South Carolina universities are scrambling to keep up with demand for highly-educated data science professors just as their counterparts in other states are doing, but for the University of South Carolina, that comes with a twist: the university is leveraging big data to improve its own understanding of the student population at the same time it’s working to educate them on the tools and processes of data science.
The university inked a deal in July of 2020 with tech-services company HelioCampus to put in place a data infrastructure for users to access, visualize, and understand data stored and generated from across the student lifecycle. Extending across student services, financial, and human resources areas, the platform will enable exactly the kind of cross-store business intelligence, data access, analytics, and reporting that data science is bringing to every other major industry.
Those industries can be found all across the state of South Carolina in both public and private sectors. Data scientists with Teradata Corporation in Columbia work with a variety of companies and industries, including predicting failures in everything from hard drives for Dell computer to MRI machines for General Electric.
But it isn’t just big university systems and multinationals that are building out their data science ranks. One startup software company, BoomTown in Charleston, also employs data scientists to manage data exchange schemes, import real estate listing data, and optimize software and web-based resources to help real estate professionals market their services.
It’s all fueling a boom, and one so big it has online job search engine DICE proclaiming how data science positions now make up two of the top three fastest growing job categories in the entire tech industry as of 2020. That demand, and the high starting salary offers that come with it, are sending many prospective data scientists on a quest to pick up the ultimate assurance of a high-paying job in the industry: a data science master’s degree.
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, and there’s no better place to do that than right here. South Carolina boasts the first school in the country that offers an undergraduate degree in data science. While earning a bachelor’s in data science is not a strict requirement for getting into a master’s degree in the field, future South Carolina data scientists should at a minimum:
- Enroll in relevant undergraduate courses in science, computer and program, and statistics and mathematics
- Choose work experiences that will lend themselves to data science, such as those involving computer programming, quantitative skills, or database administration
- Study for the GMAT or GRE, with emphasis on preparation for the quantitative portions of the exams
- Fill in any knowledge gaps independently with a bootcamp or other similar option
Undergraduate Degree and Master’s Prerequisite Coursework
Graduate schools in South Carolina offering data science master’s pogroms typically look 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 should have completed specific work and personal experience benchmarks with practical applications in the field. For instance, you should try to meet goals that include:
- A number of years of experience working in a technical field utilizing quantitative skillsor have significant personal experience using quantitative skills, like coding, database administration, or statistical or analytics work
- Recommendation letters from persons who are familiar with your academic, work or personal experience
The kind of entry-level work experiences in South Carolina that can help bachelor’s-educated professionals gain admission to graduate-level data science programs include:
- Documentation Coding Specialist at Cigna HealthCare in Greenville
- Entry-Level Java Programmer Analyst at technology company CSC in Blythewood
- Entry-Level Machine Learning Risk Statistician at All-In Analytics in Spartanburg
Scoring Within the 85th Percentile on the GRE and GMAT Examinations
Unless you qualify for a waiver, you’ll probably need to score in the 85th percentile or higher on the quantitative sections of the GRE or GMAT examinations in order to gain admittance to a master’s program in data science.
GRE – The Graduate Record Exam (GRE) revised general exam’s quantitative reasoning section tests knowledge in:
- 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
Online Data Science Bootcamps to Get You Job-Ready or to Prepare for a Master’s Program
Of course, it could be that you didn’t pick up a bachelor’s in the right area and didn’t get the kind of work experience that master’s admissions teams are looking for and don’t have the skills to pass the GRE. You might be feeling like you are up a bit of a crick at this point, but you actually have a relatively inexpensive, quick option to correct those deficiencies: enrolling in a data science bootcamp.
