New York’s CompStat system gets all the glory for breaking ground in data-driven policing, but in Tennessee, data science professionals don’t have to look any further than Memphis to find how big data can improve public safety. It’s been a decade and a half since the Memphis Police Department launched the Blue CRUSH (Crime Reduction Utilizing Statistical History) initiative in conjunction with the University of Memphis, using predictive analytics to drive police presence to maximize deployments in areas that needed law enforcement attention the most. Even early on, they saw dramatic reductions in violent crime of up to 25% in certain high crime areas.
More importantly, the program has stood the test of time. The algorithm is a whole lot smarter now than it was in those early iterations and has led to a 20% reduction in the number of officers needed to effectively police those areas thanks to smarter and more effective resource allocation.
It’s definitely not just government marshalling the power of big data. Across the board, Tennessee’s entire business and scientific communities are devoting heavy resources to putting the numbers to work in a way that is just as impactful in the real world.
Information technology consulting and solutions firm Zycron, headquartered in Nashville, is one example of a Tennessee company that keeps building out their team of data scientists to take on projects for business clients on a contract basis. Here, data scientists work with clients across a range of industries to collect new data, refine existing data, perform hypothesis testing, work on pattern classification, perform parameter estimation, and communicate the findings of their quantitative analysis.
Concert Genetics, a genetic testing company in Nashville, mixes two cutting edge fields by combining data science and advanced DNA testing to develop new data products involved in genomic testing. Working closely with marketing, sales, information technology and others in implementing, deploying and analyzing the company’s data assets, their work will have tremendous impacts on health and the health science field for generations to come.
From government to outside contract work to in-house analysts in every niche of research and commerce, almost every serious data scientist finds their place after first earning a master’s degree.
Preparing for a Master’s Degree in Data Science in Tennessee
Master’s programs in data science are packed with students these days with a crush of computer science and statistics guys looking to land one of those prestigious six-figure data science positions. If you want to get into one yourself, you need to start planning for it early, and make sure you check all the boxes required to get your resume to the top of the stack. That process all starts as early as your freshman year in college.
Undergraduate Degree and Master’s Prerequisite Courses
That’s because your choice of an undergraduate degree can have a dramatic effect on whether or not you get into a master’s program. Hard science skills in areas like math, statistics, or computer science find favor with admissions officers; poetry, interior design, and underwater basketweaving, not so much. Prerequisites for graduate-level data science degree programs typically include:
- A bachelor’s degree in data science, computers, mathematics or a related field, with a GPA of at least 3.0 on a 4.0 scale
- Undergraduate courses listed on a transcript including programming, statistics and probability, calculus I and II, and others that require quantitative skills
Related Work and Personal Experience
In addition to academics, applicants to graduate-level data science degree programs need solid practical skills to receive due consideration. Typically, you must have:
- At least five years of work experience involving quantitative skills
- Some type of personal experience involving mathematics, coding, statistics, or data analysis
- Letters of recommendations from professors or professionals who are familiar with your qualifications
In Tennessee, some examples of work experiences that would help you to meet these criteria include:
- Programmer II at Health Trust in Brentwood
- Coder Analyst Specialist at Covenant Health Corporate in Knoxville
- Database Administrator at Experian in Franklin
Passing the GRE and GMAT Examinations
Passing graduate examinations, particularly the quantitative sections, is of great importance when applying to graduate data science degree programs. Admissions officers are typically looking for scores in the 85th percentile or above on the quantitative sections of the GRE or GMAT examinations.
GRE — the Graduate Record Exam (GRE) revised general exam’s quantitative reasoning section assesses a test-taker’s quantitative knowledge by posing:
- Algebraic questions on topics like algebraic expressions, graphing, linear and quadratic equations
- Arithmetic questions, on topics such as integers, roots and exponents, and factorization
- Geometric questions, on subjects including polygons, triangles, circles and quadrilaterals,
- Data analysis, including tables, graphs, probabilities, statistics, and standard deviation
Students may prepare for the GRE by studying:
- The Educational Testing Service (ETS)’s Math Review
- The Princeton Review’s GRE Practice Exams
- Kaplan Test Prep’s GRE Practice Exams
Subject area tests that help applicants to graduate data science degree programs are:
- Mathematics (Mathematics Test Practice Book):
- Algebra
- Calculus
- Discrete mathematics
- Introductory real analysis
- Probability, statistics and numerical analysis
- Physics (Physics Test Practice Book):
- Classical and quantum mechanics
- Electromagnetism
- Lab methods and specialized topics
GMAT – The Graduate Management Admission Test includes a quantitative section that assesses an applicant’s knowledge of problem solving and data analysis by having them complete 37 questions in 75 minutes. Study aids for the GMAT may be found through:
Enrolling in a Data Science Boot Camp in Nashville or Online to Build Your Skills for a Master’s Degree and a Job
A fast-track path to employment, or the kind of skills a master’s program often looks for in candidates, is available through data science boot camps, which are now being offered at all skill levels for students and professionals at different stages.
Usually lasting between one and nine months, these intensive programs provide a rapid-fire introduction to hands-on, practical data science work. They typically rely on messy, realistic, live data and use the same type of cutting-edge tools to store and analyze it that data scientists use on the job.
Boot camps have traditionally been offered by dedicated education providers, but big-name colleges are starting to bring their formidable faculty and resources to bear on developing these programs themselves. In Tennessee, the Vanderbilt University Data Analytics Boot Camp is the sterling example of this, offered either in-person in Nashville, or online through a virtual classroom. Here you will learn the data science skills that will enable you to solve a host of real-world problems.
