With some of the top schools in the nation located in Massachusetts, including MIT, the most prestigious technical institute in the United States, you have to expect that the state will be the center of some of the most groundbreaking data science innovations in the country.
You’d be right. MIT and other hallowed universities in the Bay State regularly turn out some of the most accomplished data science students in the world, and are where some of the most cutting edge research and hottest new data startups are founded. In early 2020, for example, MIT spun-off data management firm TileDB with a $15 million funding round, launching the innovative universal data engine the company produced for new server-less Big Data process at scale.
Both startups like TileDB and more established companies drive massive demand for accomplished data scientists in Massachusetts. In the DICE 2020 Tech Job Report, Boston ranks 11th in the nation for top technology employment. Even so, the 2018 LinkedIn WorkForce Report found that one of the top skill shortages in the city was in data science.
The demand isn’t just with tiny startups, though; almost every industry can benefit from big data, and this is particularly true in two of Massachusetts’ largest economic sectors: healthcare and finance.
In the healthcare sector at Massachusetts General Hospital, data scientists can contribute to improving patient outcomes and operational efficiency by interpreting data and communicating findings to management in a way that is clear and actionable. In the banking and finance sector, data scientists at places like John Hancock Financial or Liberty Mutual in Boston develop large data models related to everything from credit valuation adjustments for derivative products, to where to set interest rates based on the likelihood of default.
No matter which direction you decide to go, a master’s in data science is the right tool to help you get there.
Preparing to Earn a Master’s Degree in Data Science
Students preparing for a master’s degree program, and ultimately a career in data science will start shaping their education during their undergraduate years in college. Nor will you stop there… data science moves fast, and constantly staying on top of the latest developments in the field is part of what will make you attractive to master’s admissions committees.
On top of the proper education and professional experience, applicants can boost their chances of acceptance in data science graduate programs by preparing for success on GRE and GMAT exams and filling gaps in functional knowledge through massive open online courses (MOOCs), bridge courses or a bootcamp.
Undergraduate Degree and Master’s Prerequisite Courses
Data science graduate programs recruit students from academic backgrounds weighing heavily in quantitative reasoning. Those qualities are judged by factors like:
- Ideally students should have a bachelor’s degree in a quantitative field like statistics, applied math, computer science, or engineering
- Academic history should demonstrate courses in disciplines like statistics, calculus I and II, programming languages, quantitative methods, and linear algebra
- Applicants to graduate data science programs should have a minimum GPA of 3.0
Relevant Personal and Work Experience for Admissions
Many graduate programs only consider applicants who already have proven relevant professional experience:
- Minimum of five years technical work experience, especially experience that demonstrates quantitative skills
- Personal experience related to statistics, programming, database administration, mathematics, and data mining
Some examples of qualifying work experience in Massachusetts could include:
- Analyzing patient outcomes and medical data from either of Massachusetts’ two largest employers – Brigham & Women’s Hospital or Massachusetts General Hospital
- Helping to code an analysis program for a financial institution like John Hancock Financial or Liberty Mutual
- Providing cyber security for Boston University
- Work with web analytics, marketing research, or demand indicators for a startup sponsored by the MassChallenge incubator
As most graduate applications require letters of recommendation, work experience can also serve to establish the contacts necessary to fulfill this important requirement.
How to Score Well on GRE and GMAT Exams
Scoring in the 85th percentile or above on one or both of these exams is a good way to stand out from the crowd and prove concrete skills. Admissions counselors pay particular attention to a student’s score in the quantitative reasoning sections. Prospective students can prepare for an exam by working problems from a number of study guides.
GRE –The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section covers the following topics:
- 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
- Arithmetic including integers, factorization, exponents, and roots
- Algebra, such as algebraic expressions, functions, linear equations, quadratic equations, and graphing
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 assesses students’ skills in data analysis. The quantitative portion is comprised of 37 questions to be completed in 75 minutes, and represents one-quarter of the entire GMAT. Questions from the quantitative portion pertain to data sufficiency and problem solving.
Joining a Data Science Bootcamp to Prepare For Master’s Program Applications and Acquire Hands-On Skills
To get the kind of skills that you will need to earn your spot in a top-ranked master’s degree program in data science, you’re going to have to put in some extracurricular work. There are many candidates and not many slots in the best data science master’s programs, so you need to come up with a way to stand out against the field.
An online data science bootcamp is one way to give yourself the kind of knowledge and direct experience that master’s admission’s committees crave. And in Massachusetts, you have a number of good online options, offered by first-rate data science departments at nationally-recognized universities:
- Columbia Engineering Data Analytics Boot Camp
- Georgia Tech Data Science and Analytics Boot Camp
- Penn Data Analysis and Visualization Boot Camp
It’s not just colleges that offer bootcamps, although they often have the greatest resources and some of the most experienced instructors. Many private organizations run them as well, aimed at every level of professional, from the inexperienced entry-level prospect to experts who have been in the industry for years.
Naturally, you’ll be looking for something on the entry end of the scale, like those three university programs. They will have lower entry requirements, revolving around high school diplomas or GEDs, but also offer the sort of introductory training that you will need if you are new to the industry. That will typically include courses in:
- SQL and SQL databases like MySQL and Postgresql
- Big Data stores like Hadoop and Big Data analysis
- Advanced statistical training
- Programming in popular stats languages like Python and R
- Familiarity with data visualization tools like Tableau
All of it is delivered through a cohort-based program that uses a series of projects rooted in real-world challenges and typically conducted with live data, just exactly as you would see if you were working in a job in the field. With instructors pulled directly from the front lines and a curriculum that is constantly adjusted to match the latest developments, you know you are getting a highly relevant education in data science—and master’s programs will recognize the same thing.
