The world of technology is fast finding its home base in the Salt Lake Valley, a place where the high concentration of tech-savvy college grads creates an eager pool of real talent that are expanding an already-booming startup scene. Dubbed “The Silicon Slopes,” Utah’s data science industry is gaining momentum and is being eyed as the next hub of technological advancement in the United States.
- Grand Canyon University - B.S. in Business Information Systems and M.S. in Data Science
- SMU - Master of Science in Data Science - No GRE Required.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
In 2020, CompTia found the Slopes supported more than 100,000 tech industry jobs, with the growth rate ranked 16th in the nation. Many of those jobs are found in companies with a heavy reliance on data science, like financial processing platform Divvy, integrating real-time tracking for every single business transaction at client firms, or drug discovery platform manufacturer Recursion Pharmaceuticals that uses powerful AI routines to run millions of rapid, automated experiments to discover new therapeutics.
That’s all driving demand up the wall for data science professionals in Utah. According to tech industry recruiting firm, Robert Half, salaries are through the roof as companies go all in competing for top talent, and data scientists are leading the pack with starting offers ranging from $114,000 to nearly $195,000. And you can bet it’s the candidates with master’s degrees that are at the top of that range.
Preparing for a Master’s Degree in Data Science in Utah
Candidates applying for admission to a data science master’s program in Utah must meet all kinds of stringent requirements, which include passing proficiency examinations with high scores, completing program-specific undergraduate courses, and possessing considerable work experience or internships in a data science field. Applicants can expect a very competitive selection process, as demand is currently higher than the number of program slots that have been ramped up in higher education.
Undergraduate Degree and Master’s Prerequisite Courses
Your preparation for a master’s application will start early… as early as when you select your major at the undergraduate level. Admissions officers look for a bachelor’s in a quantitative field, such as Applied Mathematics, Engineering, or Business Analytics. Additionally, it’s not enough to simply slog through and come out the other side with a diploma; a minimum GPA of 3.0 for undergraduate studies is often mandatory.
Relevant Personal and Work Experience
Previous work experience in data analysis or data management is always a plus and sometimes a requirement when being considered for admission to graduate-level data science programs. Many admissions departments expect to see a detailed CV along with the application. This is used to determine the professional, academic, and experiential merit of the prospective student.
In addition to work experience, applicants to the master’s program will tend to rank better if they can demonstrate some real-world communication skills and the ability to work effectively on teams.
For students looking to gain a foundation in data science on the work front before beginning a master’s program, Utah’s Silicon Slopes offer many entry-level positions and internships in related fields.
Such positions include:
- Research Analyst at the State of Utah Department of Health in Salt Lake City, UT
- Director of Data Science at Progressive Leasing in Draper, UT
- Data Scientist at Mountain America Credit Union in West Jordan, UT
- Technical Intern at Northrop Grumman in Ogden, UT
Preparing to Score Within the 85th Percentile on the GRE/GMAT
Unless you qualify for a waiver based on past work experience, the importance of scoring above average on the Graduate Record Exam (GRE) and/or the Graduate Management Admissions Test (GMAT) when required cannot be overstated in the admissions process. Candidates with scores in the 85th percentile are often shown preference.
Most admissions directors for data science graduate programs place the highest priority on the Quantitative Section of the GRE/GMAT. However, students should not disregard the importance of the exam as a whole. Strong scores in the Verbal and Writing Sections will be expected, as well. Of course, it’s essential to be able to demonstrate an extensive working knowledge of concepts within the data science field, but it’s also important to be capable of communicating that knowledge clearly.
The following is a sampling of the basic knowledge and correlating skills tested in the Quantitative Reasoning portion of the GRE:
- Basic arithmetic: integers, square roots, factorization, and exponents
- Algebraic expressions, linear and quadratic equations, graphing, and functions
- Geometrical equations, proofs, and the Pythagorean Theorem
- Specific data analysis topics including standard deviation, permutations, tables, probabilities, statistics, etc.
Practice exams and study guides are available on the official GRE website to aid in preparation.
The Graduate Management Admissions Test (GMAT) surveys the aforementioned subjects and includes more subjective word problems in an effort to measure the student’s capacity to analyze data sets and organize the information to support conclusions.
Test prep resources and study guides for the GMAT can be accessed through the official website. Supplemental practice exams are offered through Veritas Prep and The Princeton Review.
The University of Utah offers prep courses through the Office of Continuing Education and recommends the following resources to supplement their classes:
- Khan Academy (Quantitative Section Only)
- GMAT Prep Now Youtube Channel
- Kaplan GMAT Youtube Channel
- Dominate the GMAT Youtube Channel
- DoD MWR Library (For Military Personnel)
Of course, it’s not uncommon to qualify for a waiver to the test score requirement. The University of Utah, for example, offers exceptions to senior leaders in data science organizations with five or more years of experience.
Online Data Science Bootcamps to Get You Job-Ready or to Prepare for a Master’s Program
Let’s say you didn’t get that kind of work experience, and you didn’t pick up a bachelor’s degree in a field admissions departments prefer. You still have some options to burnish your CV to the point where they will actively consider you for admission, and one of the best of those is attending a data science bootcamp.