A 24-week, part-time program that holds class on evenings and weekends to accommodate your current work/life schedule, the Vanderbilt program offers an entry-level take on data science that will expose you to fundamental skills in areas such as:
- Python , Excel and Javscript coding
- Data analysis and statistical libraries like NumPy
- Visualization techniques with D3.js and third-part tools like Tableau
- Essential statistical modeling and forecasting techniques
- HTML/CSS, Tableau, and more.
- Big data storage and analytics with Hadoop and machine learning
As a base-level boot camp, you can get in with just a GED and your 18th birthday in the rearview mirror, although a bachelor’s and two years experience are recommended. A full-time career services team is hard at work throughout the course of the program, helping you build out your resume and portfolio to ensure you can land a job when you graduate, or to spiff up your grad school application with some real world expertise.
Filling Gaps in Functional Knowledge By Means Of Bridge Courses or MOOCs
If a boot camp is a little too intense for you, there are a couple of other options available to burnish your skillset before you get dropped into the deep end of data science graduate studies.
Bridge Courses – Some graduate data science degree programs will give applicants the opportunity to take bridge courses, if necessary. These courses, which are at the pre-master’s degree level, are designed to compensate for functional knowledge gaps an applicant may have. Many colleges and universities offer these courses online or in-person. Graduate data science degree programs usually offer bridge courses in subject areas such as:
- Computer programming languages, particularly C++, Java, Python, R and SAS
- Mathematics, particularly in topics like linear algebra, statistical methods and analysis of algorithms
Massive Open Online Courses (MOOCs)—These online courses provide another way applicants can fill in functional knowledge gaps in a variety of areas. They may be offered by online for free or for a fee, and are often simply up-scaled versions of popular university courses from major educational institutions. You can tailor your enrollment to exactly match your needs, unlike bridge course or boot camps. MOOCs that may be of particular use to data science students include:
- Data mining
- Statistics
- Data analytics
- Machine learning
Earning a Master’s Degree in Data Science in Tennessee
Students in Tennessee enjoy access to both traditional and online graduate programs today. Related degrees that are available in Tennessee and online include:
- Master of Science in Data Science
- Master of Science in Predictive Analytics
- Master of Science in Data Management and Analysis
- Master of Science in Professional Science with a concentration in Informatics
These programs vary in length:
- Traditional master’s in data science degree programs run from 30 to 40 credits in length and are finished in 12 to 36 months, on a full- or part-time basis
- Online master’s programs consist of about the same amount of credits, but give students more flexibility to take courses from anywhere, anytime. Often, they are completed more quickly than traditional graduate degree programs:
- 12 to 18 months for full-time students
- 24 to 36 months for part-time students
- 12 months for students of online accelerated programs
A graduate certificate in data science is another option for students. These programs range from 12 to 18 credits in length, and are completed in 12 to 18 months. Many employers, however, will not accept a graduate certificate when another applicant with a master’s degree is available, so the graduate certificate is not as marketable or practical as a master’s degree.
Core Courses, Internship and Immersion Experience
Core coursework found in a typical master of science in data science program includes:
- Experimental statistics I and II
- File organization and database management
- Network and data security
- Data visualization
- Data mining
- Statistical sampling
- Quantification
A graduate internship may also be required. The student will be placed into a real-life data science work experience, and have the chance to interact and network with other workers and potential employers. Professors and employers grade the student’s performance during the graduate internship.
Schools commonly require an immersion experience in a graduate data science program. This program consists of group case study work on a given topic. Students and faculty will meet in person and collaborate, providing more relationship-building and networking opportunities.
Key Competencies and Program Objectives
Of course, all your studies are designed toward one end: producing a competent, capable data science professional who corporations can hire and expect to create meaningful data analysis programs. Competencies that employers expect graduates of master’s in data science programs to display are:
- Statistical analysis
- Programming
- Data mining
- Machine learning
- Database management
- Network security
- Visualization of data
- Effective communication
- Problem-solving
Career Opportunities for Data Scientists in Tennessee with Advanced Degrees
Researchers in Tennessee have already recognized the importance of data science to a variety of industries and businesses in the state. In 2020, Middle Tennessee University rolled out a new Tennessee Data Initiative, designed to tie the school’s data science degree programs more closely to the current demands of the Nashville business community. Spearheaded by the director of MTSU’s Data Science Institute, Charlie Apigian – a man the Nashville Technology Council named its Data Scientist of the Year – the program promises to connect research and application at new levels for the local education and business communities.
The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
Data Scientist at Digital Reasoning in Franklin
The headquarters of this computing company, which is just over 15 years old and works with intelligence agencies and financial institutions across the country, hires Data Scientists to work with its customers, some of whom require a top-secret clearance. Through predictive modeling, machine learning and data analysis, data scientists will offer Digital Reasoning’s customers business insights. In addition, they will work with the customer to educate them on business intelligence and training their own staff.
Applicants must have a graduate degree in a technical or analytic field, along with excellent written and verbal communication skills.
Clinical Data Scientist at HealthTrust in Brentwood
This position is responsible for leadership and support in the development and implementation of HealthTrust purchasing group’s integrated clinical data system. Using visualization, programming, and mathematics, the data scientist works in partnership with customer businesses to discover opportunities and insights.
In addition to a graduate degree in a related area, this position required three years of relevant work experience and experience in clinical or research writing.
Big Data Software Engineer at KPMG in Knoxville
This position with one of the world’s leading auditing companies is responsible for designing and implementing systems to handle big data and data science needs for Fortune 1000 companies who are KPMG’s clients. Using technologies like Hadoop and SAP’s HANA, the big data software engineer translates business analytics problems into technical approaches in areas including market research, product development, risk management and public policy.
A graduate degree in a related field with two years of work experience is required for this position. Proficiency in Unix/Linux environments is also required.