It’s pretty uncommon anymore to drive through a spot where your cell phone can’t get a signal, unless you’re driving around near Green Bank, West Virginia where there’s a 13,000 square mile dead spot that was created entirely by design.
Situated in the dead center of that zone, the Robert C. Byrd Green Bank Telescope is busy sniffing out some of the faintest radio signals generated in the cosmos. Any stray radio frequency broadcast—including even low-powered cellular devices—could interfere with those sensitive observations, but it seems like a reasonable tradeoff for gaining insights into the mysteries of the universe.
But researchers at Green Bank are pushing the limits in science not only by reaching into deep space, but also closer to home, in the field of information technology. Radio signals can incorporate a great deal of information, particularly when scooped up by a device as sensitive as the Big Byrd. According to the Pulsar Search Collaboratory, a partnership between Green Bank and West Virginia University, just 2.5 minutes worth of observations generates 3 gigabytes of data. That data has to be stored, parsed, and analyzed by radio astronomers who have to double as data scientists. Depending on who you ask, they could be the best data science jobs in West Virginia, but they certainly aren’t the only ones.
Those jobs aren’t all in fundamental science, either. Situated in Clarksburg is a Lockheed Martin Aeronautics manufacturing facility. The local factory manufactures almost 10% of the subassembly and spare parts for the 300 C-130J Super Hercules military transport planes in existence. This part of the manufacturing process is ripe for the application of data science and analytics. With thousands of moving parts being brought together, data scientists have found that bringing together parts manufacturing and suppliers improves the overall production process and extends the lifecycle of each C-130J Super Hercules.
Big data analytics reaches into a wide swath of American industries, and every last one of them has come to rely on the master’s-educated soothsayers of data science.
Preparing for a Master’s Degree in Data Science
Starting a career as a data scientist means having the proper education and work experience. A typical data scientist is able to display proficiency with math, computer science, and engineering skills. The path to proficiency includes earning a relevant undergraduate degree, achieving high scores on the GRE or GMAT exam, and filling any gaps that may exist in functional knowledge as necessary.
Undergraduate Degree and Master’s Prerequisite Courses
Data scientists build a foundation for graduate studies through an undergraduate degree in a quantitative field and a high GPA:
- A minimum GPA of 3.0
- A degree in quantitative fields like computer science, engineering, and statistics
- Specific classes such as statistics, linear algebra, quantitative methods, and programming languages
Relevant Work Experience
Data science graduate programs also look for relevant and extensive work experience. After finishing an undergraduate degree, a prospective data scientist would typically work in a technical field for a handful of years, hopefully garnering some strong letters of recommendation along the way. The technical work needs to cover specific applications of technical knowledge, such as programming languages, database administration, and general data analysis.
A few examples of positions with local employers that would provide relevant work experience include:
- IBM in Rocket Center, West Virginia, as a database administrator
- The State of West Virginia as a Programmer Analyst
- Accenture Federal Services as a Systems Administrator
Passing the GRE/GMAT Exams
Your work history could very well qualify you for a waiver that would allow you to sidestep entrance exams entirely, but if not, you’ll need to perform well on the exam to get through the admissions process.
The ideal score on the quantitative sections of the GRE or GMAT for a data science graduate program applicant falls within the top 85th percentile. To reach that score, it’s important to fully understand the best options for preparing for the exam, as well as how the exam will look.
The Graduate Record Exam (GRE) general test quantitative reasoning section evaluates the following:
- Arithmetic topics including factorization, roots, integers, and exponents
- Data analysis including standard deviation, permutations, probabilities, interquartile range, and statistics
- Algebraic topics including functions, linear and quadratic equations, graphing, and algebraic functions
- Geometry topics including the properties of polygons, quadrilaterals, triangles, and circles, and the Pythagorean theorem
The best way to prepare for the test is by practicing it. The Educational Testing Service provides two free sample tests, and the Princeton Review also offers a practice exam.
Another common test option is the Graduate Management Admissions Test, or GMAT. The test has a quantitative section designed to test data analysis skills. There are 37 questions, and it must be completed in 75 minutes. Practice exams are offered by Princeton Review and Veritas Prep.
Online Data Science Bootcamps to Get You Job-Ready or to Prepare for a Master’s Program
If, for whatever reason, your undergraduate and work history did not quite position you for a successful application to a data science graduate program, you still have options. If you are lacking both the fundamental knowledge and the basic hands-on skills that master’s admissions committees look for, then you might consider enrolling in a data science boot camp.
