According to a 2014 article on Mashable.com, the business social networking platform LinkedIn voted statistical analysis and data mining the top skill that got applicants hired in 2014. While some may not think of South Dakota as a hub for big data, many are surprised to learn of its variety of applications in many of South Dakota’s industries.
- 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
For example, the agriculture industry uses big data to collect and analyze information from South Dakota farmers. Additionally, a tool called the AgData Transparency Evaluator, developed by the South Dakota Farm Bureau, allows farmers to go online to see what data has been collected about them, how it is being used, and which agriculture technology providers have access to it. It can also help to notify farmers in case of breaches of data security.
Big data is being used in health care in South Dakota as well. The Evangelical Lutheran Good Samaritan Society, the nation’s largest non-profit provider of senior health care services, is headquartered in Sioux Falls. The organization began using IBM Big Data and Analytics technology in 2014 to better provide and manage services for seniors in South Dakota and across the nation. Through this technology, information on senior patients is gathered 24 hours a day, 7 days a week and allows caregivers to better monitor and plan treatment for them. In time, the Society will add IBM’s predictive analytics to predict when a senior citizen patient is at risk for health problems.
Data scientists in South Dakota who hold master’s degrees have many career opportunities available to them, with varying job titles. Sanford Health in Sioux Falls, a group that runs many of the state’s hospitals and health care organizations, employs these professionals as Business Intelligence Supervisors in Analytics. Their responsibilities include managing clinical and statistical information and analysis of financial data. They also are responsible for accuracy and validity of data as well as regulatory compliance and interpretation of data
International computer programming company Mashpoint in Sioux Falls hire data science graduate degree holders for the position of Director of Data Modeling and Mining. This position designs and implements processes related to predictive and analytical modeling and data mining as part of the company’s global sales team. Other job duties include writing code in an analytics environment and development in business intelligence tools. The Director is responsible for working with many teams, including human resources, marketing, business development, finance, operations, information technology, and retail.
Rapid City Regional Hospital in Rapid City hires data scientists to fill the position of Clinical Intelligence Analyst. This position involves the development of new performance standards and metrics to support improving quality and safety through data reports and analysis. The hospital relies on this person to provide analytic support in developing quality, safety, efficiency and experience measures.
Preparing for a Master’s Degree in Data Science in South Dakota
Undergraduate students who are certain of their future plans should begin to prepare for a master’s degree in data science. Most South Dakota and online data science graduate programs have admissions requirements that must be met, and many of these requirements can be met during undergraduate studies. They include:
- Undergraduate courses in science, computer programming and mathematics
- Work experiences in areas such as computer hacking, computer programming, database administration or quantitative skills
- Preparation for the GMAT or GRE, particularly the quantitative sections
- Bridge gaps in knowledge through MOOCs or bridge courses, if necessary
Undergraduate Degree and Master’s Prerequisite Coursework
Graduate data science degree programs in South Dakota and online seek applicants who met the following prerequisites:
- Possess a bachelor’s degree in a relevant field like engineering, data science, statistics, mathematics, or computer programming with a minimum GPA of 3.0
- Have completed undergraduate course prerequisites that display quantitative skills and knowledge, such as calculus I and II, probability and statistics, matrix algebra, and programming fundamentals
Pertinent Work and Personal Experience
Graduate data science degree programs in South Dakota and online are also looking for applicants with the following background:
- A minimum of five years of work experience using quantitative skills and knowledge
- Personal experience in quantitative skills and knowledge, in areas such as mathematics, statistics, data mining, computer hacking, or coding
- Letters of recommendation from those who are familiar with this work or personal experience, and/or with the applicant’s academic qualifications
Examples of work experiences in South Dakota that could help an applicant qualify for admission for graduate data science degree programs are:
- IT Plant Analyst at Smithfield in Sioux Falls
- Certified Coder for Horizon Health Care in Aberdeen
- System Administrator for Dacotah Bank in Rapid City
- Data Analyst for the South Dakota State Government in Pierre
Passing the GRE and GMAT Examinations
Admissions officers for graduate data science programs at colleges and universities in South Dakota and online are looking for applicants who score in the 85th percentile or better on the quantitative sections of the GMAT or GRE examinations.
