Data science is perhaps the hottest field in Minnesota, yet this emerging field is so new that as of 2016 the Minnesota Department of Employment and Economic Development (MNDEED) has yet to officially include data scientists in the state’s occupational data classifications. The irony is that these highly sought-after professionals are doing more to drive growth in the state across all industries than virtually any other single job classification.
Headquartered in Minneapolis, Target, the second-largest retailer in the United States, continues to rely heavily on data scientists, and is benefitting from new data science applications. In 2012, a Minnesota Public Radio piece identified 50 data scientists working for Target whose job was to mine and clean customer data, and develop and deploy algorithms for machine learning with the ultimate goal of identifying new ways for the company to increase sales. Specifically, this involved developing algorithms that would take input such as shopping habits and create an output in the form of customized checkout coupons and a customized web interface for each customer.
In fact, Target’s data scientists were doing such a good job they even generated controversy when they developed an algorithm that could predict, with impressive accuracy, which customers were likely to be pregnant. 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.
Whether in retail and logistics, healthcare and biotechnology, or banking and finance, among many other sectors, data scientists are increasingly being looked upon as imperative to increasing the bottom line in the data-driven business environment of the 21st century. As local businesses, multinationals and public sector organizations alike continue to seek highly qualified data scientists, the value of a master’s degree in data science is reflected in higher starting salary offers.
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.
Prospective data science graduate students are expected to be proficient in key subject areas 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 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
Tomorrow’s senior data scientists start preparing for their careers as freshmen in college. Data science master’s programs typically consider students that meet high admissions standards that 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 quantitative skills, as proven through praiseworthy letters of recommendation. Personal experience that relates to data mining, coding, programming, hacking, database administration, mathematics, and statistics can also be very beneficial.
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 mechanics
- Optics and waves
- Statistical mechanics
- Quantum mechanics
- Atomic physics
- Special relativity
- Lab methods and specialized topics
Mathematics – mathematics test practice book
- 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
Filling Gaps in Functional Knowledge with MOOCs and Bridge Courses
Massive Open Online Courses – Offering recorded 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, STEM, 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.
Bridge programs that develop fundamental core-subject competency can be offered in areas like:
- Algorithm analysis
- Linear algebra
- Data structures
Bridge programs specifically related to programming can help new graduate students master:
Earning a Master’s Degree in Data Science
As data scientist earned Computerworld’s hottest job ranking of 2016, 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. Comprised 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
Within the field of data science, master’s-level graduate students cover core topics that include:
- Experimental statistics
- Data storage and retrieval
- Data research design and applications
- File organization and database management
- Network and data security
- Information visualization
- Experiments and casual inference
- Machine learning and artificial intelligence
- Statistical sampling
- Quantifying materials
- Ethics and law for data science
- Data mining
- Macro and micro data scaling
- Visualization of data
- Advanced managerial economics
- Applied regression and time series analysis
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.
Core Competencies and Objectives
A master’s degree in data science demonstrates that students possess these highly competitive and valuable capabilities:
- The ability to achieve specific goals by working in teams
- The ability to interpret and communicate results
- The ability to develop and conduct sophisticated data analyses
- The ability to conduct database queries
- The ability to conduct association mining and cluster analysis
- The ability to run an analysis of survey data
- The ability to develop innovative design and research methods
- Familiarity with hash algorithms, cyphers, and secure communications protocols
- Competency in programming languages such as GitHub, SAS, Python, and Shiny by Rstudio
Career Opportunities in Minnesota for Data Scientists with Advanced Degrees
A 2011 report released by McKinsey & Company details that data scientists can generate billions of dollars in value for key industries and sectors in Minnesota, from government and healthcare to retail and logistics. According to the Minnesota Department of Employment and Economic Development, the state’s top employers are the Mayo Clinic, government, Target, and Allina Health System.
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 15, 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 quantitative fields.
(The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment. These examples were taken from a survey of job vacancy announcements for data scientists in Minnesota, completed in February 2016):
Quantitative Analyst with US Bank in Minneapolis
- Working within corporate treasury, 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
- The professional hired for this position is 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
- Calabrio specializes in multi-channel contact centers
- The machine learning engineer would be 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