Online Master's in Data Science for Jobs in Massachusetts

At a 2015 big data conference hosted in Boston, virtually all the panelists spoke about the lack of qualified data scientists. Some chimed that the time for companies to hire data scientists was yesterday. At that conference, Fortune spoke with the CEO of Cambridge data analytics startup Nutonian, who opined that the sooner companies start hiring data scientists, the better.

As a newly established field, the number of data scientists throughout the nation is particularly small, which makes the University of Massachusetts’ report that there will be 120,000 big data jobs by 2018 all the more significant.

The Worcester Polytechnic Institute named data scientist the hottest job in 2016. 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 improve patient outcomes and operational efficiency by interpreting data and explaining large-scale analyses to management, who are then able to respond by developing new policies and protocols.

In the banking and finance sector, data scientists at places like John Hancock Financial or Liberty Mutual in Boston can increase revenues by developing large data models related to credit valuation adjustment, interest rates, and defaults.

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 can start by shaping their education during their undergraduate years in college. Education can be further augmented prior to beginning a master’s program. Developing relevant work experience is also critical.

On top of the proper education and professional experience, applicants can prepare to enroll 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) or by completing bridge courses.

Undergraduate Degree and Master’s Prerequisite Courses

Data science graduate programs recruit students from academic backgrounds weighing heavily in quantitative reasoning:

  • 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, coding, hacking, 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

Students can prepare for the quantitative reasoning section by reviewing Educational Testing Service’s (ETS) Math Review. The Princeton Review and Veritas Prep also provide free GRE resources.

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 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.

Practice GMAT practice exams can be found through the Princeton Review and Veritas Prep.

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 can be seen as a self-study approach to proactively preparing to enroll in a master’s program in data science.

Many graduate schools provide data science students with pre-master’s 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:

  • JAVA
  • C++
  • Python
  • R

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 paces 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:

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

Upon acquiring an advanced level of knowledge, students complete an immersion program that allows them to implement their skills in a real-life problem-solving immersion experience. 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 use programming languages such as SAS, Python, GitHub, and Shiny by Rstudio
  • Ability to conduct database queries
  • Familiarity with cyphers, hash algorithms, and secure communications protocols
  • 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, eight out of 13 of the state’s largest employers – including the top two – are in the healthcare sector. According to a 2011 study released by the worldwide management consulting agency McKinsey and Company, data scientists could generate more than $300 billion in the healthcare sector alone.

These revelations have led to a strong increase in demand for data scientists in other data-heavy industry employers that are significant to Massachusetts’ economy, including EMC Corporation, ABM Industries, and companies in the finance industry like John Hancock Financial and Liberty Mutual Group.

These 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 Massachusetts, completed in February 2016:

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

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