Online Master's in Data Science for Jobs in Mississippi

Through cost savings, increased efficiency and by contributing to the development of strategies that generate more profits, Mississippi’s data scientists are expected to add billions of dollars in value to the private sector, and to pubic-sector entities in the state.

Featured Programs:

  • SMU Master of Science in Data Science
    SMU's Online MS in Data Science is designed for working professionals looking to advance their careers.The program's interdisciplinary curriculum prepares data science professionals to work with large datasets, analyze information and relate findings. GRE waivers are available for experienced applicants. Learn More. - Bachelor's Degree Required.

Mississippi’s largest private employer – Nissan North America, which provides jobs for 6,300 state residents according to MSU’s College of Business – is just one company that is actively building teams of master’s-prepared data scientists. At Nissan North America-Mississippi, data scientists are developing new methods for gathering market intelligence to be used for product and corporate planning with the ultimate goal of improving sales.

With Mississippi’s auto manufacturing sector generating $1.19 billion in 2013 alone, 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. This is backed up by a 2011 report released by the management consulting firm McKinsey and Company, which found that data scientists could increase retail sales of manufactured goods by over 60 percent nationwide.

And the benefits data scientists could have on Mississippi’s manufacturing sector is just the tip of the iceberg, with comparable advantages being projected for government agencies, healthcare, and retail trade, which combined employed 557,484 and generated $32.7 billion in Mississippi in 2013 (US Department of Commerce).

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 competency for admission could include:

  • Scoring well on quantitative sections of GRE and/or GMAT exams
  • Completing massive open online courses (MOOCs) as a way to close any gaps in functional knowledge related to mathematics or programming

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 place. Personal experience and work history required for admissions may include:

  • Applicants should have at least five years of technical work experience that emphasizes quantitative skills
  • Applicants can also cite their personal experience as it relates to mathematics, statistics, coding, hacking, database administration, data mining, or programming

Candidates should note that letters of recommendation are a related admissions requirement for most graduate programs. Some examples of qualifying experience through employers found in Mississippi include:

  • Programming work with any of the 700 defense contractors concentrated along the Gulf Coast to enhance cyber security or create computer models
  • Marketing work with major local employers like Nissan North America or RPM Pizza that designs campaigns based on customer data
  • Government work that collects, aggregates, analyzes, or works with 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.

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:

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:

Closing Gaps in Functional Knowledge Through MOOCs and Bridge Courses

Massive Open Online Courses (MOOCs) – These online classes can include recorded 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. Completing these is a semi-formal way of proactively gaining 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 a field that perhaps was not covered during a student’s undergraduate education.

Bridge Courses – Graduate schools will often provide data science students that have met all enrollment criteria and that have been accepted into the program 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 a grad school’s bridge program 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

While several schools throughout Mississippi offer master’s programs related to mathematics, computer science, and engineering, none have caught up with the emerging field of big data to establish undergraduate or graduate programs in data science specifically.

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 online. These programs allow students to maintain their current employment while completing their master’s education. Online programs often enroll students who want to complete their degree at a select pace:

  • Traditional completion time – approximately 18 months or three semesters
  • Accelerated completion – completion in as little as 12 months or two semesters
  • Part-time – completion in as much as 32 months or five semesters
  • Graduate certificate programs – completion in 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
  • Experiments and casual inference
  • Machine learning and artificial intelligence
  • Information visualization
  • Quantifying materials
  • Scaling data – macro and micro
  • Advanced managerial economics
  • Experimental statistics
  • Data research design and applications
  • File organization and database management
  • Data storage and retrieval
  • Statistical sampling
  • Ethics and law for data science
  • Data mining
  • Applied regression and time series analysis
  • Visualization of data
  • Immersion

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, which can be assessed by instructors and potential employers beside 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
  • Conduct database queries
  • Be familiar with hash algorithms, cyphers, and secure communications protocols
  • Program in languages such as GitHub, SAS, Python, and Shiny by Rstudio

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 data scientists who work with military contractors to improve efficiency, improve products, and better secure lucrative contracts. The US Department of Labor reports that in 2014, compared with all other rural areas in the nation, southeast Mississippi was home to the third-highest number – and forth-highest paid cohort – of computer/information research scientists.

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. These examples were taken from a survey of job vacancy announcements for data scientists in Mississippi, completed in February 2016:

Data Scientist (Engineer) with Vencore at the Stennis Space Center – Vencore 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

Research Scientist with Valent USA in Oxford – Valent USA, a subsidiary of Sumitoma Chemical Company, is looking for a scientist to direct all research and development strategies.

  • This position would involve keeping up with state-of-the-art principles and theories in the scientific community, plan field research, manage research groups, develop research protocols for herbicide and fungicide trials, and make reports that detail the results of data analysis
  • Applicants must have at least a master’s degree in a related field

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