Okay, let’s face it… when most people think New Hampshire, they think big wilderness, not big data. And to that point, tourism remains one of the top three sectors in the state’s economy even today. There will always be a place for ski instructors and boutique hiking boot stores in The Granite State.
But if you are not from around here, the other two of those top three sectors might surprise you. According to the Bureau of Economic Analysis, in 2020 those were healthcare and high-tech manufacturing. And both of those are sectors where data science is expected to have an enormous impact over the coming decades.
With around 19% of the state’s wages accounted for in high-tech manufacturing, according to the New Hampshire Center for Public Policy, you can already see some of the effects of high-compensation rates for data scientists in the state. According to the 2020 Robert Half Salary Guide, the median base salary for entry-level data scientists in Nashua was $142,785, with more experienced scientists making up to $205,485 per year.
These individuals work at top manufacturing companies like BAE Systems in Nashua, NH Ball Bearings in Peterborough, and Hypertherm in Hanover, but increasingly at healthcare companies like Concord Hospital and Southern New Hampshire Health as well. Allied Market Research expects the big data analytics segment of the healthcare market to grow by almost 20 percent by 2025, reaching a size of more than $67 billion.
With that kind of money floating around, you can be sure that organizations investing in data scientists are not going to skimp on salaries… or qualifications. If you want to pull down top dollar in New Hampshire data science circles, prepare to invest in at least a master’s level education.
Preparing for a Master’s Degree in Data Science in New Hampshire
Data science graduate programs are looking for students who come from a background that includes specific features: a relevant undergraduate degree, related work experience, and applicable personal experience.
Undergraduate Degree and Master’s Prerequisite Courses
Candidates looking to gain admission to a data science master’s program should earn their undergraduate education in a quantitative field like data science, applied math, computer science, statistics, or engineering. The cumulative undergraduate GPA should be at least 3.0.
Prospective students should also come from an undergraduate background that includes completion of prerequisite courses such as:
- Statistics
- Calculus I and II
- Quantitative methods
- Linear algebra
- Programming languages like Java and Python
Relevant Personal and Work Experience
Most master’s programs in graduate science require applicants to have a solid background with relevant work experience:
- Five years of technical work experience that involves managing, analyzing, or collecting data
- The work experience should demonstrate quantitative abilities in areas like coding, hacking, math, statistics, database administration, or data mining
- Analytical reasoning ability
- Personal or professional experience should demonstrate knowledge of data structures, algorithms, and analysis of algorithms
- Personal or professional experience should also demonstrate knowledge of programming languages like Python and Java
Local examples in New Hampshire of relevant work experience can include:
- Working with BAE Systems in Nashua to improve production efficiency by analyzing performance data from test results
- Working with General Electric’s meter business in Somersworth to develop programs that aggregate data from smart meters, and create models from this data which help improve resource allocation
- Working with Lonza Biologics in Portsmouth to maintain computer network access and security for biologists communicating with their colleagues at their home office in Switzerland
- Working with Osram Sylvania in Hillsboro to develop or incorporate smart lighting circuits that can report performance data to a centralized computer system
Demonstrating Basic Proficiencies on the GRE and GMAT Exams
Graduate programs in data science may require applicants to take the GRE or GMAT exams prior to admission. Schools generally look for prospective students with scores within the 85th percentile. While scoring well on the quantitative section is a must, prospective students should also score well on the verbal and writing sections since communication skills are also very important in the field of data science.
The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section 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 GRE with resources such as:
- Educational Testing Service’s (ETS) Math Review
- GRE practice exam through the Princeton Review
- GRE practice exam through Veritas Prep
The Graduate Management Admissions Test’s (GMAT) quantitative section evaluates students’ skills in data analysis, and is composed of 37 questions to be completed in 75 minutes. These questions pertain to data sufficiency and problem solving. Students can prepare for the GMAT with resources like:
- Princeton Review’s GMAT practice exam
- Veritas Prep’s GMAT practice exam
Data Science Bootcamps to Prepare For Master’s Program Applications and Acquire Hands-On Skills
Prepping for passage of the GRE or GMAT will show your fundamental knowledge, but it doesn’t demonstrate your practical skills or any data science specific qualifications. If you find yourself lacking in either of those areas, one way to boost your odds of acceptance to a master’s program might be to join a data science bootcamp.
You don’t have to get camoed up for these courses (unless you want to), but you had better bring your warrior face and a can-do attitude along with you, because these programs aren’t easy to get through. Consisting of cutting-edge training that revolves around the latest tools and techniques in the industry, they represent a crash course in data science concepts crammed into a few weeks or months of focused training.
