Turn Information Into Insights With an Online Master’s in Data Science
Do you want to help organizations use big data to understand the present—and improve the future? Prepare to pursue an engaging career as a data scientist with Walden’s MS in Data Science program.
Taught by subject matter experts, our curriculum equips you with hands-on experience using industry-leading tools in today’s latest cloud environments. You’ll study practical, sought-after skills to help you become even more effective in your job. Our online master’s in data science program also prepares you to use big data in an ethical way—so you can drive meaningful change for your employer and society at large.
An online data science degree prepares students to use digital data and tools to solve critical, real-world problems in business and industry.
Through case studies, visualizations, and hands-on practice, build skills you can apply right away to increase your professional impact.
Work with industry-standard tools in the most common cloud environments, such as IBM and Amazon Web Services (AWS).
Explore how to protect your organization and consumers through responsible data management practices.
Gain advanced data skills that industry trends show employers want and need in their IT workforce.
- 30 semester credits
- Core courses (30 sem. cr.)
The Global Technology Environment
Students in this course are provided a critical "state-of-the-art" breadth-first review of the domain of information technology (IT). Designed to provide students with a wide-ranging introduction to topics such as software engineering, cybersecurity, and big data analytics, students completing the course have a comprehensive global view of the current IT landscape in the context of both commercial and noncommercial enterprises. The class blends both theory and practice to provide a solid foundation for future study. Students study the relationship between technological change, society and the law, and the powerful role that computers and computer professionals play in a technological society.
|(3 sem. cr.)|
Data Warehousing and Business Intelligence
Students learn key approaches to the integration of enterprise-wide information to support business strategy and decision making. They cover issues in data acquisition, storage, retrieval, and analysis in this course. Topics include data warehouses; data marts; dashboards, key performance indicators, and scorecards; online analytical processing; and data visualization.
|(3 sem. cr.)|
Statistical Concepts for Big Data
Statistical analysis supports quality management, drives decision making, enables forecasting and prediction, and provides a means for understanding many aspects of our world. Data is everywhere in today's integrated technological society, and statistical analysis provides the means to access and interpret data. Students in this course are introduced to statistics focused on working with complex data sets and analyzing big data. Students synthesize theory with practical applications to learn the fundamentals of statistical reasoning, descriptive statistics, visual data display, regression, hypothesis testing, research design, anomaly detection, and advanced analysis practices. They have the opportunity to practice using a statistical software package to solve statistical problems. Students use a publicly available big data set to formulate their own study and complete a statistical analysis.
|(3 sem. cr.)|
Students in this course are provided with a comprehensive understanding of big data tools and techniques, related issues, and the different kinds of big data ecosystems that can be used to support advanced data analytics. Students consider big data management frameworks in general, but with a focus on the Hadoop open source distributed data storage and processing platform and its underpinning subsystems. Additionally, the course content introduces students to the role of big data systems in data-driven decision-making.
|(3 sem. cr.)|
In this course, students are provided with an in-depth understanding of the concepts of data mining, including the end-to-end processes involved and the major data mining tools and techniques in common usage. During the course, students have the opportunity to apply such tools and techniques to a variety of example data sets in order to gain a critical insight into their operation and an understanding of when and where such tools and techniques can best be applied. Students also have the opportunity, using the 'R' programming language, to implement several different kinds of data mining algorithms to gain a comprehensive understanding of their operation.
|(3 sem. cr.)|
Big data normally refers to petabytes (1000 terabytes) or exabytes (one billion gigabytes) of unstructured data. This amount of data requires new methods to analyze, visualize, and present these data in a way that yields insight and understanding. Students in this course are introduced to elementary graphics programming, focusing primarily on two-dimensional vector graphics and the programming platforms for graphics. This infrastructure will also include lessons on the human side of visualization, studying human perception and cognition to gain a better understanding of the target of the data visualization.
|(3 sem. cr.)|
Predictive Analytics for Decision Making
Students in this course are provided with insight into how predictive analytics can be used within organizations. In completing this course, students have the opportunity to gain a comprehensive understanding of how results from predictive analytics can be used by organizations to grow their customer base and run operations more efficiently. This course is oriented toward the practical applications of predictive analytics.
|(3 sem. cr.)|
Cloud computing has attained great commercial significance in recent years. As companies seek to drive down the capital (and recurrent) costs of doing business, using cloud computing to reduce expenditure by outsourcing aspects of the organizations' IT infrastructure to external, web-accessible systems has become a critical goal. In this course, students study the key concepts, theories, techniques, and practices that underpin cloud computing, including the main abstraction, component and deployment models that characterize cloud computing. Students have the opportunity to critically appreciate issues and problems, as well as cutting-edge solutions, pertaining to cloud computing.
|(3 sem. cr.)|
Advanced Database Systems
In order to create a competitive advantage, organizations store and analyze information in a variety of formats. This course covers key areas of database systems, such as requirements, design, implementation, security, performance, and scalability. Through a hands-on approach and practical projects, students have an opportunity to design and build database systems using the latest database technologies.
|(3 sem. cr.)|
Foundations of Intelligent Systems
Students in this course are introduced to the concepts of artificial intelligence and emergent areas of intelligent systems. Students have the opportunity to gain a critical understanding of knowledge representation, reasoning, machine learning, and evolutionary techniques. Students are presented with real-world problems and have the opportunity to apply "intelligent" techniques to provide solutions to these problems.
|(3 sem. cr.)|
|VIEW ALL COURSES Less Courses|
Tuition and Fees
|Tuition||30 semester credit hours||$880 per semester hour||$26,400|
|Technology Fee||Per semester||$210||$1,050|
*Tuition reflects the minimum time to completion. Time to completion varies by student, depending on individual progress and credits transferred, if applicable. Tuition and time to complete may be reduced if transfer credits are accepted, or if you receive grants, scholarships or other tuition reductions. Walden may accept up to 15 transfer credits. For a personalized estimate of the number of your transfer credits that Walden would accept, call an Enrollment Specialist at 855-646-5286.
