The Robert Half International 2020 Salary Guide for Technology Professionals report shows that nationally, the salary for data scientists reached a median of $125,250. In New Jersey, where data scientists were already earning some of the best salaries in the nation, these numbers translate into a hefty boost over that baseline. In the Trenton area for example, the starting salary for data scientists ranged between $132,505 – $225,853 in 2020.
As a home to large telecom, healthcare and pharmaceutical companies, New Jersey has been at the forefront of innovation and technology for many years, giving rise to the New Jersey Big Data Alliance (NJBDA) made up of top universities that offer graduate programs in data science.
The NJBDA gives New Jersey’s colleges and universities the opportunity to work with government and industry to identify industry needs, as well as to isolate knowledge gaps among prospective students. This has allowed these schools to tailor their programs to prepare students in a way that fits the needs of the state’s major employers.
Master’s-educated data scientists in New Jersey work for companies like Janssen Research and Development, LLC, the pharmaceutical arm of Johnson & Johnson in Raritan. Here, they mine data to help medical professionals come up with new treatments for a variety of conditions and disorders, including diabetes, neurologic disorders, and immunologic disorders.
The telecommunications company Verizon runs their operational headquarters out of Basking Ridge, and has plenty of work for data scientists. Analysts use their problem-solving and critical-thinking skills along with the latest technology to analyze large and increasingly complex data assets fed into the maw of their data stores by the always-on national communication network. They contribute to investigating and fighting fraud in the company, identifying control gaps in processes, and analyzing root causes of problems.
Preparing for a Master’s Degree in Data Science in New Jersey
If you plan to pursue a Master of Science in Data Science, you need to proactively begin preparing for graduate studies while still completing your undergraduate coursework. Decisions you make as an undergrad will have a real impact on your eligibility and timeline for completing an MSDS. Preparation should include:
- Earning a bachelor’s in a relevant hard science field, with strong coursework in mathematics and programming
- Work experience in a related field displaying quantitative skills, computer programming or database administration
- Studying for and passing the GRE or GMAT, with an emphasis on obtaining high scores on the quantitative portions of the tests
- Filling in any gaps in knowledge though bridge courses, bootcamps, or MOOCs
Undergraduate Degree and Master’s Prerequisite Courses
Graduate-level data science programs are selective, requiring certain criteria in their applicants that often includes:
- A bachelor’s degree in a field such as engineering, statistics, mathematics, computer programming or computer science
- Completion of prerequisite courses including principles of operating systems, probability and statistics, calculus I and II, programming languages, and database design and management
- A minimum GPA of 3.0 on a 4.0 scale
Relevant Personal and Work Experience
Your personal and professional experience will also play into how your application is received. Most master’s programs in data science show preference to applicants who meet the following qualifications:
- Have completed five or more years of technical work experience showcasing their quantitative skills
- Possess letters of recommendation from employers or others who are well-acquainted with the applicant’s personal, work or academic experience
- Have personal experience relating to database administration, programming, statistics, and/or mathematics
Some examples of relevant jobs with local employers in New Jersey that would fulfill experience expectations include:
- Working at JFK Medical Center in Edison in areas such as quantitative analysis, cyber security, programming or coding
- Analyzing computer systems or working in database administration at Lockheed Martin’s Advanced Technical Laboratory in Cherry Hill
- Working as a data analyst at the Center for Public Safety in West Orange
Preparing for Success on GRE/GMAT Exams
Test preparation is going to be a big deal if you’re serious about getting into a master’s program in data science. You’ll want to aim for the objective of earning a score in the 85th percentile or better on the quantitative sections of the respective exams. Even though you’ll note that not all of the subject matter is, strictly speaking, relevant to data crunching, you need to brush up on every aspect that can improve your score to stand out against the competition.
GRE — The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section assesses competencies in:
- Arithmetic areas including factorization, integers, roots and exponents
- Algebraic areas like functions algebraic expressions, linear equations, graphing and quadratic equations
- Geometric topics including the properties of triangles, circles, polygons, quadrilaterals, and the Pythagorean theorem
- Data analysis, with topics including standard deviation, statistics, tables, graphs, interquartile range, permutations, probabilities and Venn diagrams
Study aids for the GRE’s quantitative reasoning section include:
- The Princeton Review’s GRE Practice Exams
- Math Review by the Educational Testing Service (ETS)
- GRE Practice Exams and Prep Options through Kaplan Test Prep
GMAT – The Graduate Management Admission Test (GMAT)’s quantitative section assesses a your abilities in data analysis. In the Quantitative Section, which is one of the test’s four main sections, students must complete 37 questions on data analysis and problem solving in 75 minutes. Study aids to help bachelor’s-educated tech professionals prepare for the GMAT include:
Building Out Your Skills and Qualifications By Enrolling in a Data Boot Camp Located in Somerset, Jersey City, or Online
Regardless of how you do on those standardized tests, admissions committees want to see some real-world experience and practical knowledge along with exam scores. One relatively new option to get a little of both comes in the form of data science bootcamps.
Just as you would expect from the name, boot camps are a pretty intense experience. Running between one and nine months, and oriented at varying skill levels, from very basic all the way up through students who are already far past their master’s degrees and well into their careers. These programs put you through rigorous, hands-on experience crunching through live data in simulated projects using the same kind of cutting-edge tools local companies rely on.
Going through a boot camp to help qualify for a master’s degree is only one use; the camp also has superlative career support services, and many graduates go directly into the job market, deferring a master’s degree until much later in their careers.
