You wouldn’t be reading this page right now if you didn’t already understand that data science is a big deal in today’s workforce. Data scientists provide crucial technical services within virtually every industry in Connecticut, including finance and banking, healthcare and biotechnology, manufacturing and logistics, retail and HR, the public sector and beyond.
- SMU - Master of Science in Data Science - Bachelor's Degree Required.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
But there just aren’t enough of them to fill all those roles right now. According to LinkedIn, in 2018 there were more than 150,000 unfilled data science positions nationwide. Plenty of those are in Connecticut, at businesses like Hartford Health Care, where a team of data scientists mine anonymized hospital records of clients and treatments for data used to develop statistical analysis plans for clinical research projects.
Data scientists also find plenty to do in the financial services industry working for companies like Voya Financial with offices in Windsor. Here they draw from massive data stores to develop predictive models, as well as mine and analyze data to identify opportunities to improve business processes, and publish reports that detail their findings for company executives.
With junior level data scientists earning around $95,000 according to executive recruiting firm, Burtch Works, but team leads who manage ten or more data scientists earning salaries closer to $250,000 or more, a master’s degree could be your ticket to that higher bracket income and lifestyle.
Preparing for a Master’s Degree in Data Science in Connecticut
Admissions departments look for candidates with a strong job history and fundamental knowledge of key data science concepts. Master’s programs maintain a set of highly selective standards, and candidates must be prepared with relevant skills, prerequisite courses, scores in the 15th percentile on graduate entrance exams, and substantial relevant work experience.
Undergraduate Degree and Master’s Prerequisite Courses
Basic minimum qualifications typically include:
- Bachelor’s degree in a related field such as statistics, computer science, engineering, or applied math
- Minimum of 3.0 GPA during undergraduate studies
Applicants must complete prerequisite courses before enrolling in the program. Required perquisites typically include the following:
- Calculus I & II
- Linear algebra
Master’s program courses will require students to have a fundamental knowledge of certain concepts from undergraduate education and work experience. These concepts include:
- Data structures
- Algorithms and analysis of algorithms
- Linear algebra
Scoring Well on the Quantitative Sections of the GRE/GMAT Exams
Master’s programs generally maintain a selective requirement for applicants to score in the top 15 percent on the quantitative section of either the GRE or the GMAT. Admissions departments also look for excellent verbal and writing scores, expecting applicants to possess excellent communication skills.
The General Record Exam (GRE) exam’s quantitative section will evaluate the candidate’s abilities to analyze data and solve basic arithmetic, algebra, and geometry problems. The quantitative section also includes problems in statistics, standard deviation, tables, graphs, and probabilities. Candidates can prepare for the exam by reviewing sample questions and free practice exams on the official GRE website. Candidates may also prepare by reviewing a preparation guide for the quantitative section of the GRE.
The General Management Admissions Test (GMAT) also has a quantitative section which will evaluate the student’s ability to problem solve and analyze data. To prepare for the GMAT, candidates may download two free practice exams through the official GMAT website. In addition, students may take practice exams hosted through The Princeton Review and Veritas Prep. The GMAT measures the candidate’s ability to problem solve and analyze data through 37 quantitative questions.
Prior Relevant Work Experience
Most data science master’s programs will require students to have a handful of years of prior work experience, either in number crunching of some kind, or in programming or software development work. Data science blends the highly technical with the highly mathematical, so coming in with some familiarity with one side or the other is a big boost to your prospects.
Admissions departments generally expect to see the following:
- Strong communication skills
- Programming proficiency and prior experience with languages such as JAVA, C++, and Python
- Database administration proficiency
In Connecticut, data science grad students often have that experience as a result of working in positions similar to these:
- Working for United Technologies in East Hartford on a team of data scientists taking on challenges in machine learning, deep learning, data mining, and human computer interaction.
- An associate data scientist with financial services firm, The Hartford, working on a team supporting commercial markets through class plan development, advanced pricing, underwriting research, operations research, marketing analytics and product innovation.
- Data scientist or programmer at local government offices or local nonprofits.
Using Data Science Bootcamps Online to Prepare For Master’s Program Admissions and Acquire Hands-On Skills
Master’s programs in data science are hugely popular today, which makes them hugely difficult to get into without something more than just a basic bachelor’s degree. You’re going to need to demonstrate experience, programming ability, analytical skills, and a can-do attitude to even make it in the door… let alone graduate from these tough courses.
One option that can set you up to clear all those hurdles with flying colors is entering a data science bootcamp first. Bootcamps are every bit as tough as they sound. But there are more and more of them emerging around the country today aimed at every possible skill level, from extremely advanced analytics professionals, to undergrads coming in with very little formal schooling in the data arts.
That means they are designed to deliver an educational experience that is aimed squarely at taking someone at your current skill level, and elevating you into a candidate who can easily acquire a job in the industry or breeze through an admissions committee interview.
This all requires a lot of hard work. Traditional bootcamps operate in a rigorous, stair-step fashion, dumping heaps of information on you and your cohort, and then expecting you to put it to immediate use on applied projects that work with real-world data in the kind of applications that actual data scientists are kept busy with in the field today.
You’ll do it by absorbing and applying cutting-edge tools and techniques such as:
- Machine learning and Big Data stores like Hadoop
- Advanced analytics libraries like Numpy, Pandas, and Matplotlib
- Presentation and display tools like Bootstrap, Tableau, and Leaflet.js
And it all comes from instructors who have recent, practical experience in the industry. Curriculums are regularly updated to reflect the most current practices in the field.
