The field of data science is experiencing major growth around the world and across the country, and Minnesota is no exception. Based in the Twin Cities, non-profit MinneAnalytics was founded to serve the data science and emerging technology community in the state. Today it has more than 17,000 regional members, drawn from every level and type of analytics profession.
Some of them work at companies like Target, one of the largest retail chains in the United States. Headquartered in Minneapolis, the company is famous in data science circles for a well-publicized story in which the ad-targeting algorithms deduced that shoppers were pregnant based strictly on shopping and purchase patterns, sometimes even before the shoppers themselves knew. The algorithm was so accurate that it was able to identify which trimester the customer was in. This translated into a measurable increase in profits for Target, and better access to the products the store’s customers preferred.
All of that points to the importance of a well-educated, highly-trained data science workforce, with analysts who both understand how to evaluate data and how to use their insights responsibly and effectively with respect to customer privacy concerns.
Whether in retail and logistics, healthcare and biotechnology, or banking and finance, master’s-educated data scientists are emerging as key players to increasing the bottom line in the data-driven business environment of the 21st century.
Preparing to Enroll in a Master’s Program in Data Science
As data science emerges as key to identifying new revenue streams, today’s employers prefer applicants with the proficiencies and leadership skills that come with earning a master’s degree in data science.
As a prospective data science graduate student, you will be expected to already be proficient in the key skills that underpin the field and should be able to demonstrate functional knowledge of these proficiencies by achieving high scores on the GRE and/or GMAT exams.
Aspiring graduate students have opportunities to fill gaps in functional knowledge by attending a data science bootcamp, completing massive open online courses (MOOCs) prior to applying to a master’s program, or by completing quantitative and programming bridge courses before transitioning to graduate-level courses.
Academic Prerequisites for a Master’s in Data Science
Your preparation to enter a data science graduate program will have to start early: you probably need to start making preparations even while a freshman in college. That’s because the scales on which you will be weighed by graduate admissions departments will include:
- Undergraduate degree in a quantitative field like engineering, applied math, statistics, or computer science
- Prerequisites in calculus I and II, statistics, programming languages, linear algebra, and quantitative methods
- A minimum GPA of 3.0
- Extensive relevant work history
Relevant Work Experience for Admissions
Data science graduate programs generally expect applicants to have at least five years of related technical work experience that demonstrates proven quantitative analysis skills. Personal experience that relates to data mining, programming, database administration, mathematics, and statistics are also a boost to your application.
Some examples of the kinds of positions and local employers in Minnesota that would support the work experience requirements of a data science graduate program might include:
- Providing computer programming, coding, or cyber security services for the Mayo Clinic
- Working to develop models with the State of Minnesota to improve efficiency in any number of areas from human resources or distribution to emergency preparedness
- Working with Target to improve data gathering capabilities or conduct efficiency analyses for areas such as human resources, distribution, sales, orders, or customer satisfaction
Preparing for Success on the GRE/GMAT Exams
Prospective data science graduate students are generally expected to score within the 85th percentile of the GRE and/or GMAT exams.
GRE – The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section measures competency in the following areas:
- Arithmetic topics including integers, factorization, exponents, and roots
- Algebraic topics such as algebraic expressions, functions, linear equations, quadratic equations, and graphing
- Geometry, including the properties of circles, triangles, quadrilaterals, polygons, and the Pythagorean theorem
- Data analysis, covering topics like statistics, standard deviation, interquartile range, tables, graphs, probabilities, permutations, and Venn diagrams
Students can prepare for the quantitative reasoning section by reviewing resources such as the following:
- Educational Testing Service’s (ETS) Math Review
- GRE practice exams through the Princeton Review
- GRE practice exams through Veritas Prep
The GRE is also offered in two relevant specific subject tests, covering the following topics:
Physics – physics test practice book
- Classical and quantum mechanics
- Electromagnetism
- Optics and waves
- Thermodynamics
- Lab methods and specialized topics
Mathematics – mathematics test practice book
- Calculus
- Algebra
- Introductory real analysis
- Discrete mathematics
- Probability, statistics, and numerical analysis
GMAT – the Graduate Management Admissions Test’s (GMAT) quantitative section evaluates abilities in data analysis. As one of the four main sections of the GMAT, the quantitative section is comprised of 37 questions to be completed in 75 minutes. All the questions in the quantitative section pertain to data analysis and problem solving.
GMAT practice exams can be found through
Preparing for a Master’s Program or Employment Through Data Science Bootcamps in St. Paul or Online
As long as you are thinking ahead about how to prepare yourself to be accepted into a data science master’s program, think about this: enrolling in a data science bootcamp can give you both the experience and the skills you need to meet those qualifications, or to move straight into the job market in only a handful of months.
Bootcamps are a relatively new concept that mixes fast-paced instruction with reasonable costs and exposure to cutting-edge tools. You’ll find yourself crunching real-world data for the kind of project-driven problem-solving exercises modeled after what data scientists do every day. Overseen by veteran instructors, you learn with a cadre of other students in a practical, hands-on environment that builds a portfolio and real skills that admissions committees will view favorably.
