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 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 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 Engineering and Technology and the College of Natural Sciences. Emphasis is placed on utilizing the tools for graduate success.
Course Description
This foundational course serves as an introduction to the core concepts and skills required for a successful journey into the world of data science. It covers the fundamental mathematical and programming principles needed to build a strong foundation for advanced data analysis.
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 is a comprehensive exploration of data modeling and analysis techniques across a diverse range of industry sectors. Students delve into seven distinct models of data and their respective analysis methods, equipping them with a versatile skillset applicable to various professional contexts. Through practical exercises and real-world case studies, learners gain a deep understanding of how to adapt and apply these models to solve complex problems in different industries. By the end of the course, students will be well-prepared to make informed decisions and recommendations based on data-driven insights. Prerequisite: DSC-510.
Course Description
This course introduces the concept of working with dynamic data, allowing students to handle and analyze data streams that change over time, not just static datasets. By the end of the course, students will be well-prepared to address dynamic data challenges and apply advanced analytical techniques in a professional setting. Prerequisite: DSC-515.
Course Description
This course is designed to equip students with a comprehensive understanding of data visualization techniques and best practices across three key stages of the data analysis process: data preprocessing and exploration, data analysis, and data presentation. Students learn to create visually engaging and informative data visualizations that add value to the information presented, ensuring they are accurate, non-misleading, and ethical. Additionally, they explore user interface (UI) principles for designing multidimensional visualizations using size, shape, and color, and how to address outliers effectively. Prerequisite: DSC-510.
Course Description
The Applied Data Visualizations course is designed to build upon the foundational concepts of data. This advanced course emphasizes practical applications of data visualization techniques and tools. Students engage in challenging assignments and create visuals from start to finish, telling a compelling story with data. Prerequisite: DSC-525.
Course Description
Students engage in the conceptualization, design, and presentation of an innovative idea, process, or product within the realm of data science. Through hands-on projects, learners integrate and apply knowledge acquired from preceding courses, culminating in the development of a comprehensive project. Prerequisite: DSC-515, DSC-525.
Course Description
This course on MLOps (Machine Learning Operations) is designed for students pursuing a master's in Data Science. MLOps make up a crucial discipline that bridges the gap between machine learning research and deployment in real-world scenarios. In this course, students learn the essential concepts, practices, and tools required to effectively manage and streamline the end-to-end machine learning life cycle, ensuring that data science projects are scalable, maintainable, and reproducible. Prerequisite: DSC-515.
Course Description
This course delves deeper into the practical aspects of machine learning operations (MLOps). MLOps are key to successfully deploying, monitoring, and maintaining machine learning models in real-world scenarios. In this course, students explore practical applications and hands-on techniques for implementing MLOps in various data science projects, preparing them for the challenges of operationalizing machine learning in the field. Prerequisite: DSC-565.
Course Description
This course is designed to equip students with advanced skills in time series analysis. Through a combination of theoretical foundations and hands-on practical applications, students delve into the intricacies of temporal data to derive meaningful insights and make informed decisions. Prerequisite: DSC-567.
Course Description
This course focuses on practical applications of time series analytics in real-world scenarios. Students engage in hands-on projects using both static historical data and live streamed data, providing them with a comprehensive skill set for addressing dynamic challenges in the field. Prerequisite: DSC-575.
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.
- GCU cannot and will not promise job placement, a job, graduate school placement, transfer of GCU program credits to another institution, promotion, salary, or salary increase. Please see the Career Services Policy in the University Policy Handbook.
- 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
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.