An increasingly diverse economy in Kansas is leading to more opportunities for data scientists to develop strategies and solutions to the problems of efficiently and profitability in the era of complicated supply chains and logistic considerations for major industries that favor just-in-time delivery over expensive and inefficient warehousing:
Aviation – According to the Kansas State Department of Transportation, in 2017 alone the aviation industry supported more than 90,000 jobs and generated more than $20.6 billion in annual economic output. Wichita is known as the Air Capital of the world, hosting major manufacturers like Spirit Aero Systems and Textron Aviation along with their many suppliers. That’s a big base of big money looking for optimizations in everything from manufacturing to logistics to air mile and passenger analysis.
- Grand Canyon University - B.S. in Business Information Systems and M.S. in Data Science
- SNHU - A.S. in Data Analytics, B.S. in Computer Science, B.S. in Data Analytics, and M.S. in Data Analytics
- 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
Energy – Kansas is the number one state in the nation for wind energy production, according to the Kansas Department of Commerce in 2018, and the state’s location in the U.S. wind corridor further offers enormous potential for future wind turbine manufacturing. The 2017 Symposium on Frontiers in Engineering reported that wind farm systems generate some 2.5 quintillion bytes per day of information from sensors and other sources. That data is vital to the future of green energy, and it all demands capable and accomplished data scientists to store, analyze, and interpret it.
Preparing for a Master’s Degree in Data Science in Kansas
That kind of top expertise in the field demands preparation with a master’s degree in data science. But getting into a top MSDS program isn’t easy—you have to clear a high bar just to hit the minimums, and even then you’ll need to distinguish yourself from thousands of other applicants to earn a slot. It demands careful preparation and top performance in a variety of areas before you apply.
Undergraduate Degree and Master’s Prerequisite Courses
Typical student requirements for being admitted to a master’s program in data science include:
- Completion of an undergraduate course load that includes coverage of areas such as programming languages, quantitative methods, linear algebra, statistics, and advanced calculus
- A minimum of a 3.0 GPA in those courses
- A bachelor’s degree in a relevant quantitative field, which could include statistics, computer science, math, statistics, or engineering
Beyond these core admission standards, programs will also consider your accomplishments in the following areas:
- GRE and/or GMAT exams
- Prior work experience
- Knowledge of fundamental concepts in statistical science
Preparing to Score Within the 85th Percentile on the Quantitative Sections of the GRE/GMAT Exams
With statistics being so important in data science, master’s programs place a strong emphasis on applicants’ scores in the quantitative section of the GRE or GMAT, typically seeking students who score in the top 85th percentile. But that’s not all; because conveying your findings is a fundamental part of data science, they also want to see high scores in verbal and writing components of the tests. One hit wonders need not apply.
GRE – The Graduate Record Exam (GRE) revised general test quantitative reasoning section evaluates the following subjects:
- Geometry topics including the properties of triangles, quadrilaterals, circles, polygons, and the Pythagorean theorem
- Algebraic topics such as linear equations, graphing, functions, algebraic expressions, and quadratic equations
- Data analysis, including topics such as interquartile range, standard deviation, probabilities, permutations, statistics, graphs, Venn diagrams, and tables.
- Arithmetic topics such as exponents, integers, roots, and factorization
To prepare for the GRE, students may take two sample tests by downloading a free program through Educational Testing Service (ETS). Additionally, students may sign up with the Princeton Review to take a practice exam.
GMAT – The quantitative section of the Graduate Management Admissions Test (GMAT) consists of 37 questions that evaluate students’ data analytics skills related to data efficiency and problem solving. To prepare for the GMAT, you may take practice exams through Veritas Prep and the Princeton Review.
Prior Work Experience
By demonstrating strong communication skills and exceptional quantitative and analytical reasoning abilities at the professional level, you can significantly increase your chances for admission to master’s in data science programs. Programs often look for applicants who have demonstrated the following skill sets:
- Coding skills
- Communication skills
- Data mining ability
- Programming proficiency in languages such as JAVA, C++, and Python
- Database administration proficiency
Example of potentially qualifying work experience in Kansas could include:
- Data management at Spirit Aerosystems
- Data scientist 2 at Humana
- Programming at a Wichita or Kansas City startup
Enrolling in a Data Science Bootcamp in Overland Park or Online to Prepare for a Master’s Degree or to Go Straight Into the Job Market
Whether you have gaps in your work experience or your education, one increasingly popular option to build your credentials in data science is now available in Kansas: a data science bootcamp.
Bootcamps offer an accelerated, practical course of training in data science tools and techniques that lasts between one and nine months and can cater to students at various levels, from pre-degree programs providing entry level skills to post-graduate options for mid-career professionals.
These programs are highly focused on applied skills and do not spend a lot of time dissecting data theory or going into the roots of statistical mathematics. Instead, they concentrate on cutting-edge approaches to analysis, usually taught through a series of projects drawing from real-world data sets and concentrating on actual problems data scientists are faced with on the job.
