Online Master's in Data Science for Jobs in Maryland

A 2011 report released by the McKinsey Global Institute found that data scientists will generate billions of dollars of value across the US, especially in three key sectors which feature prominently in Maryland’s employment landscape and economy:

  • Healthcare – Six of Maryland’s largest employers – Johns Hopkins Medical, MedStar Health, CareFirst BlueCross BlueShield, UM Medical System, LifeBridge Health, and Magellan Health Services – are in the healthcare industry, collectively employing over 67,000 according to a 2016 article in the Baltimore Sun
  • Retail – Maryland’s retail sector generated $18.5 billion in 2014 according to the US Department of Commerce, with key companies such as Giant Food, Black and Decker, Verizon, McCormick, Wal Mart, and Sears Roebuck collectively employing 83,769
  • Manufacturing – Maryland’s manufacturing sector generated $19.2 billion in 2014 according to the US Department of Commerce, with key businesses like WR Grace employing 6,300

The soaring value of data scientists in Maryland was recently highlighted nationally in the 2013 Washington Post article, “As demand for big data analysts grows, schools rush to graduate students with necessary skills.” This article profiled one university professor in Maryland who, as head of its marketing analytics program, was struggling to add academic programs focusing on big data to the curriculum at a pace that would keep up with demand.

With some of the highest entry-level salaries and appealing job opportunities in Maryland that range from manufacturing to public-sector social security or to the NSA, a career in data science starts with a master’s education in this rapidly emerging and highly competitive field.

Preparing for a Master’s Degree in Data Science in Maryland

The best way for students to position themselves to become data scientists would be to begin preparing for their master’s degree during their undergraduate studies while also gaining relevant work experience.

Master’s programs look for candidates with a relevant bachelor’s degree and half a decade of work experience that demonstrates basic competencies. Additional means of preparing for admissions or bridging gaps in functional knowledge take the form of:

  • GRE and/or GMAT exam preperation
  • Massive open online courses (MOOCs)
  • Bridge courses

Undergraduate Degree and Master’s Prerequisite Courses

Graduate schools with data science programs are looking for students who fit a particular profile. In terms of undergraduate studies this means:

  • Bachelor’s degree in a quantitative field like applied math, computer science, statistics, or engineering
  • A course load that includes coverage of key disciplines like statistics, calculus I and II, programming languages, quantitative methods, and linear algebra
  • Minimum GPA of 3.0

Relevant Personal and Work Experience for Admissions

Typically, graduate schools are looking for applicants with highly relevant professional experience:

  • At least five years of technical work experience, with an emphasis on experience that demonstrates quantitative skills
  • Personal experience that relates to coding, hacking, mathematics, statistics, database administration, data mining, or programming

Examples of potentially qualifying local experience that can be found in Maryland include:

  • Working with data management, analysis, or statistics with any of the Johns Hopkins Medical Institutions
  • Cyber security and data analysis with Verizon
  • Statistical analysis, troubleshooting, and data analysis with Northrop Grumman
  • Software engineering and development with Leidos

Federal agencies have an important role in Maryland, and those with experience in data analysis and aggregation from any of the following agencies may also demonstrate key skill competencies as it relates to data science:

  • Defense Information Systems Agency
  • United State Cyber Command
  • National Security Agency
  • Central Security Service

Quality performance with an employer is essential for securing the praiseworthy letters of recommendation that are required for admissions into a master’s program in data science.

How to Succeed on GRE/GMAT Exams

Prospective master’s students can demonstrate core-competency in key data science skills by scoring in at least the 85th percentile of the GRE and/or GMAT exams. Previous students who have taken these exams recommend working sample math problems from the practice resources until the methodology of solving the different types of problems becomes second nature.

GRE – The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section evaluates the following:

  • 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 Educational Testing Service’s (ETS) Math Review.

The GRE is also offered in two relevant subject tests, covering the following topics:

Physics – physics test practice book

  • Classical mechanics
  • Electromagnetism
  • Optics and waves
  • Thermodynamics
  • Statistical mechanics
  • Quantum mechanics
  • Atomic physics
  • Special relativity
  • 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 students’ skills in data analysis. One of the four main sections of the GMAT, the quantitative portion is comprised of 37 questions to be completed in 75 minutes. All of these questions pertain to data sufficiency and problem solving. Study aids to help aspiring graduate students prepare for the GMAT include:

Filling Gaps in Functional Knowledge Through MOOCs and Bridge Courses

MOOCs – Massive Open Online Courses – Online access to filmed lectures, problem sets, and interactive user forums combined with professors and teaching assistants make MOOCs a valuable resource for filling any gaps that exist in functional knowledge programming or other fundamentals prior to enrolling in a master’s program. These range from open-access public learning environments to forums available only to aspiring professionals in a specific field like data science, engineering, mathematics, physics, or statistics. MOOCs are for prospective graduate students looking to fill gaps in knowledge prior to applying to a graduate program.

