Through the cost savings that result from increased efficiency and by contributing to the development of strategies that add to the bottom line, Mississippi’s data scientists are expected to add billions of dollars in value to the GDP, and keep billions more in state coffers through cost savings measures.
Nissan North America is one of Mississippi’s largest private employers, providing jobs for 6,300 state residents according to MSU’s College of Business. At Nissan North America-Mississippi, data scientists are developing new methods for gathering market intelligence to be used in product development and strategic planning, streamlining production and stripping unnecessary links out of the supply chain to create a better product at a lower cost. And that’s to say nothing of what’s involved in pulling meaningful insights from mountains of data generated by R&D for the actual engineering processes the automotive industry is famous for.
But this is just one of the many companies that is actively recruiting top talent in an effort to build out teams of capable data scientists.
With Mississippi’s auto manufacturing sector generating $5.56 billion in 2016 alone according to the Clarion Ledger, the application of data science in this sector is expected to result in hundreds of millions of dollars in cost savings and increased sales each year.
With data scientists bringing that kind of value to both public and private employers across the state, demand is huge, and the supply is limited. According to the 2020 Tech Jobs Report put out by tech industry recruiting firm, DICE, the demand for big data engineers alone was the highest of any technology related profession, growing by 50% year over year.
And the salaries reflect their value as well, with the 2020 Robert Half Salary Guide showing starting offers of between $81,000 and $138,000 for data scientists in Jackson.
You can bet that individuals at the top of that range have master’s degrees or better under their belts, and if you want to join them, you’ll need one too.
Preparing to Earn a Master’s Degree in Data Science
Gaining years of relevant work experience and an undergraduate degree in a quantitative field is the most direct path to securing a place in a data science master’s program.
In addition to a relevant bachelor’s degree and work experience that demonstrates basic competencies, other means of proving your eligibility for admission could include:
- Scoring well on quantitative sections of GRE and/or GMAT exams
- Completing a bootcamp or bridge courses (MOOCs) as a way to close any gaps in functional knowledge related to mathematics or programming
- Gaining relevant work experience
Undergraduate and Master’s Prerequisite Courses
Academic skills in a quantitative field are the common denominator for data scientists, which is why graduate programs prefer or require students to come from a background that includes the following:
- A bachelor’s degree with a major in engineering, applied math, statistics, or computer science
- A course history covering disciplines like calculus I and II, statistics, programming languages, linear algebra, and quantitative methods
- A minimum GPA of 3.0
Work Experience Prerequisites for a Data Science Master’s Program
The competitive nature of master’s programs in data science often means that academic experience alone is not enough to secure a slot. Personal experience and work history required for admissions may include:
- Applicants should have a few years of technical work experience that emphasizes quantitative skills
- Applicants can also cite their personal experience as it relates to mathematics, statistics, database administration, or programming
Candidates should note that letters of recommendation are a common admissions requirement for most graduate programs. Some examples of qualifying experience through employers found in Mississippi include:
- Programming, cybersecurity or modeling work with any of the 700 defense contractors concentrated along the Gulf Coast
- Marketing work with major local employers like Nissan North America or RPM Pizza that designs campaigns based on customer data
- Government work that involves collecting, aggregating, or analyzing data related to healthcare, epidemiology, or education
Preparing for Success on the GRE/GMAT Exams
Prospective students can show they are qualified for admission to data science programs by scoring in at least the 85th percentile of the GRE and/or GMAT exams. These standardized tests are required for most graduate programs, and can be considered the lowest bar you will need to clear for entry. But unless you have enough work experience to qualify for a waiver, you will very likely need to get through them to get a spot!
GRE – The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section covers the following subjects:
- Data analysis, covering topics like statistics, standard deviation, interquartile range, tables, graphs, probabilities, permutations, and Venn diagrams
- 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
Candidates can prepare for the quantitative reasoning section by reviewing resources such as:
- Educational Testing Service’s (ETS) Math Review
- Princeton Review’s practice exam
- Veritas Prep’s practice exam
The GRE is also offered in two relevant subject tests, covering the following subjects:
Physics – physics test practice book
- Classical mechanics
- Electromagnetism
- Optics and waves
- Thermodynamics
- Statistical mechanics
- Quantum mechanics
- Atomic physics
- Special relativity
- Lab methods and specialized topics
Mathematics – mathematics test practice book
- Calculus
- Algebra
- Introductory real analysis
- Discrete mathematics
- Probability, statistics, and numerical analysis
GMAT – the Graduate Management Admissions Test’s (GMAT) quantitative section evaluates students’ knowledge regarding data analysis. As one of the four main sections of the GMAT, the quantitative portion is comprised of 37 questions that must be completed in 75 minutes. All of the questions relate to problem solving and data sufficiency.
Candidates can find GMAT practice exams offered by:
Online Data Science Bootcamps to Build Skills For Your Master’s Program or for Direct Entry into the Industry
If you didn’t happen to get into the right job after your bachelor’s program or didn’t take the kind of bachelor’s degree that would prepare you for advanced data science studies in the first place, you have another option to pick up the kind of skills and knowledge you will need: a data science bootcamp.
A bootcamp is just as hardcore as it sounds. You won’t be spoon-fed anything or walked gently into some sort of Socratic dialog to discover the finer theoretical points of data munging. No, you’ll get thrown right into the deep end, given highly directed instruction and support from experienced instructors, then assigned a practical, hands-on project to complete with your fellow students that will use real-world data and have realistic objectives. You’ll do it all with the exact same cutting-edge tools and techniques that are being used in the industry today.