No, it’s not quite the same as enlisting in the military, but some mornings you might think that was the easier option. That’s because a bootcamp pushes hard to cram all the relevant information and practical skills you need into a multi-week or multi-month program, investigating complex and cutting edge subjects like:
- Quantitative analysis
- Advanced algorithms and dedicated analytics libraries like Matplotlib and Numpy
- Teaching programming skills in R and Python
- Developing basic competence in data visualization techniques and tools like Tableau
- Exploring AI and machine learning
- Using Big Data stores like Hadoop and understanding NoSQL data concepts
A cohort-based, project-oriented approach means that you’ll be doing all this together with your fellow students on a fast-paced basis that uses real-world data to explore the same types of solutions that you would be asked to perform in a real data science job. And, fresh from some of those very jobs, your instructors will make sure you are learning the latest and greatest techniques that are being pioneered in the industry today.
These programs used to be almost entirely offered in person and on a full-time basis, but now you can find online programs through major universities available to grad students in South Carolina on a part-time basis. Options include:
- Columbia Engineering Data Analytics Boot Camp
- Georgia Tech Data Science and Analytics Boot Camp
- Penn Data Analysis and Visualization Boot Camp
Not all bootcamps are offered through major universities like these ones, but these entry-level programs are backed up by professional educators and the big-time resources that come with attending a big name institution. That means things like having access to career services teams that will help you build your project portfolios and interviewing skills for the best chance of landing a job or getting into a master’s program.
Unusually, they are also delivered part-time, taking six months of evenings and weekends to complete, but that options up options for working professionals to fit them in and prepare for a successful transition to a job or a master’s program.
Filling Gaps in Functional Knowledge By Means Of Bridge Courses or MOOCs
A full-on bootcamp is overkill for some students, however, who may only have a handful of gaps in their knowledge that need to be plugged before applying. For those students, either bridge courses or MOOCs can make a lot of sense.
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 offer you the flexibility to study on your own schedule and the opportunity to put together your own tailored curriculum to address any skills gaps you feel you have.
Earning a Master’s Degree in Data Science in South Carolina
Online data science master’s programs offer a lot of options to South Carolinians who want to get into the field without relocating or facing the challenge of full-time coursework while holding down a regular job. 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 can vary dramatically based on its scheduling structure:
- 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, with part-time options being common. 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 part-time, 36 months to complete a full time program, while accelerated programs can 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.
Core Coursework, Internship, and Immersion Experience
Core coursework offered in a graduate-level data science degree program typically include the following topics:
- Database management and file organization
- Applied time series and regression analysis
- Quantifying materials
- Network and data security
- Law and ethics in data science
- Data storage and retrieval
- Data visualization
- Artificial intelligence and machine learning
- Data research design and applications
- Data mining
Additionally, students must complete a graduate internship, which is an unpaid work experience with an area firm. Professors and employers will evaluate the students’ skills and competencies in these settings. Traditional and online data science graduate programs may offer such internships all across the state of South Carolina.
Many will also require students to participate in an immersion experience. Here, students will work as part of a group on a case study involving a particular topic. Collaboration and innovation is emphasized, and professors and professionals will evaluate student performance.
Key Competencies and Program Objectives
At graduation, you should be able to confidently:
- Ask relevant questions
- Understand ethics, legal responsibility and data security considerations
- Retrieve pertinent data
- Interpret results and communicate findings
- Use statistical techniques to collect and analyze data
- Demonstrate 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 data science are expected to grow by 11.5 million positions across the country in the ten-year period leading up to 2026. And not only will there be a lot of new jobs popping up in the field, in South Carolina and elsewhere, but they will also be extremely well-paying… Robert Half’s 2020 Technology Salary Guide puts starting salary offers for data science professionals in Charleston at over $100,000, and estimates that those in the top levels of the profession, with advanced degrees and experience, are offered more than $171,000.
Here are a few such positions pulled from the listings for data science professionals in South Carolina. These are examples only and do not represent job offers or an assurance of employment.
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 effective 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 a 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 advanced analytics to improve the manufacturing process. Continental, an automotive technology company, employs data scientists to improve the performance of load processes by optimizing ETL process design; coordinate with global operations and support team in the interface system and manufacturing; and support 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.