GRE – The Graduate Record Exam (GRE) revised general exam’s quantitative reasoning portion tests knowledge through the following types of questions
- Algebra, including quadratic equations, linear equations, graphing and algebraic expressions
- Arithmetic, including roots and exponents, factorization and integers
- Geometry, including the properties of quadrilaterals, circles, triangles and polygons
- Data analysis, including probabilities, statistics, standard deviation, graphs and tables
Preparation support for the GRE is available through:
- The Math Review, offered by the Educational Testing Service (ETS)
- GRE Practice Exams, offered by the Princeton Review
- GRE Practice Exams, offered by Kaplan Test Prep
Applicants who plan to apply to graduate data science degree programs may also take these optional GRE subject area tests:
- Mathematics (Mathematics Test Practice Book),with questions on:
- Discrete mathematics
- Probability, statistics and numerical analysis
- Introductory real analysis
- Physics (Physics Test Practice Book), with questions on:
- Optics and waves
- Lab methods and specialized topics
- Special relativity
- Atomic physics
- Classical mechanics
- Statistical mechanics
- Quantum mechanics
The quantitative section, one of the four main sections of the GMAT – The Graduate Management Admission Test, evaluates the test-taker’s data analysis knowledge and skills through 37 questions on problem solving and data analysis that must be completed in 1 hour and 15 minutes. Support for preparation for this test may be obtained through:
Online Data Science Bootcamps to Get You Job-Ready or to Prepare for a Master’s Program
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– Northwestern Data Science and Visualization Boot Camp
– Rice University Data Analytics Boot Camp
– The Data Analysis and Visualization Boot Camp at Texas McCombs
– University of Minnesota Data Visualization and Analytics Boot Camp
Filling Gaps in Functional Knowledge By Means Of Bridge Courses or MOOCs
Bridge Courses – Bridge courses, which are at the pre-master’s degree level, are offered by some graduate data science degree programs for applicants who have gaps in their knowledge. These courses may be offered in-person or online, sometimes in the summer before a student’s first semester begins, and are designed to correct undergraduate preparation deficiencies. For example, a major in computer programming may have a gap in mathematics knowledge, and the graduate program may offer bridge courses to bridge this gap. Graduate schools usually offer bridge courses in areas like:
- Computer programming, especially in languages such as SAS, R, Python, Java and C++
- Mathematics, especially in areas such as statistical methods, linear algebra and analysis of algorithms
Massive Open Online Courses (MOOCs)—Another option to bridge knowledge gaps is MOOCs. These online courses are offered in a variety of ways, including free of charge, at cost, by public universities or via private invitation only. Examples of popular subject areas of MOOCs that have been taken by data science students include (but are not limited to):
- Computer programming languages, such as R, Python, Java and C++
- Machine learning
- Database administration
- Linear algebra
Earning a Master’s Degree in Data Science in South Dakota
Colleges and universities across South Dakota and online offer master’s degrees in data science. Thanks to these programs, students in areas such as Brookings, Watertown, Aberdeen, Sioux City and Rapid Falls have access to these programs. Examples of degrees available within South Dakota and online include:
- Master of Science in Data Science
- Master of Information and Data Science
- Graduate Certificate in Data Science
Program length varies depending upon the type of program chosen:
- Traditional master’s degree programs are about 30 credits long and may be completed in a year to three years, depending upon if a student is attending classes full- or part-time.
- Online master’s degree programs are also about 30 credits long, but offer students more flexibility, in that they may be taken from anywhere in the world and may be completed more rapidly than traditional programs:
- Full-time students may earn a master’s degree in data science in as little as one year to one and a half years
- Part-time students may earn a master’s degree in data science in two to three years
- Students in accelerated online programs may earn a master’s in data science degree in one year
Some students opt for a graduate certificate in data science in lieu of a master’s degree. Such programs are from 12 to 18 credits long and may be completed in one year to a year and a half. However, many employers are looking for employees with master’s degrees, not graduate certificates, so these certificates may not be as marketable as a master’s of data science in the long run.
