Last Updated on July 31, 2023 by Oluwajuwon Alvina

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
To Succeed With Data Science, First Build the 'Bridge'

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.GET MORE INFORMATION!Let’s get started on your degreeWhat degree level are you currently seeking?Make a selectionBachelor’sMaster’sDoctoralBridgeHow would you like to attend?Make a selectionOnlineEveningWhat area of study interests you?Make a selectionBusiness & ManagementNursing & Health CareTeaching & School AdministrationPsychology & CounselingCriminal Justice, Government & Public AdministrationEngineering & TechnologyWhat program interests you?Make a selectionBridge (M.S. in Computer Science)Bridge (M.S. in Data Science)Bridge (M.S. in Information Technology Management)Bridge (Master Of Science in Nursing : Health Informatics)NextStep»TOTAL PROGRAM CREDITS & COURSE LENGTH:Total Credits: 20
Online: 7 weeks
[More Info]
TRANSFER CREDITS:Up to 90 credits, only 84 can be lower divisionPROGRAM TUITION RATE:

For students who are not yet ready for the master of science degree program in data science, UMass Dartmouth offers a preparatory bridge program consisting of five courses in data science fundamentals. Offered through UMassD’s Online & Continuing Education Programs, the preparatory curriculum is comprised of both online and in-person courses and may be completed within one year.

The program will prepare you to successfully study data science at the graduate level by introducing fundamental concepts and skills. However, the bridge courses listed below can be adjusted and are not a rigid requirement for entry into the data science program . For example, if you have studied linear algebra or a closely related area at the undergraduate level at another school you should not feel obligated to enroll in MTH 221. 

Prerequisites: A standard calculus I/II sequence

The program consists of the following courses:

  • 1 core course in programming: CIS 183 (online) or CIS115 or CIS190
  • 1 core course in data structures and algorithms: CIS 322 (online) or CIS 360.
  • 1 core course in linear algebra: MTH 221
  • 1 core course in probability: MTH 331
  • 1 core course in data science concepts of visualization and statistical analysis: DSC 101 or DSC 201. DSC 101 is focused on statistics with the R programming language while DSC 201 is focused on visualizations with Python. Either course will serve as a project-based introduction to data science.

However, even after meeting these requirements, students may need to study some undergraduate material on their own while taking graduate courses in data science.

The purpose of these preparatory courses is to prepare you to enter the MS program in data science. If a student has already taken a preparatory course through another university and is comfortable with the material, they will not need to repeat the course at UMassD. A grade of a “B” or higher is considered good in the application reviewing process, but all factors in a student’s application will be considered for admission to the program.

NYU Tandon Bridge | NYU Tandon School of Engineering

Frequently Asked Questions

When is the start date? What is the minimum and maximum duration of the program? 

There are no start and end dates, and no maximum or minimum duration. Rather, students are able to complete the classes at their own pace.

What is the cost of this program?

The cost is determined by the office of continuing education (OCE) and can be found here. When consulting the OCE webpage, please note that all of the Bridge program courses are 3-credit and undergraduate-level. 

How do I apply for the Bridge program?

There is no formal application. Interested students are suggested to email the data science department to discuss the bridge program and for course selection advice. To register for coursework you need to be at least a Non-Degree student of UMassD, which can be accomplished by a call to the Registrar’s Office. 

Bridge courses may cover coding, math, or both subjects simultaneously. Institutions offering master’s degrees in data science often require bridge courses as a solution to help students with borderline acceptance status beef up their qualifications before entering the program.

Birth of the Data Science Bridge Course

Statistics, the dry and dusty province of census takers and insurance company actuaries, suddenly didn’t seem a descriptive enough term for what analysts were being asked to do. They were no longer just crunching numbers; they were involved in designing the mechanisms for collecting the information and helping business executives and government leaders figure out how to interpret it– and data science was born.

At the University of Michigan, incoming professor C.F. Jeff Wu took a look at the field in 1997 and decided that it had broadened to encompass three overlapping, interdisciplinary pursuits:

  • Data Collection
  • Data Modeling and Analysis
  • Problem Solving and Decision Support

He dubbed the new field “data science” and called for the creation of masters and doctoral programs to offer degrees in it, with courses covering not just statistics, but the other disciplines required as well.

“‘Data Science’ is likely the remaining good name reserved for us,” he told his fellow statisticians of his decision to use that term. “‘Statistical science’ is not as attractive.”

But naming it didn’t magically make degree programs appear. And when masters degree programs for data science did emerge, the path to enter them wasn’t one that had been chosen by many undergraduates—computer science majors hadn’t been given the statistical background, and statistics majors hadn’t been provided the programming and logic background.

