Not all post-bachelor’s hopefuls are equipped to hop right into a data science master’s degree program. Thankfully, graduate programs often offer bridge courses to help fill gaps in knowledge that many master’s program applicants have, allowing them to enter the program at a skill level sufficient to keep up with graduate level coursework.
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.
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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.