Bootcamps are intensive multi-week or multi-month courses that focus in on hardcore, practical data science applications. They are offered at a variety of skill levels, from the entry level up to those intended for already practicing, advanced stage data scientists. Bootcamps of all stripes skip past the theoretical and academic aspects of the field to give you a hands-on, realistic experience using tools like:
- Structured Query Language and SQL-based relational data stores
- NoSQL and Hadoop-style big data stores
- Python and R programming with a focus on statistical libraries like Numpy
- Data visualization and display tools like Leaflet.js
- Machine learning and AI analytics techniques
You get all these lessons directly from instructors with actual experience in the field, full of ideas drawn from current practices and techniques developed at the cutting-edge. The camps are usually cohort-based and project-driven, giving you an abundance of experience in working with your fellow students to solve the kinds of problems data scientists deal with every day, often using real data sets.
While programs in the past were often on-site only and full-time, you can increasingly find part-time programs offered online, like the six-month University of Arizona Data Analytics Boot Camp. With a full university data science department behind it, you’ll find additional resources and a well-developed career services team with experience prepping graduates, both for job interviews in the commercial world and master’s program applications.
Gaps in Functional Knowledge Can Be Filled with MOOCs and Bridge Courses
If there are some prerequisites you don’t have under your belt from your undergraduate program, you can address topical weaknesses by supplementing your education with a Massive Open Online Course (MOOC) independently, or through bridge courses offered through the university you’ll be attending. Both options can be found in both online and live-class options.
MOOCs help students develop knowledge and skills in an independent study format. Once enrolled, students gain access to archived online classes, lectures, and study materials that serve to fill any gaps in prerequisite comprehension so that the student can begin master’s-level courses in data science. These courses provide an introductory foundation that grad courses build on.
Students can also take advantage of bridge courses, which are offered by their graduate schools for students accepted into the master’s program but who still need some foundational courses. Bridge courses are standard for anyone who has met the initial admission requirements, but lack the area-specific courses needed to begin graduate-level studies in data science. For example, students who possess an undergraduate degree in an unrelated field can take bridge courses in the following subjects to augment their base knowledge:
- Python for Data Science
- Introduction to Linear Algebra
- Fundamentals of Data Structures and Algorithms
Bridge courses are typically 15 weeks long, and often offered in the summer prior to the commencement of the master’s program. Once completed, students can move on to the official coursework within their graduate program.
Earning a Master’s Degree in Data Science
Data science is one of the fastest growing tech industry career paths in Utah. In light of the growing demand, graduate schools are providing groundbreaking programs, both online and in live classrooms.
A typical M.S. program requires between 30-36 credit hours, on average. 2/3 of those credits are generally made up of core courses. For traditional students taking classes full-time, the graduate coursework can be completed within 3 semesters. Some accelerated programs can be found that allow a fast-track curriculum to be completed in 12 months, or 2 semesters. Part-time programs are less common, but some schools allow extended completion times that stretch coursework over 5 semesters. Online and night classes are often available for students with full-time jobs.
Curriculum and Core Coursework
Data science grad students dig into a wide array of different areas of investigation. You often have a lot of options for customizing your degree plan, but a selection of the coursework that can be expected within the core master’s program is listed below:
- Advanced Statistical Methods
- Scripting Languages
- Database Concepts
- Artificial Intelligence
- Applied Multivariate Analysis
- Models of Computation for Massive Data
- Big Data Strategy
- Predictive Analytics
- Business Intelligence
- Prescriptive Analytics
- Data and Text Mining
- Decision Theory and Business Analytics
- High-Performance Computing and Parallelization
Key Competencies and Objectives
After completing coursework and obtaining a master’s degree in data science, graduates will be prepared to successfully navigate a career as a data scientist, competent in the following skills:
- Network/cyber security
- Data collection, analysis, and real-time application
- Data mining
- Database queries and management
- Data cleansing
- Programming languages, such as Python, R, and SAS
- Database queries
- Statistical research
Career Opportunities for Data Scientists in Utah with Advanced Degrees
Jeff Philips, Assistant Professor of computer science at the University of Utah and coordinator for the university’s new graduate certificate in big data, affirmed that big data is also big business. “We’re seeing a revolution in the availability of data,” Philips said. “It’s easy to collect information, but processing and analyzing large stores of data is becoming increasingly difficult. We are at the point where the traditional analytical tools for attacking this problem are breaking down.” Because of this, business owners and CFOs in Utah are actively seeking qualified data scientists with the ability to meet the data influx head-on, and the current job market reflects it.
That’s clear when looking at DICE’s 2020 Technology Jobs Report, which places data scientists and data engineers as two of the three fastest growing professions in the technology field on a year-over-year basis. Engineer positions grew by 50 percent since 2019, with senior data scientists not far behind with a more than 30 percent growth rate.
The following job listings are shown as illustrative examples of positions found in Utah, and are not meant to represent job offers or provide any assurance of employment.
Senior Statistical Data Analyst, Intermountain Healthcare, Salt Lake City, UT
- Produce analytical solutions (insights, statistical analysis, and models, etc.) for leadership and stakeholders across the organization that supports business or clinical initiatives
- Provide consultation and support in the development, analysis, interpretation, and management of a variety of highly complex data sources
- Support process improvement, operations, strategy, cost reduction, and increased patient safety and satisfaction
Data Scientist, Productive Data Solutions, Inc., Salt Lake City, UT
- Maintain and refine existing engineering applications with the focus on science-driven programs (network optimization models, etc.)
- Extract, consolidate, compile data from various data sources
- Conduct ad-hoc data analyses to provide decision support to business users
- Develop proprietary automation and decision support tools as needed
- Gather requirements related to analytical needs from end users
- Present analysis findings and/or recommendations to business users including senior executives
- Help Director of Engineering identify and prioritize “big rock” projects
- Coach junior team members and help them build data analysis skills