Filling Gaps in Functional Knowledge Through MOOCs or Bridge Courses
There are also less intensive options for getting up to speed in the fundamentals. MOOCs and bridge course are two of the most popular.
Bridge Programs – Schools offering graduate programs in data science will often offer bridge courses to students that have met all admissions criteria and that have been accepted into the program, even though they may lack functional knowledge in one or more key areas. These pre-master’s courses are designed to bridge gaps in functional knowledge before students transition to graduate-level coursework. As an example, a student whose undergraduate degree is in computer science might need to take bridge courses in mathematics to prepare for graduate-level data science coursework. Schools usually offer bridge courses in two primary areas:
- Programming, teaching vital languages such as Python, R, C++ and Java
- Mathematics, offering coursework in areas including data structures, linear algebra and algorithm analysis
Massive Open Online Courses (MOOCs) — These courses offer online video lectures, interactive user forums, and problem sets, along with the support of professors and teaching assistants, and can be an indispensable tool in filling in knowledge gaps that prospective graduate students might have. MOOCs provide a pathway to developing key proficiencies for prospective graduate students that wish to proactively prepare before applying to a master’s program in data science.
Earning a Master’s Degree in Data Science in New Jersey
Since Harvard Business Review declared data scientist to be “one of the sexiest jobs of the 21st century,” data science has emerged as one of the hottest fields of study at the graduate level for technically-minded students in New Jersey. Master’s programs in data science can be found all across the state, in cities including Jersey City, Trenton and New Brunswick, as well as online.
Graduate degrees available in New Jersey include, but are not limited to:
- Graduate Certificate in Data Mining
- Master of Business and Science Degree in Analytics- Discovery Informatics and Data Sciences
- Master of Information and Data Science
- Master of Science in Data Science
Total credit hours to compete a typical master’s program in data science range from 30 to 40. Some online schools offer part-time options, but typically require students to graduate within five years of starting the program.
Online graduate degree programs offer the same level of rigor and also range between 30 and 40 credits in length, but include the benefits of a flexible timeline and being accessible from anywhere in the world:
- Traditional – online master’s in data science programs that are typically completed in 18 months
- Part-time – students who opt to study part-time may complete the online program in approximately 32 months
- Accelerated – for those students who wish to work at an accelerated pace, most graduate programs in data science may be completed within a year
Graduate certificate programs usually take less time to complete – a year to 18 months – and range from 12 to 18 credits in length. These programs take the least amount of time, and may be accomplished in one to two semesters. Keep in mind, however, that a graduate certificate does not carry as much weight with employers as a master’s degree in data science. Most employers are looking for data scientists who possess graduate degrees.
Core Coursework, Internship and Emersion Experience
Courses that are typically found in master’s-level data science programs cover topics such as:
- Data analysis and decision modeling
- Statistical programming
- Data and database warehousing
- Data visualization
- Big data analytics
- Data mining
- Data law, ethics and privacy
- Predictive analytics
- Machine learning
- Experimental design
Additionally, unless a student already has three or more years of professional work experience in the field or is employed full-time in a paid data science position, a graduate internship is mandatory. This experience will provide the data science student with real-world work experience and allow prospective employers to ascertain the talents and strengths of potential employees. Many online data science graduate programs also require an internship, which would be offered at specific locations throughout New Jersey that partner with the major online programs.
Most graduate data science programs also require participation in an immersion experience. Students in both traditional and online data science degree programs meet at a central location and collaborate on a specific set of problems, applying knowledge and skills that they have gained from their graduate program. Professors and professionals in the field of data science offer evaluations and assessments of outcomes.
Key Competencies and Objectives
Upon graduation, the holder of a master’s in data science degree should be able to:
- Analyze business problems through the application of data science principles
- Identify and evaluate issues of business ethics relating to privacy, integrity, data security and intellectual property
- Have an ability to break down and analyze very large and disparate data sets
- Use good ethical practices to make everyday business and data management decisions
- Show knowledge of the techniques of statistical data analysis in making business decisions
- Solve problems in the real world through the use of data mining software
- Show competency in programming languages including Python, R, and Javascript
- Display familiarity with cyphers, hash algorithms, and secure communications protocols
Career Opportunities for Data Scientists in New Jersey with Advanced Degrees
The massive data boom taking place throughout the tri-state area puts New Jersey-based data scientists in a unique position to consider jobs even in New York City and Philadelphia. According to staffing firm DICE in 2020, data science positions occupy two of the top three slots for the most in-demand tech jobs in the tri-state area. That’s a lot of opportunity concentrated in one corner of the country.
The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
Data Scientist with Rang Technologies in Piscataway – This entry-level position involves developing predictive models in marketing, data mining, and fraud detection.
Applicants must have a master’s degree in a related field.
Data Scientist with Forbes Media in Jersey City – Responsibilities include identifying potential data products, leading development of new data products and determining what data warehouse and analysis tools to implement.
Requirements include an advanced degree plus three to five years’ experience in a related discipline.
Data Scientist with Horizon Blue Cross Blue Shield of New Jersey in Newark – Responsibilities include creating, deciphering and utilizing statistical and econometric data.
Requirements include an advanced degree in a related field.
Machine Learning Engineer- Entry Level with IP Heights of Edison – This entry-level position involves designing and running experiments to improve core algorithms on consumer devices, as well as developing new algorithms.
Job requirements include a master’s degree and proficiency in Python, deep learning and signal processing.