While bootcamps were originally all private efforts, run by companies with no particular educational experience, more and more of these are being offered today directly by colleges. And because more of them are available online, you can find some big names that deliver courses over the Internet right to Connecticut, including:
- Columbia Engineering Data Analytics Boot Camp
- Georgia Tech Data Science and Analytics Boot Camp
- Penn Data Analysis and Visualization Boot Camp
As entry-level programs, the entry requirements to these are not as stringent as some of the more advanced bootcamps, and they are delivered on a part-time basis to fit in with your current commitments in school or at work. With more resources than the typical bootcamp, you’ll find that they have particularly well-developed career services components, which help you prepare for interviews, portfolio-building, and resume writing.
Best of all, bootcamps take only a few months at most to get through, so you can really jump start your career or advance your education without taking a lot of time… or money. With a bootcamp behind you, master’s applications get easier and job placement becomes more secure at every stage of your career.
Bridge Programs and Massive Open Online Course (MOOC) Options for Master’s Program Applicants Who Need to Fill Gaps in Functional Knowledge
If you apply for a master’s program and find yourself on the bubble, with a lot of what the admissions team is looking for but not quite the skill levels to hit the ground running in every area, you may still have options. In this case, most master’s programs offer bridge programs to accepted grad students. Bridge programs help students learn fundamental skills in an accelerated time period. They are offered in two areas, depending on where your weakness lies:
- Fundamental programs, including linear algebra, algorithms and algorithm analysis, and data structures
- Programming bridge programs, including languages such a Python, Java, and C++
Bridge courses consist of about half the course load of regular master’s classes, but students must complete the bridge course before moving forward into core coursework.
Massive Open Online Courses (MOOC) are online-hosted educational programs that offer master’s students another avenue to supplement their education. MOOCs include online problem modules, filmed lectures, and the opportunity to interact with instructors and teaching assistants. This is a more self-directed path, requiring some additional discipline and attention to detail, but it also affords you the opportunity to pick and choose exactly the coursework you feel you need, and usually to complete it on a flexible timeline of your choosing.
Earning a Master’s Degree in Data Science in Connecticut
Connecticut offers a handful of traditional in-state, on-campus master’s programs in data science. However, more and more master’s students are choosing to enroll in online programs, citing benefits such as flexibility around a professional schedule and more diverse program options. Online programs are capable of delivering the same accredited curriculum recognized by professional data science associations nationwide as you would find in a quality campus-based program.
Programs are offered through full time, part time, and accelerated tracks. Full time options can be completed in 18 months, part-time programs can be completed in 32 months, and accelerated options can be completed in as little as 12 months. Most programs, including online options, require an immersion experience for students in their last semester. The immersion experience is a group project designed to simulate real-world data mining and analysis.
Students may choose from several different degree options:
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Graduate Certificate in Data Science
- Data Mining and Applications Graduate Certificate
Curriculum Content and Core Coursework
Coursework within master’s programs will vary, but all core courses will rely on fundamental data science concepts. The following topics will be included in most core coursework:
- Data mining
- File organization and database management
- Data storage and retrieval
- Applied regression and time series analysis
- Ethics and law for data science
- Network and data security
- Visualization of data
- Experimental statistics and statistical sampling
- Machine learning and artificial intelligence
- Data research design and applications
Key Competencies and Objectives
Data science master’s programs will prepare students for the challenges of working in the field. This means building up your critical thinking and problems solving skills as much as your core technical capabilities; every data science problem is unique, and can be attacked in a variety of ways that only experience will teach you. That’s why solid data science master’s programs send you off with capabilities like:
- Communication and visualization
- Statistical sampling
- Research design
- Data mining and machine learning
- Data collection and analysis
- Data cleansing
Career Opportunities for Data Scientists in Connecticut with Advanced Degrees
In Connecticut, data scientists are indispensable to almost every industry, allowing data scientists to find work with a diverse range of employers. In a 2016 report, McKinsey’s Global Institute found that much of the promise of Big Data across the board had not yet been realized, simply due to a failure to offer sufficient data scientist staffing in various industries. All over the state, employers are screaming for qualified data scientists—and happy to pay them well.
The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
Senior Data Scientist at Pitney Bowes in Stamford
- Master’s degree or seeking master’s degree in data science or another quantitative discipline
- 7 or more years of experience in applied analytical data science role
- Experience with relational and Hadoop platforms
- Exploring external datasets and internal operational data
- Producing innovative data-driven products, services and concepts
- Managing a team of data scientists and very large data sets
Senior Data Scientist at MidState Medical Center in Meriden
- Master’s degree in data science or a related field
- 3 to 5 years of experience in using data in a medical field
- Designing and directing research projects for the medical center
- Developing statistical analysis plan for clinical research projects
- Presenting research to colleagues and supporting communication with team and staff
Director of Data Science & Analytics at Pfizer, Inc. in Groton
- Master’s degree in data science, computer science, management science, statistics, mathematics, or engineering
- 10 or more years of experience working in data science, data analytics, or related field
- Experience working with pharmaceutical science data
- Defining, designing, and building solutions for predictive analytics, data mining, and advanced business analytics capabilities
- Locating and analyzing operational and scientific external data to help Pfizer identify insights
- Delivering data analytical services to the company