data Analytics
Students who earn a master’s degree in data analytics will be prepared to maximize leadership opportunities in a variety of careers. Choosing a specialization will allow students to focus on their specific career goals, gaining skills and experience that will prepare them to meet industry needs. Learn more about specific career opportunities within each specialization.
Data Science
Data Scientist
Data Analyst
Machine Learning Data Scientist
Machine Learning Engineer
Optimization Analyst
Data Engineering
Data Analytics Architect
Analytics Engineer
Data Quality Analyst
Data Engineer
Data Analyst
Decision Process Engineering
Data Analyst
Business Analyst
Data Analytics Consultant
Decision Analyst
Program Management Analyst
View Data Science Specialization
View Data Engineering Specialization
View Decision Process Engineering Specialization
WGU Grads Hold Positions With Top Employers
Explore More
ADMISSIONS
Data Analytics Admission Requirements
What We Look For in Applicants
We evaluate each application holistically, considering a range of factors that reflect academic readiness, alignment with our mission, and potential for success in the program. While no single element guarantees admission, strong applications typically demonstrate a combination of the following:
1. Possess a bachelor’s degree in a STEM field (preferably in the areas of Technology and Mathematics).
OR
2. Possess any bachelor’s degree plus ONE of the following:
Completed college-level coursework in statistics and computer programming with a grade of B- or better. Students may complete the Foundations of Coding and Data Decisions: Applied Statistics courses at
WGU Academy
for a $25 course fee prior to enrolling in the program.
At least two years of work experience in a data analytics, data science, data engineering, or database administration role
A current and active third-party certification in data analytics, data science, or data engineering from this list:
CompTIA Data+
DASCA Associate Big Data Engineer
DASCA Senior Big Data Engineer
Udacity Data Analyst Nanodegree
Udacity Data Scientist Nanodegree
Udacity Data Engineering with AWS Nanodegree
Associate Certified Analytics Professional (aCAP)
Certified Analytics Professional (CAP)
Cloudera Data Platform (CDP) Data Analyst
Microsoft Certified: Azure Data Scientist Associate
SAS Certified Advanced Analytics Professional
WGU Academy Data Analytics Skills Certificate
NOTE: You do not need to take the GRE or GMAT to be admitted to this program.
Learn why we don't require these tests.
GENERAL ADMISSION REQUIREMENTS
Get Your Enrollment Checklist
Download your step-by-step guide to enrollment.
VIEW CHECKLIST
Get Your Questions Answered
Talk to an WGU Enrollment Counselor.
CONTACT AN ENROLLMENT COUNSELOR
Transfer Credits
TRANSFER INFORMATION
FAQs about Master's in Data Analytics Programs
General IT Program Questions
Master in 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.
Absolutely. Data makes our world go round, and every business in every industry needs data to help make decisions. You can work in any field with the help of a degree in data analytics. A master's in data analytics will prepare you to convert raw data into meaningful information that can help business leaders make decisions. Become a great influence with the help of an master's degree in data analytics.
An MS in data analytics is a Master of Science in Data Analytics. This degree helps students learn how to do the many tasks associated with taking raw data and turning it into meaningful information. This includes data mining, programming, analyzation, and more.
A master's degree in data science or data analytics is a great option for IT professionals who are looking to take their career to the next level. Experience in the IT field will be extremely helpful as you pursue a master's degree in data analytics. If you're looking to start a career in IT, a bachelor's degree or certifications can help you begin.
Common careers for those with a master's degree in data analytics include:
Data analyst
Business analyst
Data engineer
Business intelligence analyst
Information research scientist
Advanced analytics expert
A master's degree in data science or data analytics can be challenging, but if you have a solid background and understanding in IT, you'll be able to excel. Experience, education, and certifications in IT can help you be better prepared for this master's degree.
Data Analytics Advisory Board Members
Boaz Hillebrand,
Senior Data Manager, Talent Acquisition – 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,
former Senior Director of Global Analytics – eBay
Mohican Laine
, Customer Engineer, Google
Nolan Hill,
SVP of HR Analytics & Data Governance – Bank of America
Stephen Gatchell,
Director of Data Advisory – BigID
Martijn Theuwissen,
CCO and Co-Founder of DataCamp
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