Situated in Clarksburg, West Virginia, is a Lockheed Martin Aeronautics manufacturing space. The local factory manufactures almost 10 percent of the subassembly and spare parts for the C-130J Super Hercules military transport plane, of which there are about 300 in the world. 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 the manufacturing of parts and the suppliers improves the overall production process and the lifecycle of each C-130J Super Hercules.
About an hour and a half away from Lockheed Martin is a small company called iCube. iCube offers data science outsource services for companies that may not be in a position to hire their own data scientist. iCube’s data systems cover two key points, Business Intelligence and Knowledge Management. Business Intelligence is a digital process of analyzing data and presenting strong, actionable information to the analyst and scientist. Knowledge Management is similar, connecting previously unconnected data sets together to derive new insights and innovate solutions. In other words, iCube is a data scientist powerhouse, serving many companies around the nation.
The quickly expanding field of data science starts with strong data analysis and reaches into the wide variety of American companies looking to improve their consumer experience, increase their profit margins, uncover scientific advancements, and expand productivity around the world.
Preparing for a Master’s Degree in Data Science
Starting a career as a data scientist means having the proper training, 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 at least five years, accompanied by strong letters of recommendation. The technical work needs to cover specific applications of technical knowledge, such as programming languages, database administration, general data analysis, data mining, etc.
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
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
Next, the Graduate Management Admissions Test, or GMAT, 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.
Filling in Gaps in Functional Knowledge with MOOCs and Bridge Courses
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 visiaulization
- Statistical sampling
- File organization and database management
- Quantifying materials
- Data storage and retrieval
- Data law, ethics, and privacy
- Big data analytics
- Scaling data
- Network and data security
- Data research design and applications
- Applied regression and time series analysis
- Experiments and casual inference
- Advanced managerial economics
After taking these classes, a data science student should be competent in the following areas:
- Express competency in programming languages including GitHub, SAS, Python, and Shiny by Rstudio
- 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
- Analyze Big Data and survey data
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, such as aeronautics, media and telecommunications, and government. 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. These examples were taken from a survey of job vacancy announcements for data scientists in West Virginia, completed in March 2016.)
Research Analyst at San José Evergreen Community College District
- This position is an entry-level position. Applicants need to have a Bachelor’s degree in mathematics or related fields and two years of field experience.
- Job specific duties include designing survey and evaluation tools, collecting and assembling statistical data across a variety of scales, and maintaining databases and data retrieval systems related to student information.
Data Scientist at iCube CSI
- iCube is a consulting service that offers data science services to local and national companies.
- Duties include deriving meaningful insights form large data and metadata sources, communicating new insights from analysis, and devising creative solutions based on analysis.
- Applicants should have experience in data and database management, a Master’s degree in Statistics or Mathematics, and five to eight years of experience in computer science or operations research.
Lead Program Scientist at Teradata
- This position is a part of Teradata’s Analytics of Things (AoT) practice, a service designed to combine data analytics and creative business solutions for clients. Teradata is also beginning cutting edge research into the Internet of Things (IoT) and the data being collected from the IoT.
- Duties include executing the AoT strategy, communicating the complexities of AoT and IoT to potential clients, and directing new areas of research and industry leadership.
- Applicants should have extensive education and work experience. An advanced education in applied mathematics and engineering is recommended, as well as 10-15 years of work experience in the science industry. Applicants that have worked in IoT related fields are shown preference.