That training usually consists of a series of hands-on projects that you will undertake with fellow students, supervised by instructors who have real-world experience in the industry. It’s learning by doing, using tools and concepts such as:
- Artificial intelligence and machine learning
- Hadoop, Spark, or other big data tools
- Structured Query Language and relational database stores like MySQL and SQL Server
- Common analytics programming languages like Python and R
- Data visualization tools ranging from custom HTML/CSS to off-the-shelf solutions like Tableau
Bridge Programs and Massive Open Online Course (MOOC) Options for Applicants that Need to Bridge Gaps in Functional Knowledge
Prospective students who fall short in just a few subjects may be eligible to enroll in a bridge program that will bring them up to speed. Bridge programs are for students who have already been admitted to a data science graduate program, and are sponsored by the graduate school’s home university, often as summer courses. Upon completing their bridge program, students will be ready to begin the data science core curriculum with their fellow classmates as first-year graduate students.
Universities usually make two types of bridge programs available to their students:
- Bridge programs in fundamental subjects, like data structures, linear algebra, algorithms, and algorithms analysis
- Bridge programs in programming languages like Python, Java, C++, and R
MOOCs (massive open online courses) are extra-curricular courses prospective students can take on their own to bolster their personal experience. These are offered in subjects like data science, statistics, mathematics, engineering, or programming languages.
MOOCs are essentially online interactive classes where students can meet, discuss sample problem sets, and watch video lectures featuring preeminent scholars. MOOCs may also include direct interaction with professors or teaching assistants to help explain and direct the online discussion. While MOOCs can improve students’ personal skills in important subjects, they are not recognized for formal academic credits applicable to your undergraduate requirements or graduate degree. However, they are more flexible than bridge programs and more easily tailored to your specific needs.
Earning a Master’s Degree in Data Science in New Hampshire
Data science programs are popping up at universities all over the country, even in little old New Hampshire. But it’s true that your selection is much improved by the expansion of such programs into the online realm, where you can enroll in courses from schools all across the country. Traditionally completed in about 30 semester credits, online graduate programs in data science can result in credentials like:
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Master of Science (MS) in Data Science
- Data Mining and Applications Graduate Certificate
- Graduate Certificate in Data Science
These programs also give students the advantage of a flexible class schedule, and may additionally offer flexible completion times:
- 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 – completion in one to two semesters
Master’s Core Curriculum
Graduate students cover core curriculum topics that include these essential subjects:
- Experimental statistics
- Data research design and applications
- File organization and database management
- Information visualization
- Statistical sampling
- Ethics and law for data science
- Data mining
- Applied regression and time series analysis
- Data storage and retrieval
- Network and data security
An immersion experience comes towards the end of the program, and features a data science project students can work on in teams to develop and demonstrate their skills. During this time, students are evaluated on their ability to work together, as well as on the outcome of their project. Prospective employers may also observe students during their immersion experience to scout talent.
Key Competencies and Objectives
Students who earn their master’s degree in data science are able to demonstrate these core competencies and apply them in the workplace:
- Ability to work in teams to achieve specific goals
- Ability to interpret and communicate results
- Ability to develop and conduct sophisticated data analyses
- Ability to conduct association mining and cluster analysis
- Ability to run an analysis of survey data
- Ability to develop innovative design and research methods
Career Opportunities in New Hampshire for Data Scientists with Advanced Degrees
As a longtime hub of manufacturing and technology, businesses across virtually all sectors of New Hampshire’s economy – from the well-established to new startups – are hungry for data scientists.
Founded in 1918, C&S Wholesale Grocers headquartered in Keene uses data scientists to efficiently plan its distribution of products throughout the country. As the largest company of its kind in the nation, having the most efficient means of distribution can have millions of dollars of impact and significantly affect the company’s bottom line, which is why this supplier requires its data scientists to have a master’s degree at minimum.
SilverCloud, a startup company based out of Portsmouth, uses data scientists to draw meaningful insights from massive amounts of customer data generated by its apps, which are used by more than 175 different banks and credit unions.
These, and many other, New Hampshire businesses will continue to drive demand for highly educated data scientists in the state. The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
End-to-End Supply Chain Data Scientist with C&S Wholesale Grocers in Keene
- With the largest wholesale grocery supply company in the US, this job involves forecasting and planning decisions that affect over 100,000 products
- Duties include developing statistical algorithms to forecast demand and contributing to modeling metrics requests
- Applicants must have at least a master’s degree in statistics, mathematics, engineering, or a related quantitative field
Data Scientist Intern with SilverCloud in Portsmouth
- With a leading developer for sales and customer service applications, this role involves analyzing consumer behavior based on their actions over the internet
- Candidates will analyze data with salesforce reports, metric generation, attribution reporting, and visualization techniques
- Applicants for this position can distinguish themselves from competition with a master’s degree in data science or another closely related quantitative field
Senior Data Scientist with CAMP Systems in Merrimack
- Working with the leading provider of aircraft compliance services to the aviation industry, this role involves working with CAMP to develop means of analyzing existing terabytes of data to develop new products
- Duties support CAMP’s executive-level product strategy, and include analysis of the company’s data assets, identification of weaknesses in the data sets, and the defining of frameworks to support product development
- Applicants must have a minimum of a bachelor’s degree in statistics, mathematics, business, or another quantitative field, and can distinguish themselves with a master’s degree in data science