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.
All that ag activity generates a ton of information in modern farming applications. Farms have come to rely on precision agriculture technologies ranging from automated soil analysis sensors to drone monitoring to maximize yields and head off damaging infestations and other potential causes of crop damage and harvest spoilage.
As all that data makes its way into the digital silos of big agriculture companies, it’s created the same potential privacy and security problems that data collection and storage causes in any other field. That’s why the South Dakota Farm Bureau helped create a tool called the AgData Transparency Evaluator to allow farmers to go online to see what data has been collected about them, how it is being used, and which ag-tech companies have access to it. It can also help to notify farmers in case of security breaches.
Big Ag dominates here, but it’s definitely not the only industry hungry for master’s-educated data scientists capable of pulling cost-saving insights from troves of collected data. 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 has been relying on IBM’s Big Data and Analytics technology for a number of years now to track and manage medical care and other senior services 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 them and develop individually tailored treatment plans.
Whether in healthcare, agriculture or virtually any other industry sector, master’s educated data scientists in South Dakota are a white hot commodity, and the way things look today, will continue to be well into the future.
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 their graduate studies early on. Most South Dakota-based and online data science graduate programs have stringent admissions requirements, that include a deep look at what was accomplished during undergraduate studies:
- Undergraduate courses in science, computer programming and mathematics
- Work experiences in areas such as computer programming, database administration or other quantitative work
- Strong results on the GMAT or GRE, particularly the quantitative sections
Undergraduate Degree and Master’s Prerequisite Coursework
Graduate school admissions departments will look for applicants who can check off the following qualifications in their undergraduate studies:
- A bachelor’s degree in a relevant field like engineering, data science, statistics, mathematics, business intelligence or computer programming with a minimum GPA of 3.0
- Undergraduate courses 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
Your practical experience or work background should include:
- Work experience using quantitative skills and analytics tools or some relevant personal experience in areas such as mathematics, statistics, or computer programming
- Letters of recommendation from those who are familiar with this work or personal experience, and/or with the applicant’s academic qualifications
Examples of the kinds of jobs with South Dakota firms that could help an applicant meet these kinds of experience requirements include:
- 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
Unless you qualify for a waiver, graduate department admissions officers with South Dakota’s universities would generally look 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 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
Some graduate schools might also require the following GRE subject area tests to vet applicants as being ready for the kind of data-intensive curriculum you’d find in a master’s program in data science:
- 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
Alternatively, some schools rely on the GMAT rather than the GRE. 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
It’s entirely possible that you didn’t decide to pursue a career in data science until long after you earned your undergrad degree and started off on a different career path. This means you might not have the fundamental knowledge or demonstrated skills necessary to make an impression with a master’s admissions committee.
But you don’t have to start entirely over with your education and job progression. Instead, consider a data science bootcamp.
These camps are offered at a wide range of skill levels, from the entry level to the extremely advanced. They tend to last for a few weeks or a few months, and offer a very practical, hands-on education in data science tools and techniques. They were originally delivered in-person and full-time, by private companies in the field, but today they are also offered by big name universities and can be found in online and part-time variations. Some of the options open to grad students in South Dakota include:
- 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
All bootcamps are delivered in a cohort style and usually revolve around a set series of projects that you’ll complete with a team of your classmates. Each project offers a piece of the educational puzzle, situated around a real-world problem and usually conducted on real-world data sets. Those projects will teach you about topics like:
- Big data analysis and data stores like Hadoop enabling NoSQL databases like HBase
- Traditional SQL language and data stores like SQL Server
- Programming languages like R and Python
- Specialized analytic libraries like Numpy
- Data visualization tools like Tableau and D3.js
Supervised by instructors with real-world experience, you’ll build out a portfolio that is sure to impress any admissions committee or prospective employer. To this end, most bootcamps also offer some career services to assist you in polishing your CV and interview techniques before graduation.
Filling Gaps in Functional Knowledge By Means Of Bridge Courses or MOOCs
Not everyone needs a full-fledged bootcamp to get prepped for their master’s application, however. In some cases, you may only have a handful of tiny gaps in skills or knowledge to fill, which would benefit from a more tailored approach. That’s available via bridge courses or MOOCs.
Bridge Courses – Pre-master’s bridge courses are offered by some graduate data science degree programs for accepted students who may have a few 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 give otherwise well-qualified students the chance to complete prerequisites. For example, a major in computer programming may have a gap in mathematics knowledge, and the graduate program may offer bridge courses to effectively 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)—These online courses are offered in a variety of ways, including free of charge, at cost, and often by major universities. Examples of popular subject areas found in MOOCs that data science grad students frequently take 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
Universities across South Dakota, in Brookings, Watertown, Aberdeen, Sioux City and Rapid Falls, as well as online, offer graduate programs in data science. 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 you go with:
- 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.
Core Courses, Internship and Immersion Experience
The core courses you’re likely to take in a data science master’s program include:
- Big data analytics
- Statistical programming
- Modern applied statistics
- Data warehousing and data mining
- Information management and file organization
- Time series analysis
- 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 with a company in the field, which is something that often leads to long term employment. 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 these 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
According to officials at Dakota State University, financial services firms in the state are scrambling to fill seats for data scientists. AI and algorithmic trading represent the easiest money out there, and data science expertise is a no brainer for these firms as far as where to invest in resources.
They’re not the only industry on the hunt for qualified data scientists, though. According to DICE, a major online job listing service, two of the top three fastest growing technology job categories for 2020 are in data science and data engineering. That kind of demand fuels some spectacular salaries. Technology recruiting firm, Robert Half, found that starting salaries for Sioux Falls data scientists could be anywhere from $89,000 to $151,000 annually as of 2020.
The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
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 analytics. 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 involves 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.