The concept has become popular enough that major universities are starting to offer their own versions, like the University of Kansas Data Analytics Boot Camp. Available on campus in Overland Park or online in a virtual classroom experience, this part-time, 24-week camp takes place on evenings and weekends and runs you through a series of complex projects in a collaborative effort teaching:
- SQL Databases
- Statistical analysis
Experienced instructors help you learn how to tease meaningful trends and insights out of your work, building a portfolio that any employer will be happy to see in your resume. Although you can use this experience to prepare for applying to a data science master’s program, the school also has a comprehensive career services team waiting to help you land a job, so you can also move directly into the job market with your newly-acquired skills.
Bridge Courses and Massive Open Online Course (MOOC) Options for Applicants that Need to Fill Gaps in Knowledge
Even with a strong educational and professional background, some aspiring data scientists may lack one or more of the qualifications necessary for admission to a master’s program. Many schools offer bridge programs that allow you to fulfill these requirements after being accepted, but before beginning graduate studies. Alternatively, you may elect to take massive open online courses (MOOCs) to fill gaps in knowledge before applying to master’s programs.
Bridge programs – Bridge programs are designed to build on your current experience and fill in any of the common gaps that people have when coming into data science programs. Those gaps usually fall into one of two different categories:
- Fundamentals – These programs allow you to earn outstanding qualifications in data structures, linear algebra, and algorithms and analysis of algorithms.
- Programming – These programs offer courses in the programming languages required for graduate study, such as Python, JAVA, or C++.
MOOCs – Massive Open Online Courses – MOOCs are more like free-form, online college courses that may or may not be affiliated with the university you are applying to. The strength here is the flexibility; you can pick courses in exactly the areas you need help with, without having to take additional classes that may be irrelevant. But you also need to have the self-direction and motivation to get through them on your own.
Earning a Master’s Degree in Data Science in Kansas
Master’s degree programs in data science consist of classroom-based coursework and an immersion experience. Typically consisting of 30 semester credits, programs may be completed at different paces depending on the student’s needs:
- Traditional learning format – typically three semesters over 18 months of study
- Accelerated learning format – typically two semesters over 12 months of study
- Part-time learning formats – typically five semesters over 32 months of study
Examples of master’s degrees in data science may include:
- Data Mining and Applications Graduate Certificate
- Master of Science in Data Science (MSDS)
- Graduate Certificate in Data Science
- Master of Information and Data Science (MIDS)
While there are a number of excellent master’s-level data science programs in Kansas, many aspiring data scientists elect to pursue their graduate education through accredited online programs. Through these programs, students may earn degrees such as the Master of Science in Data Science (MSDS) or the Master of Information and Data Science (MIDS) in a flexible learning format that consists of both self-paced coursework and live classes.
Core Curriculum and Immersion
Master’s in data science programs don’t pull punches in their coursework: it’s all hardcore programming, statistics, and visualization training conducted at an extremely high-level. You’ll likely find courses available in your program dealing with subjects like:
- Applied regression and time series analysis
- Ethics and law for data science
- Data research design and applications
- Machine learning and artificial intelligence
- Data storage and retrieval
In addition to these courses, most programs require students to complete an immersion experience – a team project that simulates real-world data application and problem solving. Through these experiences, students can demonstrate their relevant skill sets and talent before entering the job market.
Key Competencies and Objectives
Although that market is hungry for applicants right now, you can be sure that employers are looking for well-qualified, competent, effective professionals. And that means you need to be proficient in data science aspects such as:
- Network and data security
- Visualization of data
- Statistical sampling
- File organization and database management
- Experimental statistics
- Scaling data – macro and micro
Career Opportunities in Kansas for Data Scientists with Advanced Degrees
The diversity of Kansas’s growing economy does not end at the manufacturing and energy sectors — the state is also home to Fortune 1000 companies in areas ranging from truck lending to food production. What’s more, Kansas is a growing hub for innovative startups, with Inc. Magazine rating Kansas City as one of the top 50 metro areas in which to start a small business in 2019. For data scientists in Kansas, the presence of both corporate giants and growing upstart companies means a broad range of potential career opportunities in the coming years.
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 Kansas:
Data Scientist III at Sprint in Overland Park – The role would consist of working within the company’s financial data science team to accomplish goals including, but not limited to:
- Developing processes and systems to analyze massive amounts of data
- Communicating results from data experiments and analysis
- Creating machine intelligence models
Data Scientist at Cargill in Wichita – The data scientist would be responsible for developing data models and algorithms for pattern detection and forecasting, as well as supporting business functions around pricing and risk management through the following techniques:
- Statistical analysis
- Data mining
- Mathematical modeling
Data Scientist at Evergy in Topeka – The data scientist’s role would include duties including, but not limited to:
- Developing algorithms and processes designed to utilize data from multiple data stores
- Creating visualizations to aid in understanding data
- Managing large amounts of data with limited hardware, bandwidth, and software constraints
- Building mathematical models