Data Science Master’s Degree
What Is a Master’s in Data Science?
A Master of Science in Data Science degree prepares professionals with a strong aptitude for mathematical reasoning and statistical computing for a career path in data analysis, essential to data-driven enterprises. In today’s information age and fast-growing digital world, data science is rising as a high-demand career path, seeking results oriented professionals who are skilled in data analysis, data mining, computer programming and problem solving in order to make effective data-driven business decisions.
Students in the data science master’s degree program will gain a solid understanding and practical experience with the application of predictive analytics theory, principles, technical tools and industry-specific knowledge to a wide variety of problems in science, engineering and business. This is a profession that requires data science professionals to be able to handle the entire data lifecycle:
- Identify challenges/ask questions
- Capture data from various sources
- Interpret and organize information
- Use intelligence to develop solutions
- Apply findings to answer questions
- Drive business strategy
These data science professionals are assets for all industries that are experiencing a shortage of candidates who have the technical skills to perform this job. The master’s in data science program is ideal for professionals who are interested in advancing their career, making discoveries with big data, working with new innovative technologies and pursuing research-oriented, forward-thinking leadership roles to impact business decisions.
About the Online Data Science Master's Program at GCU
GCU’s College of Science, Engineering and Technology offers this MS in Data Science program 100% online to meet the needs of adult students and working professionals. By learning through an innovative digital learning platform, students are able to study from anywhere, anytime, according to their schedule. The data science master’s values-based curriculum is based on GCU’s Christian worldview, which advocates for ethical decision making.
To be admitted into GCU’s MS in Data Science program, students must have graduated from an accredited college or university with a GPA of 2.8 or higher. In addition, students must have earned an undergraduate or graduate degree in computer science, statistics, mathematics, physics or engineering. Alternatively, an undergraduate or graduate degree in a non-technology field and GCU’s Bridge to Master of Science in Data Science is also acceptable.
What You Will Learn With a Master’s in Data Science
The focus areas for the online master’s in data science program include:
- Predictive analytics and modeling
- Computing
- Statistics
- Business intelligence
- Machine learning
- Software tools
Students will gain competencies in these domains:
- Data Science Analytics: Apply data analytics and statistical techniques to discover and deliver meaningful insights for problems, processes and decision making
- Data Science Engineering: Use engineering principles and modern computer technologies to develop new data analytics applications
- Data Management: Develop enterprise data management, protection and storage systems
- Research and Applications: Formulate research questions, design experiments, analyze results and communicate research findings
- Business Analytics: Gain knowledge in Business Analytics and Business Intelligence (BI) methods and develop business-oriented data products
- Professionalism, Ethics and Sustainability: Demonstrate ethical judgment for new data-driven business processes
The online Master of Science in Data Science degree culminates in a capstone project course that provides an opportunity for students to develop an evidence-based practice project proposal that addresses a current problem, issue or concern in data science. Students identify a problem amenable to research-based intervention, search literature, propose a solution and develop a plan to implement the solution, evaluate its outcome(s) and disseminate the findings.
What Can I Become With a Master’s in Data Science?
Graduates of the MS in Data Science program may take on roles within an organization such as:
- Analytics officer
- Business analytics director
- Predictive analyst
- Data scientist
- Fraud analytics manager
- Analytics strategy consultant
- Marketing analytics manager
- Risk analyst
- Customer analytics manager
Potential work settings in the field of data science include:
- Web-based retailers
- Social media companies
- Hospitals
- Primary care facilities
- Large manufacturing corporations
- Financial institutions
- Insurance companies
- Educational institutions
- Technology suppliers
- Consulting firms
- Think tanks
- Research facilities
Frequently Asked Questions About Data Science Master’s Degrees
Yes, earning a data science master’s degree is worth it. Beyond earning a degree in a growing field, you can step into a position where you can make a profound impact. The field of STEM is vitally important to the future of education and business. As data continues to evolve, highly skilled and effective workers are needed to advance technology and the future of our professional environment. The skills you learn in a MS in Data Science teaches you these important concepts so that you can enjoy a fulfilling lifelong career.
According to the U.S. Bureau of Labor Statistics, the median annual wage for computer and information research scientists was $126,830 in May 2020.1
If completed without interruption, GCU’s master’s in data science degree takes approximately 76 weeks to complete.