Market demand drives the technologies that you are trained in, and you don’t need anything more than a GED and to have hit your 18th birthday to get in. Other types of bootcamps aim at mid-career professionals and higher skill levels, but this is exactly what you need to prepare for a master’s degree. Comprehensive career support is available as well if you choose to head right for a professional position after completing the camp.
Filling Gaps in Functional Knowledge with MOOCs and Bridge Courses
Massive Open Online Courses – Offering online lectures, interactive user forums, and problem sets, MOOCs can be a valuable resource for prospective students who want to be proactive about developing core-subject proficiency in a specific area before applying to a graduate program. MOOCs are offered in specific fields like programming, physics, statistics, and data science.
Bridge Programs – These individual classes and class sequences are offered by colleges and universities to help new graduate students bridge gaps in functional knowledge before beginning graduate-level coursework. These pre-master’s courses are made available to students that have met all other criteria for enrollment and that have already been accepted into the master’s program.
You’ll find bridge courses that focus on both core-subject competencies, like algorithms, math, and data structures, and those that aim at increasing your level of technical skills in coding, focus on languages commonly used in data science like Python and R.
Earning a Master’s Degree in Data Science
As data scientist earned CIO Magazine’s hottest job ranking of 2020, data science is also spawning new master’s programs throughout the nation and in Minnesota. Relevant in-state programs include:
- Master’s of Science (MS) in Data Science – Minneapolis
- Master’s of Science (MS) in Data Science – Saint Paul
Online data science master’s programs are also widely available. With an average load of around 30 semester credits, these programs offer options designed to accommodate a range of student preferences:
- Traditional completion time – approximately 18 months or three semesters
- Accelerated completion – completion in as little as 12 months or two semesters
- Part-time – completion in as much as 32 months or five semesters
- Certificate programs can be completed in one to two semesters
The most relevant online data science programs result in credentials like:
- Master of Science (MS) in Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Graduate Certificate in Data Science
- Data Mining and Applications Graduate Certificate
Core Curriculum and Immersion Experience
Getting accepted into those programs is just the start of the hard part, of course. Once you’re in, you’ll get a rigorous education in aspects of data science like:
- Data storage and retrieval
- Data research design and applications
- File organization and database management
- Network and data security
- Information visualization
- Machine learning and artificial intelligence
- Ethics and law for data science
Programs typically culminate with an immersion experience that involves a group application of core skills to achieve specific goals. During immersion, students work together to demonstrate what they have learned throughout the course of their master’s program, and are evaluated by professors as well as visiting prospective employers. On the plus side, the result of this experience will form a core to your portfolio to demonstrate your skills.
Core Competencies and Objectives
Those skills will need to include more than just technical competence. Employers will be looking for other elements in your capabilities, including:
- The ability to achieve specific goals by working in teams
- Communication and presentation of key concepts and results
- The ability to develop innovative design and research methods
- Familiarity with hash algorithms, cyphers, and secure communications protocols
Career Opportunities in Minnesota for Data Scientists with Advanced Degrees
Although big data has wide-ranging applications and impacts, it’s particularly heavily favored in healthcare today. A 2019 report from Research And Markets showed the global healthcare data analytics market was worth $19.6 billion in 2018, and projected it would reach $47.7 billion by 2024. That’s good news for Minnesota data scientists: according to the Minnesota Department of Employment and Economic Development, the state’s top employers are the Mayo Clinic, government, Fairview Health Services, and Allina Health System—three out of four directly involved in the healthcare, and the fourth partially involved.
While the opportunities for data scientists to improve efficiency and implement analysis techniques at a company like Target are obvious, these professionals are also just as important for the state’s smaller businesses. An example of this is Flywheel, a startup company headquartered in Minneapolis that specializes in building software platforms that allow data and algorithm sharing for scientific research groups. With a staff of 30, Flywheel depends on its employees to implement a range of data science core-competencies like model development, computer programming, database interpretation, and data analysis.
Because master’s programs in data science have only been available for the past several years, many employers specify that they are looking for candidates with advanced education in other quantitative fields.
These are some examples of data science jobs available in Minnesota:
Quantitative Analyst with US Bank in Minneapolis
- The quantitative analyst is involved with research and development for quantitative models that will allow management to make better informed decisions
- Duties include creating models for credit valuation adjustment, default, interest rates, and Value-at-Risk (VaR)
- Preferred applicants hold at least a master’s degree in a quantitative field, and must have at least four years of experience with quantitative analytics
Data Scientist with the Mayo Clinic in Rochester
- Tasked with focusing on practice optimization
- Duties involve developing predictive models from large-scale data sets using advanced modeling techniques, operations research, machine learning, and data mining
- Preferred applicants have a master’s degree in business analysis, business administration, information science, engineering, information science, or a related field, plus four years of work experience
Machine Learning Engineer with Calabrio in Minneapolis
- The machine learning engineer is responsible for analyzing diverse data sets to find patterns, trends, themes, and errors
- Ideal applicants have a master’s degree in a field like mathematics, computer information systems, or computer science