News from the Kansas City Star in 2019 that Google is considering investing up to $25 billion in data center expansions in the city over the next few years is a big deal on the local front, but just a drop in the bucket compared to the overall impact of data science across the state. From the state’s largest employers like Ascension Health and O’Reilly Automative to its niche innovators like MeterGenius, a smart-energy startup, data scientists are in demand to increase productivity, efficiency, customer satisfaction, and sales across virtually all sectors.
- 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
But data science is also a field that is drawing increasing scrutiny from privacy and legal angles, as St. Louis insurer Essence Holdings found in 2019. A big data success story, Essence used advanced analytics to administer more than 60,000 Medicare Advantage plans in the region, growing to a $1.64 billion valuation… until Medicare and DHHS audits found that the company couldn’t substantiate fees charged to dozens of patients under their plans; likely the result of someone simply forgetting to run a regression analysis or double-check an algorithm along the way. The honest mistake still resulted in a black eye for the company and proved to be an important lesson in how the complexities of data science and the power of the analysis it offers needs to be wielded carefully.
That’s where well-trained data scientists come in. With a master’s degree in the field, you’ll have the combination of training in statistical science and ethical considerations in your portfolio to leverage all the benefits of big data, while guarding against accidents and abuses.
Preparing for a Master’s Degree in Data Science in Missouri
And developing that combination of skills at the graduate level starts with some extra preparation during your undergraduate years. Data science graduate programs prefer to recruit students who come from a background that includes relevant academic and professional experience.
Beyond those qualifications, applicants may also need to demonstrate their abilities and close gaps in functional knowledge through the following paths:
- Massive open online courses (MOOCs), data science bootcamps, or bridge courses to close gaps in functional knowledge related to programming and math
- High scores on quantitative sections of the GRE and/or GMAT exams
Undergraduate Degree and Master’s Prerequisites
Data science involves a lot of deep theoretical understanding of information and statistical theory to get it right. Your high school algebra courses aren’t going to cut it. To ensure the success of the students they recruit, data science graduate programs pay close attention to the academic background of their recruits. This means:
- An undergraduate degree in a quantitative field like statistics, applied math, computer science, or engineering
- A course history that includes coverage of key disciplines like statistics, calculus I and II, quantitative methods, linear algebra, and programming languages
- Minimum grade point average of 3.0
Relevant Personal and Work Experience for Admissions
Applicants to data science grad programs also need technical work experience and/or personal experience that demonstrate quantitative skills, programming, mathematics, statistics, or database administration.
Examples of potentially qualifying work experience through employers found in Missouri would include:
- Crunching data that relates to customer analysis at any employer, such as Mercy Hospital System or Washington University
- Providing cybersecurity or computer network services for companies like Ascension Health or Emerson Electric
- Programming for startups in Saint Louis like MeterGenius
A Data Science Bootcamp in St. Louis or Online Can Boost Your Skills for Master’s Preparation or Entry-Level Employment
A data science bootcamp is a relatively new way to build up your expertise in the field, but it’s a lightning fast path to those skills, and a relatively affordable one at the same time. Bootcamps involve a fast-paced, hands-on education in the kind of cutting-edge tools and techniques that data scientists are actively working with in the field today.
Often, those skills are taught using live data sets and solving actual problems local businesses encounter, giving you the kind of practical experience you need to be job-ready or to transition confidently to a master’s program.
The courses last between one and nine months typically and are offered at a variety of skill levels, from entry-level pre-degree programs to advanced post-graduate programs for seasoned professionals. At all levels, bootcamps are more about practical information and skills training than theoretical concepts.
For that reason, colleges have been slow to get into the game, but that’s changing. You now have the Washington University Data Analytics Boot Camp available both in St. Louis and online, giving you the exact course of training local employers expect of entry-level analysts from a school that’s respected region-wide. In 24-months of intensive evening and weekend courses, the camp will bring you up to speed on important topics like:
- Statistics in modeling and forecasting
- Programming in languages like R and Python
A well-rounded camp like the one at WashU will build your skills from the ground up, offering a straight path to a master’s degree or an entry-level job in the industry. Like many, this program has extensive career services baked in, with portfolio and resume building support. In either case, you’ll get the kind of training you need to look good to a master’s admissions committee or to potential employers in the field.
Closing Gaps in Knowledge Through Bridge Courses and Massive Open Online Courses
There are other options for building your skill level and knowledge that aren’t quite so intense, but also offer more opportunity to customize the training you are getting. Bootcamps are taught off a one-size-fits-all menu. But MOOCs and bridge courses can be more carefully tailored to your particular requirements.
MOOCs – Massive Open Online Courses – These online courses are offered both by private providers and universities, and sometimes by partnerships between the two. They mirror current online college courses in including sample problem sets, online lectures by distinguished speakers, and interactive user forums. Participants get feedback from teaching assistants, professors, and their peers.
MOOCs are useful to students who want to fill any gaps they have in their knowledge repertoire. For example, if you have a strong background in mathematics, but not much programming experience, you might sign up for an informal MOOC covering the programming language R to gain the well-rounded base of knowledge that grad schools offering data science programs look for in applicants.
Bridge Courses – Graduate programs will often offer their students the chance to catch up on key classes offered by the university before enrolling in core data science courses. This is common practice because of the multi-disciplinary nature of the data science field. Bridge courses are available to students that have already been accepted to the graduate program and involve about 15 weeks of pre-master’s coursework before transitioning to the formal data science master’s program.
