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 innovations derived from big data. A 2018 report from LinkedIn found that there was a shortage of more than 150,000 data scientists nationwide, and the gap is only likely to have increased since then. 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.
But if big businesses aren’t your style, Kentucky has some surprising entries in the big data startup front as well. EdjAnalytics, based in Louisville, offers data analytics services for hire to a wide variety of industries, all growing out of a very Kentuckian introduction to data science: through football.
The company’s founders, an astrophysicist and a one-time world backgammon champion, put together a simulation model called Zeus that helped assess the win probability of intra-game risk management decisions. With the publicity and success that emerged from that experience, EdjAnalytics was formed, and has gone on to win the Best Tech Startup in Kentucky and in Louisville for three years running. With a master’s degree under your belt, you can join in that success… or maybe even start your own blockbuster analytics firm.
Preparing for a Master’s Degree in Data Science in Kentucky
Even as companies of all stripes come to rely more and more on big data analytics, clearing the bar to get into a master’s program in data science hasn’t gotten any easier. To prepare for these programs, you’re going to have to find ways to build both your elementary knowledge base in math and coding, as well as put together a portfolio showcasing your experience in applying that knowledge.
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:
- Statistics
- Linear algebra
- Programming
- 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 need to score in the top 15% of the quantitative section of the GRE or GMAT. Relatively high scores are also expected on the communication sections of these exams, since data science discoveries are worth very little without some ability to convey them accurately.
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
- Statistics and Probabilities
- Permutations
- Arithmetic topics such as:
- Integers
- Exponents
- Roots
- Factorization
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)
- Programming proficiency in languages such as Java, C++, and Python
- Database administration proficiency
- Communication skills
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
Considering an Online Data Science Bootcamp to Get Into Master’s Programs or Employment in the Industry
You’re probably getting the picture that it’s not easy to get into a master’s degree program in data science. You’re not wrong; they are looking for the best, to make them better, and that means showing up with some demonstrable skills or experience in the field for most candidates.
If you are struggling with ways to get those stand-out skills, then it’s time to think about joining a data science bootcamp. These shake-and-bake programs last for only weeks or months and cost a fraction of what a degree does. Yet when you come out the other side, you will find yourself having absorbed solid, hands-on skills in a wide array of data science techniques and technologies, including:
- Data visualization tools like D3.js and Leaflet.js
- Programming abilities in JavaScript, Python, or even R
- Machine learning and Big Data handling
- Social media mining techniques
- Advanced applied statistical analysis
- Essential SQL and database store experience
As fast-paced, low-theory, high-application programs, you’ll find that most of your tasks will be oriented toward performing real-world analysis projects on live datasets from diverse industries ranging from healthcare to government. You will typically work on them together with others in your cohort, supported and directed by experienced instructors who often come straight from important positions working in the data science industry.
Bootcamps come in all shapes and sizes and aimed at every possible skill level. To get ready for a master’s program or an entry-level position, you’ll want to find a bootcamp that focuses on core skills and offers admissions to students without prior experience or a great deal of training.
When you add it all up, it’s the perfect preparation for either a job in the industry or an improved chance at a slot in a master’s program in data science.
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 can 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 through the school itself. 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 admissions requirements. While you get the flexibility to choose your own courses and complete them at your own pace, you’ll also need the discipline to get through them without the framework of traditional courses.
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
- Programming in languages like C++, Python, and Java
These classes are typically the basic undergraduate level courses that you have likely already taken in your bachelor’s program, but are available as bridge courses if you missed them for some reason.
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 usually 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
Both in Kentucky and nationwide, students can 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
- Visualization of data
- Experiments and causal inference
- Ethics and law for data science
- Network and data security
- File organization and database management
- Data research design and applications
- Data mining
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, 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 develop and conduct sophisticated data analyses
- Familiarity with hash algorithms, cyphers, and secure communications protocols
- Proficiency in innovative design and research methods
- Proficiency in association mining and cluster analysis
- The ability to interpret and communicate results
Career Opportunities in Kentucky for Data Scientists with Advanced Degrees
The presence of seven Fortune 1000 companies in Kentucky means data scientists will likely continue to find corporate career opportunities in the state. Lexington-based Valvoline, for example, employs data scientists to develop innovative software and algorithms to assist in developing chemical formulations and honing manufacturing and development processes in factories around the world.
The opportunities for Kentucky data scientists do not end at the corporate level, however. The state is becoming a new hub for technology startups, jumping up two spots since 2018 on the DICE Tech Job Report for 2020’s list of the hottest states for tech industry development. Fast-rising companies compete for the talents of qualified data scientists, seeking fresh and inventive ideas driven by insights gleaned from massive data sets.
The following job listings 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 consists 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 consists 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