Master’s in Data Science Degree
Study Advanced Analysis Concepts With a Master’s in Data Science
A Master of Science in Data Science degree from Grand Canyon University (GCU) can prepare those with a strong aptitude for mathematical reasoning and statistical computing for a career path in data analysis The data science career path may be ideal for 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 can 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 may require 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
The master’s degree in data science program is ideal for professionals who are interested in positioning themselves for possible career advancement, making discoveries with big data, working with new innovative technologies and pursuing research-oriented, forward-thinking leadership roles to impact business decisions.
Benefits of Pursuing Your MS in Data Science From GCU
Earning a STEM degree from GCU can prepare you for careers in your field. By earning your data science master’s from the College of Science, Engineering and Technology, you will study material instilled with a Christian worldview, promoting human flourishing and intentionality while serving others as you work to become a leader in your field. The faith-integrated curriculum at GCU advocates ethical decision making.
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MS in Data Science Topics and Coursework Covered
In the MS in Data Science online or evening program, you will cover various topics and gain competence in areas designed to boost your knowledge of the field and set yourself up for potential advancement and opportunities.
Career Paths for Master’s in Data Science Degree Holders
Graduates of the MS in Data Science program may take on roles within an organization such as:
- Natural sciences manager
- Actuary
- Statistician
- Data scientist
- Survey researcher
- Mathematical science teacher (postsecondary)
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
Master’s in Data Science FAQs
Read through some of the most frequently asked questions to learn more about pursuing a master’s-level data science degree and furthering your career in the field.
According to the U.S. Bureau of Labor Statistics, the median annual wage for computer and information research scientists was $136,620 in May 2022.1
A data science master’s degree may be worthwhile for many students. Beyond earning a degree in a growing field, you can step into a position where you have the potential to make an impact. As data continues to evolve, skilled and effective workers are needed to advance technology and the future of our professional environment. The skills you learn in an MS in Data Science program teaches you these concepts so that you can set yourself up to potentially pursue your ideal career path.
The master’s in data science degree requires a total of 38 credits for completion. Online classes for this degree are each approximately eight weeks in length.
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 degree can be 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.
According to survey responses from Payscale.com, data scientists received a job satisfaction rating of 3.98 out of 5 stars. Based on these responses, data scientists were rated as being highly satisfied with their jobs.2
The data scientist occupation is growing at a much faster than average rate compared to the average of all occupations, opening up the potential for job opportunities nationwide. The U.S. Bureau of Labor Statistics Occupational Outlook Handbook estimates job growth for data scientists to increase by about 32% from 2022 to 2032, much faster than average, accounting for an estimated increase of 59,400 jobs in the field.3
You do not need a PhD degree to become a data scientist. According to the U.S. Bureau of Labor Statistics, data scientists need a minimum of a bachelor’s degree to be able to enter that occupation. However, some employers may require a higher level of degree, depending on the specific position requirements.4
If you’re looking to advance in the field of data science, fill out the form on this page to learn more about earning your MS in data science from GCU.
1 The earnings referenced were reported by the U.S. Bureau of Labor Statistics (BLS), Computer Information and Research Scientists as of May 2022, retrieved on Sept. 15, 2023. Due to COVID-19, data from 2020 and 2021 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 scientists. It does not reflect earnings of workers in one city or region of the country. It also does not reflect a typical entry-level salary. 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. Grand Canyon University can make no guarantees on individual graduates’ salaries as the employer the graduate chooses to apply to, and accept employment from, determines salary not only based on education, but also individual characteristics and skills and fit to that organization (among other categories) against a pool of candidates.
2 Payscale. (n.d.). Average Data Scientist Salary. Retrieved July 6, 2023.
3 COVID-19 has adversely affected the global economy and data from 2020 and 2021 may be atypical compared to prior years. The pandemic may impact the predicted future workforce outcomes indicated by the U.S. Bureau of Labor Statistics as well. Accordingly, data shown is effective September 2023, which can be found here: U.S. Bureau of Labor Statistics, Occupational Outlook Handbook, Data Scientists, retrieved on Sept. 15, 2023.
4 U.S. Bureau of Labor Statistics (n.d.). Data Scientists. Occupational Outlook Handbook. Retrieved July 6, 2023.
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.
Grand Canyon University’s evening programs cater to the demands of working professionals who prefer an in-person learning environment. Our night classes meet just once per week and offer the interaction and discussion of a typical college classroom.
* 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.