Bridge Master of Science (MS) Data Science

Bridge (Master of Science in Data Science)

Offered By: College of Science, Engineering, & Technology

Grand Canyon University’s Bridge (Master of Science in Data Science) program provides an accelerated pathway for those interested in pursuing a rewarding career in data science. This bridge program serves as an introduction to data science and is available for anyone with a bachelor’s degree in any field who wishes to enter into GCU’s Master of Science in Data Science offered by the College of Science, Engineering and Technology. Students will gain the foundation necessary to begin their academic journey toward a future in data science and business analytics.

Data science is an ideal career path for those with a passion for data analysis, research and uncovering hidden information, as well as for those with a curiosity for how data can impact a wide range of fields. These fields include biology, medicine, cybersecurity, transportation, climate research, psychology, sports and more. Professionals who have strong analytical, strategizing and problem-solving skills, or a desire to develop and strengthen these skills, are set up to excel in the bridge and master’s programs.

What is a Data Science Bridge Program?

In this bridge program, students will take five courses and earn 20 credits to prepare to move into the corresponding graduate program. The program is offered 100 percent online to meet the needs of the adult learner, which allows working professionals to continue to work full-time while completing the program.

Students will be introduced to the fast-advancing digital landscape and how data science and analysis can drive organizations forward successfully as well as make a difference in the world, from discovering causes of disease and climate change to observing people migration patterns. The program begins with a highly rigorous class for primarily mathematics, science and engineering students with the goal to impart concepts and techniques of modern linear algebra. As students move through the program, they will acquire a foundation in probability and statistics, calculus and computer programming that go into detail below.

Learn Fundamentals for a MS in Data Science

Students will gain competencies in the following areas that will prepare them for the next step.

  • Applied Linear Algebra I: Learn linear algebra concepts and apply theory to examples, emphasizing the practical nature of solutions to linear algebra problems.
  • Probability and Statistics: Study the basics of probability, descriptive and inferential statistics, and decision making.
  • Calculus for Science and Engineering I: Understand the rigorous treatment of concepts and methods of elementary calculus and how it applies to real-world problems.
  • Computer Programming I: Learn fundamental concepts of the Java programming program focusing on object-oriented techniques, problem solving and fundamental algorithms.
  • Calculus for Science and Engineering II

Next Steps After a Data Science Bridge Program

Students are encouraged to move directly into the Master of Science in Data Science degree program to not lose momentum. As the next step, the master’s program shapes students into data science professionals who are equipped with the technical skills to fulfill roles in data science, data and business analytics and analytics management. These are roles for those who want to explore big data to make discoveries beneficial to businesses, use new technologies for research, specialize in the data lifecycle and become a forward thinker who leads impactful business decisions making.

According to Glassdoor rankings, data science is the number one best job in America as of 2018 with high job satisfaction and fast-growing job openings. Due to the shortage of data science professionals, graduates have expansive occupational opportunities and high earnings potential. See GCU’s Master of Science in Data Science page for an overview of the program and a list of potential career opportunities and workplace settings.

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Total Credits: 20
Online: 7 weeks
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Up to 90 credits, only 84 can be lower division
Online: $485 per credit [More Info]

Course List

20 credits
Total Degree Requirements:
20 credits

Core Courses

Course Description

This course provides a rigorous treatment of the concepts and methods of elementary calculus and its application to real-world problems. Topics include a brief review of linear, exponential, logarithmic, trigonometric, and inverse functions; understanding and calculating limits, continuity, and derivatives as rates of change; differentiation rules including derivatives of polynomials, exponentials, trigonometric, and logarithmic functions; product and quotient rules, the chain rule, and implicit differentiation; related rates, curve sketching, maximum and minimum problems, mean value theorem, linear approximation, indeterminate forms, and L’Hospital’s rule; and applied optimization problems, antiderivatives, and approximating areas under the curve. Prerequisite: Grade of C or better in MAT-250 or MAT-261.

Course Description

This course provides a rigorous treatment of the concepts, methods, and applications of integral calculus and is the second course in a three-course sequence. Topics include definite integrals, fundamental theorem of calculus, and integration rules; arc length, solids of revolution, and physical applications; techniques of integration including improper integrals and an introduction to differential equations; polar coordinates, parametric equations, infinite sequences, and series; power series and conic sections; and vector arithmetic, dot product, and projections. Prerequisite: Grade of C or better in MAT-252.

Course Description

This course is intended primarily for mathematics, science, and engineering students. The goal of the course is to impart the concepts and techniques of modern linear algebra (over the real scalar field) with a significant level of rigor. Students write clearly about the concepts of linear algebra (definitions, counterexamples, simple proofs), and apply theory to examples. The course emphasizes the practical nature of solutions to linear algebra problems. Students implement some of these solutions, where appropriate, as computer programs. Prerequisite: MAT-264 or MAT-253

Course Description

This course provides an introduction to the study of basic probability, descriptive and inferential statistics, and decision making. Emphasis is placed on measures of central tendency and dispersion, correlation, regression, discrete and continuous probability distributions, quality control population parameter estimation, and hypothesis testing. Prerequisite: Grade of C or better in MAT-134, MAT-144 or MAT-154.

Course Description

This course introduces the fundamental concepts and syntax of the Java programming language. The course focuses on object-oriented techniques in Java with an emphasis on problem solving and fundamental algorithms.

Program Locations

GCU Online Student

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.

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