Graduate Certificate in Data Science

Data is impacting many areas of science, engineering, and industry; from analyzing troves of weather data to modeling traffic patterns to processing millions of online customers, it is the enormous data which is creating new opportunities and challenges. To tackle these challenges, one must have the training to store, manage, process and analyze data at these scales. But the challenges are beyond scale alone, the complexity of the data requires new powerful analytical techniques. Finally, it is crucial to have skills in communicating and interpreting the results of this analysis. A person trained in all of these skills is a data scientist.

Graduate Certificate in Data Science Program

Requirements (PDF)

Program Information (PDF)


Admissions to the Graduate Certificate in Data Science here.

Frequently Asked Questions about the Graduate Certificate in Data Science Program

Applying and Enrolling in the Program

Q: I am not currently enrolled or affiliated with the university, how do I apply?

A: You should follow these application instructions. It involves applying as a non-matriculated student, and a short form with some background information, a CV, and a personal statement, and a question about your background in programming/computing.

Q: What if I am already affiliated with the university (and have permission to take classes)?

A: Then just go to the second step here: application instructions. It is a short form with some background information, a CV, and a personal statement, and a question about your background in programming/computing.

Q: After I apply how soon should I hear back?

A: We process the applications on a rolling basis. You may hear back in a few days, but it may be a few weeks.

Q: Can someone who completes the Graduate Certificate in Data Science convert to the MS program?

A: A student who does well in the certificate classes will be a very strong candidate for the MS program. But they will need to apply here. The most similar degree is the MS in Computing under the Data Management and Analysis track.

Q: Can credits taken as non-matriculated be counted towards the MS degree in Computing?

A: Up to 9 hours (3 classes) can transferred from non-matriculated to a graduate degree program.

Q: Does being enrolled in the Certificate program count towards a degree program for F1B status?

A: No. It is not a degree program. For this, one would need to be enrolled in the MS or PhD program.

Programming and Computing

Q: How much programming/computing background is expected or required?

A: The Graduate Certificate in Data Science consists of classes which are also taken and required for MS and PhD students in the School of Computing, so they all have programming and computing components. However, they are typically not among the heaviest programming classes (except the optional CS6530 Advanced Database Systems). That said, we will expect for admitted students that short abstract programming tasks will not be a burden to your learning experience, but rather a way to enhance it.

Many successful applicants/students aiming for the certificate are software engineers, while others work in a quantitative area where they do some minor programming. Having taken some computer science classes at a university a while back is fine (it should come back to you). But we expect you can do more than just scripting; use of data structures is essential to manipulating big data.

Q: Why do you require so much programming?

A: While other certificate or degree programs may focus on software tools (e.g. Tableau) that try to remove the need for programming from the job of a data analyst, we feel this approach is limited. All of these tools work in limited scenarios, with limited data sizes and formats. Our program focuses on the fundamentals of data science, including how these sorts of tools work. But more generally, as the tools change we want to train our students to understand what is going on behind this change, how to analyze and handle a problem when the data is too big for a tool, and how to investigate and evaluate new developments before they are integrated into easy to use mainstream programs. For instance, a tool or package may present many options to learn a classifier (e.g. given nicely formatted and labeled customer data, predict which of the future potential customers will contribute to a net profit), our program aims to teach students which option to use, how much to trust that prediction, how to apply this to very large datasets, and potentially how to go beyond the presented options.

If an exciting new technique is described in a brand new research paper about data science, we hope our students will be trained to find, understand, and put this technique to work.

Q: What language(s) should I know, or prepare before I start?

A: Most of the classes are fairly agnostic to the programming language, and can be completed in many languages ranging from R to Python to C++. High level languages like python, or to some extent even including R, are becoming built out to deal with larger scale. More importantly, they now have many useful libraries that really eases the implementation of many core tasks in data management and analysis. On the other hand, knowing and using a more low-level language like C, C++, or Java, will perhaps allow students to get more out of the classes and assignments. Students have witnessed 3 to 4 orders of magnitude speed up on some assignments using Java instead of R.

Certain classes may require C, SQL, or Processing (based on Java), but (with the exception of CS6530 Database Systems) do not expect much if any prior experience in those languages by the students.


Q: How much does the certificate program cost?

A: It is a bit complicated. It depends on how spread out you take the classes and whether you are a Utah resident or not. For residents at a 1-class/semester pace, the certificate should cost roughly $10,000.

Here are the rate schedules for residents (lived in Utah last 3+ years) and non residents.
So if one takes the classes more rapidly, then it will cost less.
We are investigating normalizing this cost.

Courses and Availability

Q: Which CORE certificate classes are offered next semester?

A: The plan is to offer the following classes every Fall and Spring.

  • cs6630 Visualization for Data Science
  • cs6530 Database Systems
  • cs6350 Machine Learning


  • cs6140 Data Mining
  • cs5530 Database Systems (can be substituted for cs6530)

Q: When will the certificate classes be offered? What times and days?

A: The times and days will be on a semester by semester bases. The most up to date tentative plans for scheduling can be found at the University of Utah Class Catalog. Once you have chosen the appropriate semester, all classes are listed under the CS : Computer Science link.

We plan to mostly video tape and live stream, depending on demand.

Q: Where can I find the official requirements for the Graduate Certificate in Data Science?

A: The document found here serves as the official requirements. It may be updated on an annual basis. You may complete the certificate under any such official document posted during the course of your enrollment in the program.

Q: How many classes per semester do you anticipate for individuals who are actively working?

A: 1 or maybe 2. It will depend on your background. Some classes have a bit less load than others, so pairing two classes with a lesser load, especially if you have some background in the area may be doable. But most students also working full time take 1 class at a time.

That said, some very dedicated students do manage to successfully take 3 or more classes at a time while also working. 3 classes is a full load for full-time MS or PhD students.

Q: Do the Certificate in Data Science classes need to be taken in a specific order?

A: No, not in general. However, given an individual’s background and time off from school, we may guide you towards classes that may be a bit easier first so that it is easier to get back into the swing of things.

Research Centers & Groups

Center for Extreme Data Management Analysis and Visualization (CEDMAV)

Utah Center for Data Science


Aditya Bhaskara
Chris R. Johnson
Mike Kirby
Marina Kogan
Alexander Lex
Valerio Pascucci
Bei Wang-Phillips
Jeff M. Phillips
Vivek Srikumar
Blair Sullivan
Hari Sundar
Shandian Zhe