Bridge Courses – Many graduate schools will provide data science students with programs that bridge any gaps in functional knowledge before beginning graduate coursework. For example, students coming from an undergraduate background in engineering could attend bridge courses to bring them up to speed with fundamental topics in data science like:

  • Analysis of algorithms
  • Linear algebra
  • Data structures
  • Programing in languages like JAVA, C++, Python, and R

Bridge programs are offered through graduate schools as a precursor to graduate-level coursework and are designed for students that have met all other enrollment criteria and that have already been accepted to the graduate program. Bridge courses typically take about 15 weeks to complete.

Earning a Master’s Degree in Data Science in Maryland

New data science graduate programs are springing up throughout the nation in an attempt to meet the demand of growing student interest amid a national call for more skilled data scientists in industry and the public sector. Would-be graduate students have the option to pursue accredited online programs that provide flexible options designed to accommodate students’ work schedules. Master’s programs in data science are comprised of around 30 semester credits and can be completed at a different pace depending on a student’s needs:

  • 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

Relevant graduate programs include:

  • Master of Science (MS) in Data Science
  • Master of Information and Data Science (MIDS)
  • Master of Science in Data Science (MSDS)
  • Online Certificate in Data Science
  • Data Science Certificate
  • Graduate Certificate in Data Science
  • Data Mining and Applications Graduate Certificate

Core Curriculum and Immersion

Master’s-level graduate students cover core curriculum topics that include:

  • Experimental statistics
  • Data research design and applications
  • File organization and database management
  • Data storage and retrieval
  • Network and data security
  • Experiments and casual inference
  • Machine learning and artificial intelligence
  • Information visualization
  • Statistical sampling
  • Ethics and law for data science
  • Data mining
  • Quantifying materials
  • Scaling data – macro and micro
  • Advanced managerial economics
  • Applied regression and time series analysis
  • Visualization of data
  • Immersion

As part of their education process, schools also include an important immersion experience. This emphasizes group data science activities to achieve specific goals with projects. This also creates an opportunity to get to know other students as well as faculty members, and cross-pollinate ideas or working styles. Prospective employers pay particular attention to the immersion experience as this represents real-world applications of data science.

Key Competencies and Objectives

Students who earn their master’s degree in data science should be able to exhibit these core competencies and apply them in the workplace:

  • Be able to work in teams to achieve specific goals
  • Be able to interpret and communicate results
  • Be able to develop and conduct sophisticated data analyses
  • Learn programming languages such as GitHub, SAS, Python, and Shiny by Rstudio
  • Be able to conduct database queries
  • Become familiar with hash algorithms, cyphers, and secure communications protocols
  • Be able to conduct association mining and cluster analysis
  • Be able to run an analysis of survey data
  • Develop innovative design and research methods

Career Opportunities in Maryland for Data Scientists with Advanced Degrees

Data scientists play an important and growing role, especially in businesses that comprise key economic sectors of Maryland. The following top Maryland employers are listed here as they were identified in a 2016 Baltimore Sun article, cross-referenced with some of the top data science fields as identified by a 2011 McKinsey Global Institute report and the US Department of Labor in 2013:

  • MedStar Health – The McKinsey report found that data scientists could add $300 billion to the US healthcare sector each year
  • Verizon – US Department of Labor identified telecommunications as one of the top fields for data scientists
  • Black and Decker – The McKinsey report found that data scientists could increase the operating margin of US retailers by over 60 percent

Upon graduating, students will have the opportunity to translate their academic knowledge into professional work experience. (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 Maryland, completed in February 2016):

Research Data Analyst with Johns Hopkins University in Baltimore – This professional is responsible for data analysis related to healthcare, including data manipulation, table preparation, methodology development, data resourcing, and computer programming. Applicants can meet the minimum requirements for this position with a master’s degree in a related field.

Cyber Data Scientist with Verizon in Silver Spring – Working to detect anomalies, malicious patterns, and suspicious changes in customer behavior, this professional collects and analyzes cyber security data from multiple sources. Preferred applicants hold a master’s degree in a related field and should have experience with regressive statistical analyses for modeling large data sets.

Data Warehouse Analyst with Northrop Grumman in Woodlawn – While Northrop Grumman is best known for its work within the defense industry, it also has government contracts that relate to social security. This analyst works on a project in this latter group to develop, modernize, and implement an Enterprise Data Warehouse (EDW) ecosystem, with responsibilities that include system architecture, target mappings, large data set acquisition, and expansion of an on-site Hadoop cluster. Applicants can meet the minimum requirements for this position with a master’s degree in a related field plus four years of work experience.

Data Scientist with the National Security Agency at Fort Meade – Applicants who are able to pass a security background investigation and polygraph test are qualified to apply for this position, which involves multi-tangential big-data cloud analysis to protect national security. Applicants can meet the minimum experience and education requirements for this position with a relevant master’s degree.

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