GCU also provides a Bridge Master of Science in Data Science degree that focuses on the core courses of the master’s degree and takes only 36 weeks to complete.
Data science degrees require a combination of hard skills (like learning Python and SQL) and soft skills (like business concepts and communication best practices). Students looking to pursue this degree will need to have strong foundational knowledge in these areas. While difficult, the skills earned in a data science online degree will be highly valuable as you continue on in your professional career.
While these programs both work with data, each requires a unique skill set. Data science involves multiple areas of predictive analysis, machine learning and algorithm development. Data analytics takes a broader view to interpret the data with an array of statistical tools. Those in data science may have a larger role in using programming language to process and verify the data, while a data analyst explores the data from a historical perspective.
1The earnings referenced were reported by the U.S. Bureau of Labor Statistics, Computer Information and Research Scientists as of May 2020. Due to COVID-19, data from 2020 may be atypical compared to prior years. The pandemic may also impact the predicted future workforce outcomes indicated by the BLS. BLS calculates the median using salaries of workers from across the country with varying levels of education and experience and does not reflect the earnings of GCU graduates as computer information and research scientist. It does not reflect workers’ earnings in one city or region of the country. It also does not reflect a typical entry-level salary. The median income is the statistical midpoint for the range of salaries in a specific occupation. It represents what you would earn if you were paid more money than half the workers in an occupation and less than half the workers in an occupation. It may give you a basis to estimate what you might earn at some point if you enter this career. You may also wish to compare median salaries if you are considering more than one career path.
Core Courses
Course Description
This course is designed to prepare students for the graduate learning experience at Grand Canyon University. Students have opportunities to develop and strengthen the skills necessary to succeed as graduate students in the College of Science, Engineering, and Technology. Emphasis is placed on utilizing the tools for graduate success.
Course Description
This course reviews probability, distributions, statistical methods, and data analysis, in the context of computational science. Students use statistical computing software to analyze, visualize, and communicate results.
Course Description
This course covers methods and applications of linear regression and multivariate analysis in predictive modeling. Students learn how to build and validate statistical models, using exploratory analysis, linear regression, principal components analysis, and cluster analysis. Prerequisite: DSC-510.
Course Description
This course focuses on foundational principles and on the process of developing mathematical tools and models that generates accurate predictions. Students design, build, and validate software applications that implement supervised learning algorithms. Prerequisite: DSC-520.
Course Description
This course covers the use, analysis, design, and implementation of machine learning algorithms. Students acquire in depth understanding of theoretical underpinning of both simple and advanced algorithms. Prerequisite: DSC-520.
Course Description
This course introduces deep artificial neural networks, reviewing the theoretical concepts and practical applications in data science. Students design and implement ANNs, while learning methods for training, testing, and deployment. A distinction is made between neural networks, convolutional neural networks, and recurrent neural networks. Prerequisite: DSC-520.
Course Description
This course prepares students for the research process in computational sciences, while developing an appreciation for the philosophy and ethics related to how research is conducted. Students learn how to design experiments, how to test the results using statistical methods, and communicate the findings. Prerequisite: DSC-510.
Course Description
This course combines mathematical and theoretical aspects of data analytics towards implementations in a computational form. Data mining algorithms and related methods for knowledge representation and reasoning form the basis for the development of decision and analytics software tools. Prerequisite: CST-560.
Course Description
This course presents the process for designing and creating software applications that use data to achieve an end goal. Several software development tools and languages are used to build products that use data to accomplish a business analytics or scientific exploration task. Prerequisite: DSC-570.
Course Description
Students conceptualize, design, and present an innovative idea, process, or a product in the field of data science. Projects synthesize and apply knowledge from previous courses and include a scientific report anchored in current theory and research. Prerequisite: DSC-580.
Pursue a next-generation education with an online degree from Grand Canyon University. Earn your degree with convenience and flexibility with online courses that let you study anytime, anywhere.
* Please note that this list may contain programs and courses not presently offered, as availability may vary depending on class size, enrollment and other contributing factors. If you are interested in a program or course listed herein please first contact your University Counselor for the most current information regarding availability.
* Please refer to the Academic Catalog for more information. Programs or courses subject to change.