That includes concepts and tools like:
- Python and R programming
- Numpy and other specialized analytics libraries
- Tools like Tableau for data analysis and presentation
- AI and machine learning concepts
- Hadoop and other big data storage and analytics tools
Different bootcamps are aimed at students with different levels of expertise and expecting different outcomes. Some high-level courses will only accept people who already have a master’s or a PhD, for example. But to prepare yourself for master’s-level studies, you should look for a more entry-level course.
It’s no accident these are all offered by prestigious, well-known universities with big-name data science programs. Clocking in at six months, they are longer than many intro bootcamps, but that’s because they are also part-time, designed to work for individuals with other commitments during the day.
That big name backing means they come with all the services the university offers, including comprehensive career guidance, with counselors and coaches helping you develop your resume and portfolio for any goal you might have after graduation, whether looking for direct employment in the industry or tailoring your master’s program application.
Closing Gaps in Functional Knowledge Through MOOCs and Bridge Courses
If you have only a few gaps in your background knowledge, you can take on a less intensive option, like MOOCs or bridge courses.
Massive Open Online Courses (MOOCs) – These online classes can include video lectures from the world’s preeminent thinkers in mathematics, physics, engineering, programming, or other fields. MOOCs also involve interactive user forums and exercises that can be completed individually or in teams, and then evaluated by student teachers or professors. MOOCs are offered in a semi-formal format, but ensure you gain proficiency in any number of areas relevant to preparing for a master’s program. MOOCs are also a great way to gain a working knowledge of subjects that simply weren’t covered during your undergraduate education.
Bridge Courses – Graduate schools will often provide recently accepted students who have met all enrollment criteria with access to bridge courses, or a series of bridge courses in more than one area. These can be thought of as an official university-sponsored crash course in a subject that was not covered much in a student’s undergraduate studies, but that is essential to proceed in a data science graduate program. For example, a student with an undergraduate degree in math or statistics may need to complete some bridge courses in programming.
Bridge programs may be offered in fundamental subjects or course series:
- Algorithms
- Linear algebra
- Data structures
They are also offered for a variety of programming languages:
- Python
- Java
- C++
- R
Enrolling in a Master’s Program in Data Science in Mississippi
Students who want the full advantage that comes with a master’s degree in data science can choose from a variety of graduate programs offered either on campus or online in Michigan. The flexibility of online programs allow students to maintain their current employment while completing their master’s education.
Online programs are offered with a number of scheduling options:
- Traditional completion time – approximately 18 months or three semesters
- Accelerated completion – completed in as little as 12 months or two semesters
- Part-time – take as long as 32 months or five semesters
- Graduate certificate programs – one to two months
Programs are comprised of around 30 semester credits in total, and result in competitive graduate credentials such as:
- Master of Science (MS) in Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Data Mining and Applications Graduate Certificate
- Graduate Certificate in Data Science
Core Curriculum and Immersion
Data science graduate students cover core curriculum subjects that include:
- Network and data security
- Machine learning and artificial intelligence
- Information visualization
- Data research design and applications
- File organization and database management
- Data storage and retrieval
- Ethics and law for data science
- Data mining
- Applied regression and time series analysis
Programs culminate in an immersion experience that involves group work on a project – a real-world application of a student’s acquired skills in data science. The immersion experience gives students the chance to demonstrate the core abilities they have developed, while being assessed by instructors and potential employers along with their ability to work and collaborate with others.
Key Competencies and Objectives
Students with a master’s degree in data science will be capable of exhibiting these core competencies and applying them in the workplace:
- Develop innovative design and research methods
- Run an analysis of survey data
- Conduct association mining and cluster analysis
- Work in teams to achieve specific goals
- Interpret and communicate results
- Develop and conduct sophisticated data analyses
Career Opportunities in Mississippi for Data Scientists with Advanced Degrees
Mississippi’s Gulf Coast region is one of the hottest places in the nation for defense contractors, and specifically for defense contractors working with big data… both the General Dynamics Information Technology division and Northrop Grumman’s Electronic Systems division have major facilities there.
That means plenty of opportunities for data scientists with the right qualifications. According to QuantHub in 2020, 67% of employers are actively expanding their data science teams, and the job ranks third overall for the number of new positions being created. Those shortages aren’t going away anytime soon, with an estimated global tech talent shortage of 85 million by 2030. To attract candidates with the right kind of advanced education, many employers specify the requirement for a master’s degree in a quantitative field.
The following job listings are presented as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
Data Scientist (Engineer) at the Stennis Space Center – Working at a NASA contractor, the scientist in this position provides information, engineering, and analytical services as a government and civilian contractor.
- Job duties include examination of large amounts of data, formulation of statistical models, and the development of scalable data systems
- Applicants must have at least a bachelor’s degree in a mathematics-intensive discipline, computer science, or engineering, and can compete more strongly with a relevant master’s degree; candidates must also be able to obtain a “secret” clearance level
Data Scientist with Maden Technologies in Vicksburg – As a leading integrator of outsourced IT solutions, Maden Technologies needs data scientists with experience in quantitative research methods.
- Duties include the development of computational software and computational analysis research
- A bachelor’s degree in computer science, computer engineering, or a related discipline is required; applicants can compete on a higher level with a master’s degree in data science; the ability to obtain a security clearance of “secret” is also required