Mountains of information that comprise big data recently inspired Clemson University in South Carolina to join the South Big Data Regional Innovation Hub, bringing researchers and industry together to tackle some of the biggest big data problems. Their efforts have been applied to health care, coastal hazards, industrial big data, materials and management, and habitat planning since joining in November 2015. Like all regional innovation hubs, the South BD Hub is funded by the National Science Foundation with $1.25 million over the next three years.
- 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
Researchers at the University of South Carolina note that big data can be defined as both an action and an object. As an action, or a process, big data is the act of collecting quickly generated, massive sets of varied data for analysis. As an object, they say, big data is any type of data generated in large quantities at rapid rates, with varying degrees of truth or accuracy. The volume, variety, veracity and velocity of big data present all sorts of issues for South Carolina’s data scientists, as well as data scientists worldwide, having to do with its management, storage and processing.
Opportunities for working in big data abound in South Carolina for masters-educated data scientists. Data scientists with Teradata Corporation in Columbia work with a variety of companies and industries, including predicting when hard drives will fail for Dell computer company; and determining the likelihood of General Electric Healthcare’s MRI machines failing.
Startup software company BoomTown in Charleston also employs data scientists. Here, they specialize in web and software materials to help real estate professionals market their services. Data scientists and analysts manage data exchange schemes and work on the importing of real estate listing data.
Data scientists at AECOM in Greenville and Columbia help industry professionals in a variety of areas solve complex problems. These include, but are not limited to, data problems posed in the construction of One World Trade Center in New York City; helping to create innovative technical educational solutions for schools and universities; and helping major cities around the world with solutions to develop clean drinking water.
Preparing for a Master’s Degree in Data Science in South Carolina
It’s never too early to plan for a master’s degree in data science. In fact, the best time to begin preparations is while completing undergraduate studies. South Carolina boasts the first school in the country that offers an undergraduate degree in data science. While possession of this degree is not a strict requirement for those planning to pursue a master’s degree in data science, future South Carolina data scientists should:
- Enroll in relevant undergraduate courses in science, computers and mathematics
- Choose work experiences that will lend themselves to data science, such as those involving computer programming, hacking, quantitative skills, or database administration
- Study for the GMAT or GRE, with emphasis on preparation for the quantitative portions of the exams
- Enroll in MOOCs or bridge courses as necessary to fill in any knowledge gaps
Undergraduate Degree and Master’s Prerequisite Coursework
Masters in Data Science degree programs in South Carolina are looking for applicants who have:
- A bachelor’s degree in a field such as data science, engineering, mathematics, statistics, computer programming or computer science, with a GPA of at least 3.0
- Completion of course prerequisites that showcase quantitative skills, such as calculus I and II, statistics, probability, and computer science and programming classes
Relevant Personal History and Work Experience
In addition to the academic criteria listed above, applicants to masters in data science degree programs should have completed specific work and personal experience benchmarks; for instance:
- Five or more years of experience working in a technical field utilizing quantitative skills
- Have personal experience using quantitative skills, like coding, database administration, hacking, data mining, statistics and/or mathematics
- Possess recommendation letters from persons who are familiar with the applicant’s academic, work or personal experience
Entry-level work experiences in South Carolina that can help bachelor’s-educated professionals gain admission to graduate data science degree programs include (but are not limited to):
- Documentation Coding Specialist at Cigna Health Insurance in Greenville
- Entry-Level Java Programmer Analyst at technology company CSC in Blythewood
- Project Engineer for Bastian Solutions global material handling automation company in Chester
- Entry-Level Machine Learning Risk Statistician at All-In Analytics in Spartanburg
Scoring Within the 85th Percentile on the GRE and GMAT Examinations
It is important to score in the 85th percentile or higher on the quantitative sections of the GRE or GMAT examinations in order to gain admittance to masters in data science degree programs.
GRE – The Graduate Record Exam (GRE) revised general exam’s quantitative reasoning section tests knowledge via:
- Algebraic problems including algebraic expressions, graphing, linear equations and quadratic equations
- Arithmetic problems including factorization, integers, exponents and roots
- Geometric problems including polygons, triangles, circles and quadrilaterals
- Data analysis problems including standard deviation, statistics, tables, graphs and probabilities
Test-takers can ready themselves for the GRE by using:
- Princeton Review’s GRE Practice Exams
- Math Review administered by the Educational Testing Service (ETS)
- Kaplan Test Prep’s GRE Practice Exams
In addition, two of the GRE’s subject area tests can be helpful to those who wish to apply to graduate data science degree programs:
- Mathematics (Mathematics Test Practice Book):
- Introductory real analysis
- Probability, statistics and numerical analysis
- Discrete mathematics
- Physics (Physics Test Practice Book):
- Special relativity
- Classical mechanics
- Lab methods and specialized topics
- Statistical mechanics
- Atomic physics
- Quantum mechanics
- Optics and waves
GMAT – The Graduate Management Admission Test – the quantitative section, one of the test’s four main sections, evaluates data analysis abilities. Test-takers must complete 37 data analysis and problem solving questions in 75 minutes. Preparation aids for this test include:
- GMAT Test Prep courses offered by the Princeton Review
- GMAT Tutors and Test Prep Courses offered by Veritas Prep
Filling Gaps in Functional Knowledge By Means Of Bridge Courses or MOOCs
Bridge Courses – Graduate level data science programs may offer newly enrolled students the opportunity to fill in gaps in knowledge by taking bridge courses before beginning graduate-level coursework. These pre-master’s level courses will help to “bridge the gap” between post-undergraduate and master’s work. Majors in areas such as mathematics, for example, might have a functional knowledge gap in computer programming, and may need to take a bridge course in that area. Examples of areas in which schools usually offer such bridge courses include:
- Computer programming, especially in languages such as C++, Java and Python
- Computer science, especially in database administration and database management
- Mathematics, specifically courses in data structures, linear algebra and algorithm analysis
Massive Open Online Courses (MOOCs) — Students who realize that they have functional knowledge gaps prior to applying for admission to a master’s program in data science may choose to enroll in MOOCs. These online programs offer students courses in the form of video lectures, interactive problems, and professorial support. MOOCs that can be particularly beneficial to those pursuing a graduate degree in data science include:
- The Analytics Edge, offered by MIT
- Machine Learning, offered by Stanford University
- Databases, offered by Stanford University
Earning a Master’s Degree in Data Science in South Carolina
Online data science master’s programs can be quite beneficial to South Carolinians who wish to pursue the field, as there are few traditional programs offered within the state. Some South Carolina graduate schools offer related degrees that may lend themselves to a data science career. Examples of degrees available to bachelor’s-educated tech professionals in South Carolina include:
- Master of Science in Data Science
- Master of Science in Computer Science and Engineering
- Master of Engineering in Computer Science and Engineering
- Master of Information and Data Science
- Graduate Certificate in Data Science
The length of the graduate program chosen by a student varies:
- Traditional master’s degree programs may be from 30 to 40 credits in length and are usually completed in 24 to 36 months. Schools will typically admit students on a full- or part-time basis.
- Online graduate degree programs are also 30 to 40 credits long, but are more flexible in the time it takes to complete the program. Furthermore, students may study from anywhere in the world, making these types of programs highly accessible to all.
- It may take 18 months to complete an online graduate degree in data science if a student studies full-time
- For part-time students, online graduate degree programs may take up to 36 months to complete
- Accelerated online graduate data science programs may be completed in as little as 12 months
Another option for those pursuing graduate studies in data science is a graduate certificate. These programs usually run from 12 to 18 credits and can be completed in 12 to 18 months. Keep in mind, however, that many data science employers will hire a holder of a master’s degree in data science over one who holds merely a graduate certificate.
Core Coursework, Internship and Immersion Experience
Core coursework offered in a graduate-level data science degree program should include the following topics:
- Database management and file organization
- Applied time series and regression analysis
- Quantifying materials
- Advanced managerial economics
- Network and data security
- Law and ethics in data science
- Data storage and retrieval
- Data visualization
- Statistical sampling
- Experimental statistics
- Artificial intelligence and machine learning
- Data research design and applications
- Experiments and causal inference
- Data mining
Additionally, students must complete a graduate internship, which is an unpaid work experience in a real-life data science setting. Professors and employers will evaluate the students’ skills and competencies in these settings. Traditional and online graduate data science programs may offer such internships all across the state of South Carolina.
Many graduate data science programs will also require students to participate in an immersion experience. Here, students will work as a group on a case study involving a particular topic. Collaboration and innovation is emphasized in such a setting, using skills and competencies learned in the graduate data science program. Professors and professionals in the data science field will evaluate the student’s immersion experience.
Key Competencies and Program Objectives
Once graduated, the holder of a master’s degree in data science should be able to:
- Ask relevant questions
- Understand ethics, legal responsibility and data security
- Retrieve pertinent data
- Visualize and analyze data
- Interpret results and communicate findings
- Use statistical techniques to collect and analyze data
- Cultivate technical skills in data and network security, database management, machine learning, data mining and programming
- Be able to practically apply analytic and mathematic principles of data science
Career Opportunities for Data Scientists in South Carolina with Advanced Degrees
According to the Bureau of Labor Statistics (BLS), jobs in information technology are expected to grow by 22 percent through 2020 nationwide. In South Carolina specifically, the BLS predicts that jobs for data scientists will increase by 9.1 percent through 2022. (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 South Carolina, completed in March 2016.)
Data Scientist with Price Waterhouse Coopers in Columbia- This position involves working for one of the nation’s largest assurance, tax and advisory service companies. Responsibilities include identifying and pursing new opportunities, conducting market analysis and preparing revenue predictions, and developing new service offerings in the five key areas of benchmarking, research, analyst services, technology platforms and data services.
Applicants must have an advanced degree in a related field, as well as five years of experience in data and business analysis.
Big Data Scientist with Soteria in Charleston – This security consulting and incident response company hires big data scientists to help with their fight against computer network exploitation and attack. The position involves developing predictive security models that will adjust to real-time changes; developing and implementing effecting solutions to various environments including virtual and cloud-based; and collaborating with marketing, consulting and sales to solve user questions.
A graduate degree in data science, computer science, applied mathematics, statistics or electrical engineering is a must for this position, as is background in machine learning, statistical analysis, conceptual modeling, predictive modeling and hypothesis testing.
Senior Data Scientist/Manufacturing Intelligence Analyst with Continental in Fort Mill– This position involves using advanced analytics and proactively using information in the manufacturing process. Continental, an automotive technology company, employs data scientists to improve the performance of load processes by optimizing ETLprocess design; coordinate with global operations and support team in the interface system and manufacturing; and supporting the plant user community in advanced analytics use cases.
A graduate degree in data science or a related field is necessary, as is five or more years of related quantitative experience.