Online Master's in Data Science for Jobs in Kansas

Qualified data scientists look to be increasingly in demand in the coming years, as organizations ranging from Fortune 500 companies to the U.S. government are collecting and using big data at unprecedented rates.

In Kansas, an increasingly diverse economy is leading to more opportunities for the state’s data scientists, who develop strategies and solutions for some of the state’s largest and fastest growing industries:

Manufacturing According to the Kansas Department of Commerce, the city of Wichita alone produces more than 30 percent of the world’s general aviation aircraft. Kansas-based aviation manufacturing giants such as Beechcraft, Bombardier Learjet,and Spirit AeroSystems are increasingly using big data to reduce costs on everything from machine operations to international shipping. In fact, Sangita Richardson, Senior HR Manager at Spirit AeroSystems, spoke directly about the company’s use of data science in its hiring practices at a 2015 Mercer conference. Specifically, the company compares employee data such from local and foreign markets to seek cost-effective recruiting strategies.

Energy Kansas is among the top 10 states for operating wind farms, according to the Kansas Department of Commerce, and the state’s location in the United States’ wind corridor offers enormous potential for future wind turbine manufacturing. The innovative use of big data will undoubtedly continue to bolster the wind sector in the coming years. For example, Time Magazine noted in a 2013 article that “smarter energy management can help utilities better handle intermittent renewable sources like wind, compensating automatically when the wind isn’t blowing.”

Preparing for a Master’s Degree in Data Science in Kansas

To earn admission to a master’s degree program in data science, bachelor’s-prepared professionals would distinguish themselves from their peers by earning relevant work experience and demonstrating competency in a variety of areas related to the field.

Undergraduate Degree and Master’s Prerequisite Courses

Typical student requirements for admission to master’s programs 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 calculus I and II.
  • A minimum of a 3.0 GPA during undergraduate studies
  • 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 consider applicant criteria in the following areas:

  • GRE and/or GMAT exams
  • Prior work experience
  • Fundamental concepts

Preparing to Score Within the 85th Percentile on the Quantitative Sections of the GRE/GMAT Exams

Master’s in data science 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. Programs may also consider students’ scores on the Verbal and Writing sections of these exams.

GRE – The Graduate Record Exam (GRE) revised general test quantitative reasoning section evaluates the following:

  • 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, students 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, applicants may significantly increase their 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
  • Hacking skills
  • 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
  • Cyber security at Lansing Trade Group
  • Programming at a Wichita or Kansas City startup

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 master’s programs. Many schools offer bridge programs which allow students to fulfill these requirements before beginning graduate studies. Alternatively, students may elect to take massive open online courses (MOOCs) to fill gaps in knowledge before applying to master’s programs.

Bridge programs – Students admitted to master’s in data science programs that offer bridge programs would be able to fill gaps in knowledge in two focus areas:

  • Fundamentals – These programs allow students 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 provide students with the opportunity to obtain skill sets in diverse areas through a blend of learning formats such as:

  • Filmed lectures
  • Interactive user forums
  • Problem sets
  • Student-professor communication

Earning a Master’s Degree in Data Science in Kansas

Master’s in data science programs consist of curricular 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)

With no schools offering campus-based master’s in 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 offer diverse courses that give students a comprehensive skill set before entering the professional data science realm. Just some of the courses often found in these programs include:

  • Applied regression and time series analysis
  • Advanced managerial economics
  • Ethics and law for data science
  • Experiments and causal inference
  • Data research design and applications
  • Machine learning and artificial intelligence
  • Data storage and retrieval
  • Immersion
  • Experimental statistics
  • Data mining
  • Visualization of data
  • Information visualization
  • Statistical sampling
  • Network and data security
  • File organization and database management
  • Quantifying materials
  • Scaling data – macro and micro

In addition to these courses, 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 professional realm.

Key Competencies and Objectives

Upon graduation from master’s programs in data science, students should be proficient in areas including, but not limited to:

  • Advanced managerial economics
  • Data storage and retrieval
  • Data mining
  • Information visualization
  • Applied regression and time series analysis
  • Machine learning and artificial intelligence
  • Quantifying materials
  • Experiments and causal inference
  • Network and data security
  • Visualization of data
  • Data research design and applications
  • Ethics and law for data science
  • In addition to traditional cour
  • 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 Entrepreneur ranking Kansas as one of the 10 best states to start a small business. For data scientists, the presence of both corporate giants and growing upstart companies in Kansas 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, completed in March 2016:

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:

  • Optimization
  • Statistical analysis
  • Data mining
  • Mathematical modeling

Data Scientist at Westar Energy 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

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