Core Courses, Internship and Immersion Experience
Master’s in data science degree programs usually include the following core courses:
- Big data analytics
- Statistical programming
- Modern applied statistics
- Data warehousing and data mining
- management and file organization
- Time series analysis
- Programming in data analytics
- Nonparametric statistics
- Predictive analytics
- Network and data security
- Law and ethics in data science
Most graduate data science programs also require students to complete a graduate internship, in which the student is placed into a real-life work experience in data science. Prospective employers and professors assess students’ competencies in the internship, and students also get the chance to network with fellow students and potential employers.
Another networking opportunity may be presented through an immersion experience. Although not all graduate data science programs require one, this experience allows students to work in a group setting on a case study, collaborating with fellow students and using innovative skills to solve problems together. Again, professors and potential employers will evaluate students’ performance in these settings.
Key Competencies and Program Objectives
Graduates of master’s in data science programs are expected to have met these benchmarks:
- Learned the fundamentals of data analytics
- Developed competencies in programming languages including Python, as well as data-related Python libraries like Pandas, Numpy, and Scipy
- Be able to store and access data from a variety of sources including web-based, traditional relational databases, and NoSQL data stores
- Mastered basic software engineering practices and have an understanding of how they enable reproducible and scalable data analyses
- Learned how to scope resources required for a data science project
- Applied statistical methods, regression techniques, and machine learning algorithms to make sense out of large and small data sets
- Have knowledge of the analyses possible given a particular data set
- Have the ability to speak to different groups within an organization, from management to the information technology director, to apply data science solutions
Career Opportunities for Data Scientists in South Dakota with Advanced Degrees
As noted by South Dakota State University scientist David Clay, data can help to build a bridge between science and informed policy. Data science in South Dakota has helped to improve the usability of the state’s land and predict future use for crop production and management, as reported by the Soil Science Society of America.
South Dakota has become an innovator in data science and its use in preventing counterfeiting of goods and service. One of its native companies, Bright Planet of Sioux Falls, has developed an automated infringement detection system to help detect mentions of its clients’ products online. Another area in which data science is utilized in South Dakota is the financial services industry. According to officials at Dakota State University, financial services employers in the state are seeking graduates with data science and data analytics capabilities, as they realize the importance of big data analytics to their industry.
(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 South Dakota, completed in March 2016.)
Stress Testing Quantitative Analyst with Great Western Bank in Sioux Falls – This position with one of the state’s largest financial services providers involves testing third party models for use in its Dodd Frank Act Stress Tests and other bank stress tests. The responsibility of the Stress Testing Quantitative Analyst is to make sure that these tests are fit for their purpose, are built on sound principals, and are usable.
Applicants must have an advanced degree in data science, mathematics, quantitative modeling or engineering, along with three to five years of quantitative experience and strong data mining skills.
Data Engineer with Omnitech in Sioux Falls – This local software engineering firm and Microsoft Gold Partner was seeking a Data Engineer to work in business intelligence consulting, data warehouse consulting, data acquisition consulting, data management, and data architecture. Skills necessary for this position include understanding of data profiling techniques, understanding of SQL Server design and development, knowledge of Multidimensional Expression (MDX) and Data Analysis Expressions (DAX) languages, and experience with dimensional modeling, star schema and Kimball Data Warehouse methodologies.
Applicants must have a graduate degree in data science, engineering, computer science or a related field, plus three years of data experience.
Systems Engineer with Boeing at Ellsworth Air Force Base – This position is responsible for working on modifications and upgrades for the B-1 Bomber. The candidate must work with B-1 software, be familiar with platform modernization using system engineering skills, providing B-1 technical advice and maintaining and troubleshooting the B-1 Integrated Battle Station Crew Familiarization Modules and Fully Integrated Data Link Ground Stations.
Applicants must be eligible for U.S. security clearance and have a master’s degree in data science, engineering or computer science, plus three or more years of experience.