Maryland is well-positioned to benefit from the buzz around big data today. With acclaimed institutions like Johns Hopkins holding down the private sector side of the medical research that generates terabytes of data daily and the U.S. Army Medical Research Institute of Infectious Diseases (USAMRIID) at Fort Detrick on the federal side of the game, the state has both the demand and the resources to drive data science jobs to the next level.
And that’s quite a high bar. According to the Robert Half 2020 Technology Salary Guide, data scientists earn between $105,750 and $180,250 annually, while big data engineers can command between $130,000 and $222,000 per year. They are worth every penny, as everyone in the world can see.
One of the first and the most accurate sites that was able to process and display the COVID-19 outbreak was the Johns Hopkins Dashboard by the Center for Systems Science and Engineering. The dashboard proved to be a poster child for the advantages of highly trained data science professionals: a combination of relevant, accurate data, together with well-considered, intuitive, understandable visualizations of that information.
Those are skills that will continue to be in strong demand in life sciences long after COVID is conquered. The major life sciences sector in Maryland, centered around Frederick County in the BioHealth Capital Region and the Frederick Innovative Technology Center forecast strong demand for data science professionals even during economic downturns. Private sector companies like the National Cancer Institute and the Frederick National Laboratory for Cancer Research are hiring alongside the Food and Drug Administration and the National Institutes of Health.
With some of the highest entry-level salaries and appealing job opportunities in Maryland ranging from medical R&D and manufacturing to database administration and analysis with the NSA, a career in data science starts with a master’s education in this high-demand yet 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 is to begin preparing for their master’s degree during their undergraduate studies. There are core skills that you will need to establish early, or be forced to back up and relearn later. Master’s programs also like to see some evidence that you have actually been able to apply those skills in the real world as well, usually through some demonstration of your work experience in a relevant field.
Master’s programs look for candidates with a bachelor’s degree in a related field 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 preparation
- 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:
- A 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
- A minimum GPA of 3.0
Relevant Personal and Work Experience for Admissions
Typically, graduate schools are looking for applicants with highly relevant professional experience. That typically includes five years or more of technical work experience, with an emphasis on experience that demonstrates quantitative skills; anything to do with programming, analytics, database administration, or relevant mathematical functions will do.
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
It’s not enough to just hold down a job, however. You’ll need to have performed well enough to get some recommendations from supervisors along the way.
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. Students who have previously 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) contains a quantitative reasoning section that 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
- 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 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:
Data Science Bootcamps Build Skills For Master’s Program Applications and Prepare You for the Job Market
It should be clear by this point that getting into a master’s degree program will involve a lot more than just filling out an application and dropping it in the mail. These programs are in enormous demand and the competition is fierce. That means standards are high, and without the right background and education, you are at the bottom of the pecking order when it comes to admissions.
This is a place where an unconventional solution might give you a boost. Enrolling in a data science bootcamp is exactly that kind of out-of-the-box preparation that admissions committees love to see.
A bootcamp isn’t an easy choice. Although it will last only weeks or months, and cost a lot less than a full-fledged degree program, it will feature a fast-paced, rigorous, focused course of study on the latest technologies and techniques being used in the field. Bootcamps drill down into the practical applications of the latest technologies, skipping past a lot of the theoretical groundwork that might make understanding easier… instead, you’ll learn by doing, and you’ll do it all quickly and without looking back.
Bootcamps exist across the spectrum of skills and technologies that exist in data science today, so some of them won’t be appropriate as a master’s prep course. You’ll need to find one that delivers education at an entry-level, something like The Data Analytics Bootcamp at Johns Hopkins Engineering, which is available online or in person from this prestigious Maryland institution.
The Hopkins program is part-time, which is unusual in the bootcamp field, but perfectly oriented toward students who are currently working or already studying in a different college program. Like most bootcamps, it is based around a set cohort, meaning you’ll be working with the same fellow students and instructors through the entire six-month program of study, problem-solving and learning together on real-world datasets in mock projects that mimic actual employer demands.
The technologies and skills you will be taught include:
- Advanced statistical analysis techniques
- Social media analysis
- HTML and CSS
- Big Data analysis and Hadoop data stores
- SQL and relational databases like SQL server and MySQL
Most bootcamps come with career services tacked on for added value, and the Hopkins program is generous with these… you will get access to a dedicated Profile Coach and Career Director who will help you burnish your interview skills, build a competitive resume, and sort out your project portfolio to impress either possible employers or college admissions committees. It’s an intensive but entirely worthwhile preparation for a full-fledged master’s program.
Filling Gaps in Functional Knowledge Through MOOCs and Bridge Courses
Bootcamps aren’t your only option, however. For something a little less stressful, and a little more targeted, you can consider both MOOCs and bridge courses offered by the schools themselves to help boost your skill levels.
MOOCs – Massive Open Online Courses – Online access to video 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 can help 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
- 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 program titles may 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
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
- 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
As of 2019, Maryland is one of the states with the highest level of employment for data scientists according to the U.S. Bureau of Labor Statistics, with more than 2,500 active in the field. Burtch Works, a firm that analyzes professional compensation for technology professionals, found in their 2019 analysis of Data Science and Predictive Analytics market trends that compensation tends to be highest for such professionals along the coasts… including Maryland.
The company also identified a trend in the industry looking for ever more and more highly specialized data scientists, with additional training in highly specific areas such as natural language processing or computer vision. That only drives up the value of master’s programs, where you can develop exactly that sort of expertise. Burtch Works found that the best trained and educated data scientists could bring in $250,000 a year in supervisory positions.
Upon graduating, students will have the opportunity to translate their academic knowledge into some of that cash. The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
Research Data Analyst with Johns Hopkins University in Baltimore – This role 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 position 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.