Tuition and fees are subject to change. Books and materials are not included and may cost between $1,000 and $1,400.
Many Walden degree-seeking students—67%—receive some form of financial aid.* Create a customized plan that makes sense for you.
*Source: Walden University’s Office of Financial Aid. Data reports as of 2018.Find Ways to Save
To be considered for this master’s program, you must have a technical bachelor’s degree or at least one year of relevant professional experience in information technology and meet the general admission requirements. All graduate programs in the School of Technology and Applied Science require the submission of a résumé. Proficiency in at least one modern programming language is highly recommended but not required. All applicants must submit a completed online application and transcripts.
More information for international applicants.
Gain In-Demand Data Analysis Skills
Our online master’s in data science program integrates core business, compliance, and management principles with the rigorous, hands-on training you need to excel in your field. Explore machine learning as you discover how to collect, analyze, and visualize an ever-growing volume of data from information systems. Learn to share insights with stakeholders to empower better decision-making. Work toward becoming the voice of data responsibility and the go-to data expert in your organization and beyond.
Meet Your Academic Team
Gail MilesProgram Director
Dr. Miles has decades of teaching experience, including teaching computer science on campus for more than 25 years and teaching IT online since 2000 in a variety of programs. Her research interests include software engineering education and online education.
Osama MoradContributing Faculty
Dr. Morad has more than 27 years of experience in software development, databases, big data, and data analytics and teaches online graduate classes in these areas. He is an active program evaluator for the ABET (Accreditation Board for Engineering and Technology) accreditation organization.
Graduates of Walden’s master’s in data science online program will be prepared to:
- Evaluate emerging technical developments that apply to data science.
- Analyze current technologies that provide practical solutions to data science problems.
- Explore the role of supporting technologies for data science in data-driven decision-making.
- Analyze legal, ethical, professional, and social issue elements within the domain of data science.
- Differentiate how the techniques and tools of big data predictive analytics can be used to add “business value” in data-driven decision-making in the modern workplace.
Now more than ever, businesses need data scientists to help them interpret and leverage a growing volume of raw data. An online data science degree program can help provide you with the hands-on training and specialized technical knowledge necessary to fill this pressing need.
An online degree in data science could also potentially help lead to higher earnings. According to the Bureau of Labor Statistics (BLS), the median annual wage for computer and information research scientists was $126,830 in May 2020, with overall salaries ranging from approximately $72,210 to $194,430 depending on skills, years of experience, and other applicable factors.1
An MS in Data Science degree can prepare you to pursue career options such as:2
- Computer systems analysts
- Computer systems architects
- Quality assurance analysts
- Database administrators
An MS in Data Science degree can prepare you to work in settings such as:
- Government organizations
- Educational institutions
- Retail organizations
- Financial institutions
Career options may require additional experience, training, or other factors beyond the successful completion of this degree program.
FAQ About Walden’s Online MS in Data Science Program
Data science is the process of extracting insights from data. Using computer algorithms along with their own analytical skills, data scientists look for previously unrecognized connections between data points and use those connections to help improve efficiencies, target consumers, develop new products, and/or augment business strategies. It’s a field made possible by a connected, computerized world—and it’s a field that’s growing quickly.
Earning an MS in Data Science degree from Walden can help you gain valuable knowledge and skills in your field. Some of the most crucial skill sets include:
Communication: Individuals need to know how to communicate their key findings with stakeholders to ensure their work is effective. Refining their communication style helps data scientists formulate what business insights are most important and how they can use data to create a unique snapshot of their customers.
Collaboration: Data scientists bring significant value to organizations, but they must collaborate effectively with other departments to be successful. Involving stakeholders in a variety of departments gives data scientists a well-rounded view of the organization.
Predictive Analytics: Organizations use predictive analytics in many different ways, including data mining and artificial intelligence algorithms to update and optimize business decisions. A data scientist leads the charge with predictive analytics to make informed and educated decisions to benefit the organization.
Problem-Solving: A key skill in driving important business decisions is the ability to view a challenge from many perspectives. This approach allows data scientists to understand the issue they’re trying to resolve and use their skills and tools to address and drive good business decisions.
If you are interested in pursuing a degree in this expanding and important field, you should consider enrolling in an online master’s program. An online MS in Data Science program can give you the knowledge and skills you need to help organizations understand and harness big data. Plus, if you choose an online university like Walden, you can earn an MS in Data Science and learn from a curriculum that features real-world case studies and hands-on exercises—all from the comfort of your home or from wherever you have internet access.
Programming is crucial to the field of data science, so it is beneficial for students to have at least a foundational understanding before pursuing a master’s degree in the field. That’s because programming helps data scientists to process, analyze, and generate predictions from data. Python, R, and SQL are just a few of the programming languages used to tackle specific tasks. Sifting through and analyzing large datasets is a key part of the job, so the ability to understand one or more of these languages is essential.
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