For those data science master’s degree candidates, bridge courses were the answer.

Understanding How Bridge Courses Fit in to Your Academic Plans

Bridge courses for data science masters degree programs are typically oriented around covering one of two possible deficiencies in a candidate’s educational background:

  • Lack of computer science skills
  • Lack of statistical and math skills

Candidates with prior math or engineering education may have a good grounding for the statistical concepts they will encounter in a data science masters program, but not enough programming experience to keep up with the modeling and analysis aspects. Conversely, those with a computer science background may have the coding chops to keep up with the modeling and scripting, but lack the math background to understand how to design the models.

Although they cover advanced topics, bridge courses tend to provide entry-level approaches suitable for students coming in with no real background in these subjects.

For many prospective masters program candidates, bridge courses aren’t optional: the university may require they be taken if the student’s background is not deemed sufficient to prepare them for the rigors of the data science program. Finding out whether or not this is the case will typically be a feature of the admissions process.

Bridge courses also aren’t usually available to candidates who have not been accepted to the program yet (although, if they are current undergraduate offerings at the institution, matriculated students may take them subject to normal entry requirements).

Many bridge courses are regular college classes, part of existing graduate or undergraduate programs at the institution where they are offered. Bridge courses typically take a full quarter or semester to complete and students are charged the regular fee for the credits. Taking a bridge course is like any other college class, including requisite homework, testing, and evaluation by instructors.

Some schools, however, deliver specialized bridge courses that are not otherwise part of the curriculum. These can be shorter and more specific, and may not be graded or offer credit conventionally. They might be scheduled as special summer programs to allow students to get them out of the way in time to enter the master’s program on a good footing with the rest of their cohort.

Bridge Courses vs MOOCs and Bootcamps

Candidates who are weak in basic computer science or statistics may decide to take Massive Open Online Courses (MOOCs) or enroll in a data science bootcamp to help brush up on those skills. Both these options allow a greater degree of flexibility in preparation:

  • A MOOC can be found on almost any subject, and taken at almost any time
  • There are a variety of bootcamps offered with less stringent entry requirements than master’s degree programs

However, the education gained in a MOOC or bootcamp is less certain preparation than that of a bridge course. Bridge courses are either designed, or have been expressly evaluated, by the master’s program as providing the skills and information required to succeed in more advanced classes. A MOOC or bootcamp offers no such assurance.

There is also some benefits to consolidating the course of study as a part of the master’s program: it will be administered by the same school, and all admissions, billing, accreditation, timing, and other potential points of friction will be smoothed over.

Bootcamps are more oriented toward practical information and real-world project scenarios, with many designed to include integral job placement programs to put participants to work right out of the bootcamp. They also offer little customization, so if you are weak on one particular aspect of data science, there is no guarantee that you will spend much time on it in your bootcamp.

On the other hand, people looking into data science master’s programs who have no special grounding in either computer science or statistics might find that a bootcamp offers a full-spectrum course of instruction in both subjects.

MOOCs are more like bridge courses in content and style, but few are accredited and may not fulfill eventual master’s program acceptance requirements.

What You Should Expect from Your School’s Bridge Course Options

At many institutions, bridge course credit will not count toward your master’s degree. However, you should make sure that your bridge course GPA will be factored into your cumulative GPA at graduation.

Since many bridge courses originate as classes outside the data science master’s program, the course of study will often diverge from that particular field of study. Bridge courses that are normally a part of other programs may naturally have a focus other than data science in mind. Consequently, students may feel like they are spinning their wheels learning subjects that aren’t ultimately related to their field of study.

Some universities, however, offer special, truncated bridge courses that may be free or offered at a reduced cost to master’s program candidates. This offers a more focused approach that cuts out any coursework not immediately related to the masters degree program.

In some cases, universities clearly define what classes they consider to be bridge courses. In other cases, admissions officials may look at a candidate’s background individually and assess weaknesses and assign bridge courses on that basis.

Getting the Most Out of Your Bridge Course

There can be a tendency to view bridge courses as hurdles or obstacles in the path toward getting a master’s degree, causing eager graduate students to hurry through them with the minimal required effort to pass. This attitude is a mistake since bridge courses often provide a valuable footing for progress through the master’s degree sequence.

In some ways, students required to complete a bridge course prior to entering the masters program are getting a leg up on fellow students– the information presented in the bridge course will be fresh in their minds and recently practiced, whereas students with a background deemed sufficient for immediate entry to the masters program may not have studied or used that particular knowledge since they graduated with their bachelor’s degree.


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