MS in Data Science

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. Grand Canyon University’s Master of Science in Data Science prepares professionals with an a strong aptitude for mathematical reasoning and statistical computing, for a career path in data analysis, essential to data-driven enterprises.

Students 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 professionals are assets for all industries that are experiencing a shortage of candidates who have the technical skills to perform this job. This 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 percent 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, and according to their schedule. Students who prefer to learn as part of a traditional classroom may choose to enroll in GCU’s evening campus program. Also, the data science master’s values-based curriculum is based on GCU’s Christian worldview, which advocates for ethical decision making.

Degree Courses

The Master of Science in Data Science program’s focus areas include predictive analytics and modeling, computing, statistics, business intelligence, machine learning and 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 program 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.

Data Science Careers

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