As one of the hottest and newly established fields, data science is taking Missouri businesses by storm. From the state’s largest employers like Boeing, Walmart, and the Barnes-Jewish Hospital System 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.
With a report released by the management consulting firm McKinsey and Company projecting that data scientists can increase retail sales alone by over 60 percent nationwide, companies like Walmart are scrambling to add these professionals to their workforce. In fact, Walmart was so desperate to add qualified data scientists to its payroll that it turned to crowdsourcing, using the analytics competition platform Kaggle to generate buzz, as detailed in the 2015 Forbes article, “Walmart: The Big Data Skills Crisis And Recruiting Analytics Talent.”
Walmart collects an astonishing 25 terabytes of unstructured data from a million customers every hour. It keeps extensive customer data on more than 145 million Americans. In 2012, Walmart began using a 250-node Hadoop cluster. Data scientists use this data to develop models that have helped Walmart strategically market to each and every consumer individually, translating into billions of dollars in additional sales.
Whether in retail, manufacturing, finance, logistics or healthcare, a career in data science begins with a master’s degree that gives graduates the theoretical skills they need to effectively collect, clean, organize and analyze data in the information age.
Preparing for a Master’s Degree in Data Science in Missouri
Data science graduate programs recruit students who come from a background that includes relevant academic and professional experience.
In addition to a relevant undergraduate education and proven professional skills, applicants may also need to demonstrate their abilities and close gaps in functional knowledge through the following means:
- Massive open online courses (MOOCs) 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
To ensure the success of the students they recruit, data science graduate programs choose candidates who come from a highly relevant academic background. 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, coding, hacking, mathematics, statistics, database administration, or data mining.
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, Washington University, or Casey’s Store
- Providing cyber security or computer network services for companies like Schnuck Markets, Hy-Vee, or Cerner Corporation
- Programming for startups in Saint Louis like MeterGenius
Closing Gaps in Knowledge Through Bridge Courses and Massive Open Online Courses
MOOCs – Massive Open Online Courses – These online platforms include sample problem sets, recorded 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, a student from a background in mathematics might sign up for an informal MOOC about 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 pertaining to the subjects of:
- 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
Students may be asked, or choose, to demonstrate quantitative competency by scoring in at least the 85th percentile of the GRE or GMAT exams. Different companies sponsor these exams, both of which contain sections that measure quantitative skills.
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. Examinees 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
Data science is such a new field that Missouri’s colleges and universities do not yet offer an undergraduate or graduate program in this subject.
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.
Data science graduate degrees are generally comprised of around 30 semester credits.
Core Curriculum and Immersion
Master’s-level graduate students cover core topics that include:
- Data storage and retrieval
- Network and data security
- Experiments and casual inference
- Experimental statistics
- 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
- Advanced managerial economics
- Applied regression and time series analysis
- Visualization of data
- Data mining
- Quantifying materials
- Scaling data – macro and micro
Programs 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
Students who earn their master’s degree in data science should be able to exhibit these core competencies and apply them in the workplace:
- Be able to conduct database queries
- Be able to conduct association mining and cluster analysis
- Be able to run an analysis of survey data
- Be able to work in teams to achieve specific goals
- Be able to interpret and communicate results
- Be able to develop and conduct sophisticated data analyses
- Develop innovative design and research methods
- Become familiar with hash algorithms, cyphers, and secure communications protocols
- Learn programming languages such as GitHub, SAS, Python, and Shiny by Rstudio
Career Opportunities in Missouri for Data Scientists with Advanced Degrees
While Walmart 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. The McKinsey and Company projection that data scientists can increase retail sales by more than 60 percent also identifies opportunities for hundreds of billions in savings for governmental institutions, and over $300 billion in revenue created in the US healthcare industry. Missouri has significant stakeholders in these sectors, including:
- Healthcare industry – exemplified by organizations like Barnes-Jewish Hospital System, Mercy Hospital System, SSM Health Care, and Children’s Mercy Hospital
- Government organizations – exemplified by a federal workforce of 95,218 generating $9.07 billion, and a state/local workforce of 380,643 generating $24.8 billion (source: US Department of Commerce, 2013)
For example, data scientists in Missouri’s healthcare industry can develop means for gathering statistics that reveal the factors that relate to infection rates and patient outcomes. Data scientists working within government can drive operational efficiency improvements using 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. This is because master’s programs in data science are so new. (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, completed in February 2016):
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 Clayton – 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