Fundamental bridge programs may include a series of courses covering things like:
- Linear algebra
- Analysis of algorithms
- Data structures
Bridge programs for programming languages are also often available, with some of the most common languages including Java, C++, Python, and R.
Preparing for Success on the Quantitative Sections of the GRE and GMAT Exams
Applicants also commonly have to demonstrate quantitative competency by scoring in at least the 85th percentile of the GRE or GMAT exams. Although the exams themselves are more general in nature, you’ll receive particular attention for how you handle the quantitative and communications aspects of the tests.
GRE – The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section evaluates students on the following topics:
- Arithmetic topics including integers, factorization, exponents, and roots
- Geometry, including the properties of circles, triangles, quadrilaterals, polygons, and the Pythagorean theorem
- Algebraic topics such as algebraic expressions, functions, linear equations, quadratic equations, and graphing
- Data analysis, covering topics like statistics, standard deviation, interquartile range, tables, graphs, probabilities, permutations, and Venn diagrams
Students can prepare for the quantitative reasoning section by reviewing any of these resources:
- GRE practice exam through Princeton Review
- GRE practice exam through Veritas Prep
- Educational Testing Service’s (ETS) Math Review
GMAT – The Graduate Management Admissions Test’s (GMAT) quantitative section evaluates students’ abilities in data analysis. You’ll have 75 minutes to complete 37 questions in the quantitative section. All of these questions relate to data sufficiency and problem solving.
Earning a Master’s Degree in Data Science in Missouri
Programs in data science are springing up across the nation, including at the master’s level here in Missouri. But there is another path that students often consider these days, one that offers the flexibility of attending any data science master’s program anywhere in the country, without even leaving the house.
Prospective graduate students who want the full competitive advantage that a master’s degree in data science brings can apply to any number of online graduate programs that offer degrees that include:
- Master of Science (MS) in Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Online Certificate in Data Science
- Graduate Certificate in Data Science
- Data Mining and Applications Graduate Certificate
Online master’s programs in data science allow students to complete their education on a flexible schedule as well as at a flexible 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
- Certificate programs can be completed in one to two semesters.
Whatever format you pick, you are looking at having to complete around 30 semester hours of coursework in order to graduate.
Core Curriculum and Immersion
That coursework is a comprehensive perspective on the many core elements of math and coding that go into effective data science, including:
- Data storage and retrieval
- Network and data security
- Data research design and applications
- File organization and database management
- Machine learning and artificial intelligence
- Information visualization
- Statistical sampling
- Ethics and law for data science
Programs often culminate in an immersion experience that gives students the opportunity to put the skills they have learned into practice. Working in teams, students are assigned a project, which could be viewed as their first major real-world assignment as data scientists. Professors and potential employers collaborate with students during this process to assess their competence and ability to work together.
Key Competencies and Objectives
The kinds of competencies you will graduate with are designed to present well-rounded professional capabilities to your future employers. They range from the basic to the advanced, including:
- Elementary programming skills in Python, R, or other languages
- The ability to conduct association mining and cluster analysis
- Be able to work in teams to achieve specific goals
- Be able to interpret and communicate results effectively
Career Opportunities in Missouri for Data Scientists with Advanced Degrees
While Google and their big data center project presents some of the most obvious opportunities for data scientists in Missouri, it is by no means the only company that can benefit from these professionals. Missouri’s high concentration of healthcare and government agencies also represents a big opportunity. Research firm Research and Markets found in 2019 that a $19.6 billion global market for healthcare data analytics was poised to jump to $47.7 billion by 2024. That’s a big increase, and one that data scientists will be instrumental in achieving in Missouri and elsewhere.
- Healthcare industry – Lead by organizations like Barnes-Jewish Hospital System, Mercy Hospital System, SSM Health Care, and Children’s Mercy Hospital
- Government organizations – A significant federal workforce of 56,500 contributing services that amounts to one-fifth of the economy
For example, data scientists in Missouri’s healthcare industry can develop the means for gathering statistics that reveal not just infection rates and patient outcomes, but also the contributing factors. Data scientists working within government can drive operational efficiency improvements with data models developed using sophisticated computer programs.
To attract top talent, employers specify that they are looking for candidates who hold advanced degrees in statistics or other quantitative fields, which can include data science degrees. 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 Missouri:
Data Scientist 4 with Boeing in Saint Louis – Responsible for providing experienced technical analytics support for major initiatives
- Duties include leading teams that analyze complex data sets with advanced methods such as machine learning, mathematical simulation, and mathematical optimization
- Applicants can qualify with a master’s degree in a relevant field plus seven years of related work experience; candidates must also be able to obtain a security clearance
Data Analytics/Statistical Scientist with Monsanto in Saint Louis – works as part of the biotechnology training testing unit
- Key duties of this position include modeling of rich geo-spatial data, teaching scientists statistical concepts, and collaborating with IT analytics teams
- Applicants must at minimum have a master’s degree in statistics, bioinformatics, biostatistics, or another related field
Business Line Data Scientist with Commerce Bank in St. Louis – responsible for using advanced statistical analysis to support and develop strategic business goals
- Duties include analyzing the success of marketing campaigns, developing new statistical predictive models, and conducting advanced analysis with the use of data mining
- Applicants must have a master’s degree in mathematics, statistics, industrial information systems, or another closely related field