That’s because bootcamps blend critical analytical skills and quantitative techniques with a hands-on application of those processes using cutting-edge tools that are broadly used in the field today. In a fast-paced, cohort-built program, instructors who are fresh from the industry guide you through a series of projects that use real-world data addressing realistic problem sets to absorb those lessons by building genuine solutions. Those efforts will take you through concepts and tools like:
- Artificial intelligence and machine learning
- Hadoop and NoSQL Big Data storage
- Python and JavaScript programming
- SQL Databases
- Data visualization principles and tools like D3.js
At the end of those programs, you will often find a dedicated career services team waiting to help you burnish your CV or line up interviews with potential employers. The same process can get you prepped for master’s program applications and interviews, however.
Filling in Gaps in Functional Knowledge with MOOCs and Bridge Courses
Of course, you may not need the intensive approach of a bootcamp if you only have a few minor areas to brush up on. There are two options for filling gaps in functional knowledge in preparation for data science coursework at the graduate level.
First, MOOCs, or Massive Open Online Courses, are typically offered in areas like programming and the quantitative disciplines of statistics and mathematics. MOOCs can be very useful for data science graduate program applicants even if they have the necessary experience, as they are a great way to refresh prior experience and knowledge.
Second are bridge programs typically offered by colleges and universities to fill in gaps in functional knowledge required before beginning graduate level coursework for students that have met all other requirements for admittance into a data science graduate program. Bridge programs are also offered in the quantitative fields and programming languages, including data structures, algebra, Java and Python coding, and algorithm analysis.
Earning a Master’s Degree in Data Science
By completing a master’s program in data science, students can enter into one of the fastest growing fields in the nation. Online programs lend flexibility to a high quality education, as well as showing employers a willingness to further educational experiences. Such online programs include:
- Master of Science in Data Science (MSDS)
- Master of Information and Data Science (MIDS)
- Graduate Certificate in Data Science
- Data Mining and Applications Graduate Certificate
The typical master’s degree in data science requires completion of 30 to 40 credits in a full-time, part-time, or accelerated program. Full-time programs take 18 months, while part-time programs can go as long as 32 months. For the more eager student, the accelerated option can be completed in as little as 12 months.
As a part of the credit load, no matter the speed of the program, students will need to attend an immersion portion of the program, a three to four day visit to campus to meet with professors and students in real time. These immersion experiences offer networking opportunities, as well as collaborative learning opportunities and face time with professors.
Core Curriculum and Coursework
A student of a master’s level data science program should expect the following topics to be covered:
- Machine learning
- Information visualization
- Statistical sampling
- File organization and database management
- Data storage and retrieval
- Data law, ethics, and privacy
- Big data analytics
- Data research design and applications
- Applied regression and time series analysis
- Experiments and casual inference
After taking these classes, a data science student should be competent in the following areas:
- Display familiarity with hash algorithms, cyphers, and secure communications protocols
- Responsibly solve issues of business ethics relating to intellectual property, integrity, privacy, and data security
- Analyze business problems and develop innovate solutions with data analysis
- Use good ethical practices to make business and data management decisions
- Show knowledge of the techniques of statistical data analysis in making business decisions
- Design data visualizations to effectively display analysis and predictions.
- Solve problems in the real world through the use of data mining software
Career Opportunities for Data Scientists in West Virginia with Advanced Degrees
In West Virginia, data scientists can work in some of the state’s biggest industries, from aeronautics, to media and telecommunications, to government. They can also expect to be well-compensated for their efforts. According to Robert Half’s 2020 Technology Salary Guide, data scientists in Charleston are getting starting offers of between $88,000 and $150,000 … with the high end of that range representing the best trained and most experienced scientists. You can bet they have a master’s degree under their belts at the very least.
Here is a sampling of the kinds of jobs a data scientist with an advanced degree can apply for in West Virginia. 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 the Neuroscience Institute – Morgantown, West Virginia –Requires a master’s degree in a statistics, or computer science-related field or an equivalent combination of education and experience
- Work independently and/or with a research and development team to carry out data analysis on large data sets;
- Create visualizations;
- Develop and validate models;
- Generate reports for research protocols associated with clinical, population health, and human performance.
- Data mining to select features in order to build and optimize classifiers using machine-learning techniques to be used for models and analytics reports.
Senior Programmer and Data Analyst at the West Virginia Higher Education Policy Commission – Charleston, West Virginia – Requires a master’s degree in Higher Education Administration, Computer Science, Information Systems, or related field and at least five years of recent relevant work experience
- Facilitates submission, edit checking, warehousing, and reporting on postsecondary institutional data submissions to the Commission and Council
- Works as part of a development team to maintain, enhance, and support the development of web-based computer applications
- Provides institutional and user support related to institutional data submission and other web-based computer applications
- Develops and maintains applications used by research staff to access and analyze institutional data for statutory and ad hoc reporting.
- Develops documentation of information system processes; and applies appropriate data security and role-based data access protocols.
- Assists in the development of system requirements and specifications.
- Conducts independent, high-level data analysis in support of the development of annual statutory reports