Playing on the relationship between data science and the STEM fields – science, technology, engineering, and mathematics – the 2016 article, “The Art of Big Data,” published on fosters.com cleverly identified data scientists as adding in an A for artistic, resulting in STEAM. The article makes sure to distinguish data analysts from data scientists, the latter of whom rely on a broad range of skills that includes computer science, software, research, and experimentation. Data scientists make models that can be explained to management, artistically using data that was generated from newly conceptualized experiments.
- 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
The article pivots around the commentary of a New Hampshire professor in data science, who notes the high demand – a “hunger” – for data scientists among many of the state’s businesses. Market Reach, a B2B technology marketing company headquartered in Nashua, is one such company. Data mining with web and mobile applications can be used to understand customer habits and thereby produce better marketing that allows for greater client satisfaction.
New Hampshire’s top manufacturing companies like BAE Systems in Nashua, NH Ball Bearings in Peterborough, and Hypertherm in Hanover can also benefit from data scientists who develop methods of ascertaining production efficiency improvements. In fact, the management consulting firm McKinsey and Company projects that data science could boost the operating margins of retailers who sell manufactured products by 60 percent.
The professor profiled in, “The Art of Big Data,” noted that data science is usually taught at the graduate level. This is exactly the type of education students need to transition from the academic world to problem-solving as professionals in an exciting marketplace.
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, relevant 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:
- 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 demonstrating 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. Students may also volunteer to take these to improve their demonstrable skills. Schools look for students to pass within the top 15th percentile of these exams. While scoring well on the quantitative section is a must, prospective students should also score well on the verbal and writing sections as communicating is also important in 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 comprised 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:
Online Data Science Bootcamps to Get You Job-Ready or to Prepare for a Master’s Program
Start or advance your career in the booming field of data analysis. This 24-week boot camp teaches the fundamentals of Programming in Excel, Fundamental Statistics, Python, SQL, HTML/CSS, Tableau, and more. Click for more info:
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 subject areas may be eligible to enroll in a bridge program that will catch 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’s schools home university. 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 forums where students can meet, discuss sample problem sets, and watch recorded lectures featuring preeminent scholars. MOOCs may also feature professors or teaching assistants to help explain an direct the online discussion. While MOOCs can improve students’ personal skills in important subjects, they are not recognized as formal academic credit.
Earning a Master’s Degree in Data Science in New Hampshire
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
- Quantifying materials
- Scaling data – macro and micro
- Advanced managerial economics
- Applied regression and time series analysis
- Data storage and retrieval
- Network and data security
- Experiments and casual inference
- Machine learning and artificial intelligence
- Visualization of data
The immersion experience comes towards the end of the program, and features a data science project students can work on in teams, proving 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 apply of programming languages such as GitHub, SAS, Python, and Shiny by Rstudio
- Ability to conduct database queries
- Ability to use hash algorithms, cyphers, and secure communications protocols
- 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, 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 analyze massive amounts of customer data generated by its apps, which are used by more than 175 different banks and credit unions.
Graduates with a master’s degree in data science can browse the following additional examples of relevant employment in New Hampshire. Employers often list the requirement of a master’s degree in, “a quantitative field,” because many of today’s candidates did not have the option of obtaining a master’s degree in data science at the time they completed their graduate education.
(The following 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 New Hampshire, completed in March 2016):
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 incumbent is responsible for 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 incumbent analyzes 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 among their 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 incumbent will work 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