While many bootcamps are hardcore, full-time efforts that will demand your constant and full attention, the three programs noted above are all part-time, stretching the same content out over evening and weekend courses to allow working professionals an option to get up to speed.
Like other programs, they come with a full range of career services that will help you boost your resume and portfolio, and prepare for interviews either directly with employers, or with master’s admissions committees.
Filing Gaps in Functional Knowledge Through MOOCs and Bridge Courses
MOOCs can be an important source of supplemental information for students who want to gain introductory knowledge or hone their skills in a particular area of focus, such as programming prior to applying to a master’s program. MOOCs are a self-study approach to proactively preparing to enroll in a master’s program in data science. With thousands to choose from and a flexible online format, you can tailor your studies to exactly the gaps you need to fill in your CV.
Many graduate schools provide data science students with pre-master’s courses, called Bridge courses, that fill in knowledge gaps in areas vital to success in graduate-level coursework. For example, students coming from an undergraduate background in engineering could attend bridge programs that relate to key programming languages. Bridge courses are available to students that have met all entrance requirements and have been accepted to a master’s program. These courses typically take about 15 weeks to complete before transitioning to graduate-level coursework.
Bridge programs can be a necessity for students who have an undergraduate degree in a field that is unrelated to quantitative reasoning.
Fundamental bridge programs:
- Analysis of algorithms
- Linear algebra
- Data structures
Programming bridge programs:
Earning a Master’s Degree in Data Science
Colleges and universities throughout Massachusetts and the nation are working hard to add relevant majors and classes to create graduate degree programs in this field. Prospective students will find the following programs are available at campus locations in Massachusetts:
- Worcester – Master’s of Science (MS) in Data Science
- Dartmouth – Master’s of Science (MS) in Data Science
- Amherst – Master’s in Computer Science with a Concentration in Data Science
- Cambridge – Data Science Certificate
- Boston – Certificate in Data Science
Prospective students also have the option of completing their master’s degree in data science online. These programs are gaining in popularity as students find the scheduling options in online programs to be most suitable while maintaining a career. Options available online to residents of Massachusetts include:
- Master of Science in Data Science (MSDS)
- Online Certificate in Data Science
- Data Mining and Applications Graduate Certificate
Data science programs are generally comprised of around 30 semester credits. Online programs in particular can offer very accommodating scheduling options:
- 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
- Graduate certificate programs can generally be completed in one to two semesters.
Core Curriculum and Immersion
The core-curriculum subjects covered by a master’s-level data science program include:
- Data research design and applications
- Applied regression and time series analysis
- Data storage and retrieval
- Information visualization
- File organization and database management
- Ethics and law for data science
- Machine learning and artificial intelligence
Toward the end of the program, students usually complete an immersion program that allows them to implement their skills in a real-life problem-solving experience, using live data on realistic problems reflecting what you will find in the field after graduation. This involves interaction with fellow students and faculty members, while company recruiters closely evaluate student performance on theory, implementation, and teamwork.
Key Competencies and Objectives
A master’s degree in data science stands as a credential that represents competency in the following areas:
- Teamwork to achieve specific goals
- Interpretation and communication of results
- Development of means for sophisticated data analyses
- Ability to conduct cluster analysis and association mining
- Ability to run an analysis of survey data
- Development of innovative research and design methods
Career Opportunities in Massachusetts for Data Scientists with Advanced Degrees
According to Massachusetts’ Executive Office of Labor and Workforce Development, six out of ten of the state’s largest employers – including the top two – are in the healthcare sector. According to research firm AMR, the big data analytics segment of the healthcare market alone will be worth more than $67 billion by 2025.
If that’s not impressive enough, consider that the other four out of those top ten employers include companies like Oracle—which builds the toolsets that big data is stored and processed with—and Raytheon, a major technology factor with huge government contracts and private sector growth opportunities. And that’s not even scratching the surface of the burgeoning startups in the state.
That kind of competition does nothing but boost wages. According to Burtch Works, data scientists in 2020 can expect a salary range of between $95,000 and $167,000 annually… and that’s before you get to management levels, which can go all the way up to $250,000.
These job listings are some of those lucrative options available in Massachusetts, and are shown as illustrative examples only and not meant to represent job offers or provide any assurance of employment.
Data Scientist at Pixability Headquarters in Boston
- Digital video advertising company
- Duties involve developing algorithms and analytics scripts, building large data sets, and developing models
- Applicants can qualify with a master’s degree in data science, five years of work experience at a software, marketing, or research company, plus two years of experience with related software
Data Scientist with McKinsey & Company in Waltham
- Global management and consulting company
- Duties include developing models and building solutions to improve the performance of insurers through a combination of software development and advanced analytics
- Applicants must have an excellent academic track record of success and at least two years of work experience in advanced analytics
Machine Learning Engineer with Spotify in Boston
- Media streaming company
- Duties include prototyping new algorithms, evaluating small-scale experiments, and applying machine and deep learning to massive data sets
- Applicants must have at least a master’s degree in machine learning or a related field, and have a strong mathematical background