The massive demand for qualified data scientists is only expected to grow in the coming years, with companies across a variety of industries relying increasingly on the innovative use of big data. Kentucky looks to be a promising location for data scientists in the coming years, with Fortune 500 companies and startups in the state alike seeking data-derived strategies and solutions.
Louisville-based healthcare giant Humana is just one organization using data science to push boundaries and achieve unprecedented successes. The company states on its website that doctors’ use of big data software, including programs that compare drug interactions, can provide patients with “better, faster care.” Data scientists working for Humana predict and quantify business and health metrics alike, making them pivotal members of the multibillion-dollar corporation.
In the startup realm, Louisville-based Edj Analytics LLC develops predictive modeling techniques to aid companies with data-driven decision making. Led by Sean O’Leary, former CEO of energy intelligence provider Genscape, Edj Analytics creates solutions across diverse fields, including financial markets, education, sports and healthcare.
Preparing for a Master’s Degree in Data Science in Kentucky
As data scientists become increasingly in demand in the professional workforce, admission to master’s degree programs in data science is becoming more selective. To prepare for these programs, students would complete their undergraduate studies while also earning relevant work experience.
Undergraduate Degree and Master’s Prerequisite Courses
In terms of undergraduate education, master’s programs in data science typically expect students to meet the following minimum requirements:
- Applicants must possess a bachelor’s degree in a field such as computer science, statistics, engineering, or applied math
- Applicants must earn a 3.0 GPA or higher during undergraduate studies
- Applicants must complete prerequisite courses, which typically include the following:
- Linear algebra
- Calculus I & II
Admissions offices may also consider applicant criteria in the following areas:
- Fundamental concepts, including data structures, linear algebra, and algorithms and the analysis of algorithms
- GRE and/or GMAT exams
- Prior work experience
Preparing for the GRE/GMAT Exams
To receive top consideration for admission to master’s programs in data science, students would have to score in the top 15% of the quantitative section of the GRE or GMAT. Students may also increase their chances for admission by earning high scores on the communication sections of these exams.
GRE – The Graduate Record Exam (GRE) revised general test quantitative reasoning section evaluates the following:
- Algebraic topics such as:
- Linear equations
- Algebraic expressions
- Quadratic equations
- Geometry topics such as:
- The properties of triangles, quadrilaterals, circles, and polygons
- The Pythagorean theorem
- Data analysis topics such as:
- Interquartile range
- Venn diagrams
- Standard deviation
- Arithmetic topics such as:
By signing up with the Princeton Review or downloading a free program through Educational Testing Service (ETS), students may access practice exams to prepare for test day.
GMAT – Consisting of 37 questions designed to test students’ data analytics skills, the quantitative section of the Graduate Management Admissions Test (GMAT) gives students the opportunity to demonstrate their knowledge in problem solving and data efficiency. To prepare for this section of GMAT, students may take practice exams through Veritas Prep and the Princeton Review.
Relevant Personal and Work Experience for Admissions
An applicant’s professional experience is strongly considered by the admissions staff at master’s in data science programs. Typically, schools seek applicants who have demonstrated exceptional quantitative and analytical reasoning abilities and strong communications skills in the professional realm. Just some of these professional skill sets include:
- Total relevant work experience (five years is preferred)
- Coding skills
- Programming proficiency in languages such as JAVA, C++, and Python
- Database administration proficiency
- Hacking skills
- Communication skills
- Data mining ability
Potentially qualifying work experiences in Kentucky could include:
- Programming for a tech startup in Louisville
- Cyber security at Yum! Brands
- Data management at the University of Kentucky Medical Center
Bridge Programs and Massive Online Open Courses (MOOCs) for Applicants Who Do Not Meet Admission Criteria
Students who lack one or more of the qualifications required for admissions to master’s in data science programs must fill these knowledge gaps before beginning graduate studies. Some schools offer bridge programs that allow students to complete their remaining requirements in programming or various fundamentals. Alternatively, students may independently pursue massive open online courses (MOOCs) to fill gaps in knowledge before the application process.
MOOCs – Massive Open Online Courses – MOOCs are guided by professors and teaching assistants and provide students with online access to problem sets, filmed lectures, and interactive user forums. Students can complete these courses to fulfill their outstanding requirements for admission to master’s programs in data science.
Bridge Programs – Many graduate schools offer bridge programs for students who lack one or more gaps in knowledge in the following areas:
- Linear algebra
- Data structures
- Analysis of algorithms
- Programing in languages like C++, Python, and JAVA
Earning a Master’s Degree in Data Science in Kentucky
Master’s programs in data science consist of both curricular coursework and an immersion experience, which takes place in the final semester. Through accelerated learning formats, students may earn their degree in as little as 12 months. Traditional and part-time learning options allow students to earn their degree in 18-30 months. Examples of master’s degrees in data science include:
- Data Mining and Applications Graduate Certificate
- Online Certificate in Data Science
- Master of Science in Data Science (MSDS)
- Master of Information and Data Science (MIDS)
- Master of Science (MS) in Data Science
- Data Science Certificate
- Graduate Certificate in Data Science
With no campus-based master’s programs in Kentucky specifically related to data science, students in the state take advantage of accredited online programs, which consist of both live courses and self-paced coursework. The flexibility of these programs allows students to further their education without sacrificing current work obligations.
Core Curriculum and Immersion
Master’s in data science programs offer diverse courses designed to equip students with relevant, in-demand skill sets for the professional world. Just some of the courses often found in these programs include:
- Machine learning and artificial intelligence
- Scaling data – macro and micro
- Visualization of data
- Experiments and causal inference
- Ethics and law for data science
- Network and data security
- File organization and database management
- Advanced managerial economics
- Statistical sampling
- Experimental statistics
- Data research design and applications
- Data storage and retrieval
- Data mining
- Quantifying materials
- Applied regression and time series analysis
- Information visualization
Beyond curricular coursework, students must complete an immersion experience – a collaborative project with fellow students and professors that simulates real-world data application. Students use this experience to demonstrate their talents before entering the professional realm.
Key Competencies and Objectives
After graduating from master’s programs in data science, students enter the professional realm with a comprehensive skill set that covers core competencies including, but not limited to:
- The ability to run an analysis of survey data
- Teamwork skills
- The ability to to develop and conduct sophisticated data analyses
- Familiarity with hash algorithms, cyphers, and secure communications protocols
- The ability to conduct database queries
- Proficiency in innovative design and research methods
- Proficiency in association mining and cluster analysis
- The ability to interpret and communicate results
- Proficiency in programing languages such as GitHub, SAS, Python, and Shiny by Rstudio
Career Opportunities in Kentucky for Data Scientists with Advanced Degrees
The presence of eight Fortune 1000 companies in Kentucky means data scientists will likely continue to find corporate career opportunities in the state. Lexington-based Lexmark International, for example, employs data scientists to develop innovative software and algorithms to assist clients ranging from governmental organizations to banks with cost-effective decision making.
The opportunities for Kentucky data scientists do not end at the corporate level, however. The state is becoming a new hub for technology startups, with the state awarding nine high-tech companies with $2.7 million as part of its matching funds programs in 2015. Fast-rising companies compete for the talents of qualified data scientists, seeking fresh and inventive ideas driven from massive data sets.
The following job listings, taken from a March 2016 survey of job vacancy announcements for data scientists in Kentucky, are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
Clinical Analytics Data Scientist at Humana in Louisville – The role would consist of duties including, but not limited to:
- Working with large scale unstructured data to fuse with other enterprise data sources such as demographics
- Developing models to predict, quantify, and forecast business and health metrics
- Working with IT, operation, and business teams to implement new model results or enhancements
Senior Data Scientist at IKKON group in Georgetown – The role would consist of duties including, but not limited to:
- Translating business partners’ questions to information system requirements
- Gathering and analyzing data within the information system environment
- Using UML domain models, ER data models, and business intelligence dimensional models