Online Master's in Data Science for Jobs in Florida

Florida and real estate go together like chocolate chip cookies and milk. A state with consistently warm, sunny weather, miles of coastline, and all kinds of retirement money floating around ensures a lot of transactions… but low-lying areas and hurricanes can make investments dicey.

Enter big data. Companies like CoreLogic and CRE Models in St. Petersburg crunch the numbers from hundreds of publicly available data sources, giving investors and agents the insights they need to engage in transactions with peace of mind and profitable returns.

The state also has some more traditional high-tech opportunities for data science. Citrix Systems, located in Fort Lauderdale, collects information on all of its tech support cases, allowing the company to determine how long it takes to resolve a problem, which then informs new strategies on how to zero in on new and better ways to fix issues faster. Boca Raton-based firm Modernizing Medicine recently received a massive investment from IBM to use its Watson cognitive computer to collate data in an effort to gain new insights from dermatological research.

These ambitious projects represent the kind of thing happening all over Florida, as companies strive to gain new insights and derive purpose from their massive, scattered, and unorganized data assets.

Data is flowing at breakneck speed throughout every area of our global economy. Companies now have access to a boggling volume of transactional data, capturing trillions of bytes related to customers, operations, and supply chains. What we know now is that harnessing the power of big data is the foundation for realizing productivity, growth and efficiency in the 21st century.

All that adds up to make the technically-capable and creatively-minded master’s-prepared professionals that can take messy, incomplete and unstructured data and transform it into something corporations can use to inform new and innovative strategies the soothsayers of modern business.

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

In addition to coming to the table with an impressive undergraduate GPA and a strong showing in related courses, applicants to data science master’s programs are also expected to demonstrate proficiency through equally impressive scores on graduate entrance exam and a resume that highlights their coding and ethical hacking skills, and their proficiency in data analytics.

Undergraduate Degree and Masters Prerequisite Courses

Master’s programs are in great demand, and are bombarded with applicants. You need to have ways to stand out from the pack if you want to maximize your chances of being accepted. Some colleges and universities require candidates entering a master’s degree in data science to hold an undergraduate degree in a related major:

  • Engineering
  • Physics
  • Computer Science
  • Applied Statistics
  • Math

Even when a particular undergraduate major is not specified, you’ll often need to show that they have completed specific undergraduate prerequisite courses in:

  • Programming languages (C++, Python, JAVA, R)
  • Linear algebra
  • Data structures
  • Algorithms and algorithm analysis

Other requirements for entering a master’s degree program in data science typically include:

  • Minimum undergraduate GPA
  • Admissions interview
  • Letters of recommendation
  • GRE/GMAT minimum scores in quantitative reasoning
  • Undergraduate prerequisites/completion of bridge courses

Prepare for a Masters Degree, or Bypass it and Go Straight Into Your Career With a Data Science Bootcamp Located in Lake Mary or Online

A master’s degree isn’t your only option to get into data science, however. A data science bootcamp gives you a faster, less expensive route into the profession, or can be used to burnish your skills in preparation for a master’s program application that will make it to the top of the stack.

A bootcamp is every bit as intense as it sounds. Usually offered in a compressed period of time, taught cohort-style with a group of peers at the same stage of skills development, bootcamps focus almost exclusively on practical knowledge and training in the real-world tools and methods data scientists use every day.

Bootcamps exist to cater to students at different skill levels, with options for recent high school grads all the way up to post-graduate options for analysts with years of experience. Something near the entry level, like the University of Central Florida Data Analytics and Visualization Bootcamp, available at a campus location in Lake Mary and online,  is both relatively easy to get into and offers good preparation for either a master’s program or a career in data science.

Although two years of experience and a bachelor’s are recommended for getting into these kinds of 24-week, part-time programs, all you really need is a GED and to be 18 years of age. Working on evenings and weekends, either on campus or online, you’ll get a thorough run through the fundamentals and more, working with the same expert instructors teaching the data science programs offered at the university. Expect courses covering:

  • HTML and CSS for visualizations
  • Statistical modeling and forecasting
  • Databases like PostgreSQL, Hadoop, and MongoDB
  • Python and statistical libraries like NumPy

It all happens using real-world datasets and projects to develop the exact skills the job market demands. In the process, you’ll build a portfolio you can use to impress admissions committees or future employers. Career services teams help you with the latter if that’s your aim, working on resume polishing and interview skills to make sure you’re job-ready as soon as you graduate.

Bridge Programs and Massive Open Online Course (MOOC) Options to Fill Gaps in Functional Knowledge

Graduate schools may grant admission to highly qualified and desirable students even if they have some gaps in functional knowledge as it relates to programming or statistics and math. To bring you up to speed, bridge courses are often made available, allowing you to gain mastery of foundational skills before transitioning to graduate level coursework. Bridge courses are typically structured as:

  • Fundamental Mathematics Bridge Courses, with courses including algorithm analysis, linear algebra and data structures
  • Programming Bridge Courses, with courses in programming languages such as Java, C++, R and Python

Massive open online courses (MOOC) serve as another option for individuals seeking to master the math, statistics and programming skills necessary to study data science at the graduate level. Many students choose to complete MOOCs before applying to a master’s degree program, which eliminates the need to take bridge courses and allows them to dive directly into their master’s curriculum. MOOCs allow students to take courses in specific areas of math, database management and in specific programming languages, all completely online.

Relevant Personal and Work Experience

In addition to a bachelor’s degree in a related major, many data science master’s programs expect candidates to have a background in an area like programming, data mining, business analytics, or data analysis, sometimes requiring between 5 and 7 years total related job experience. Examples of employers and positions in Florida that would provide relevant work experience include:

  • Harris Corporation, located in Melbourne, employs data scientists to develop custom models and algorithms that analyze large data sets to detect threats information systems integrity. These junior-level positions require a bachelor’s degree in applied math, engineering, statistics, or a similar field.
  • Darden, in Orlando, employs associate data scientists to collect, manage, analyze, and interpret large volumes of data while ensuring a high level of data quality. Associate data scientists must have a bachelor’s degree in economics, statistics, applied mathematics, computer science, management information systems, or a related field.

Preparing for Success on the GRE/GMAT

Most master’s degree programs in data science require students to score in the 85th percentile on the quantitative section of either the GMAT or the GRE. To achieve the best outcome on these exams, candidates should take advantage of the exam prep materials available to them:

GRE Revised General Test

The GRE Revised General Test includes a quantitative reasoning section that measures a candidate’s ability to:

  • Understand and interpret quantitative information
  • Solve problems using mathematical models
  • Apply basic skills and elementary concepts of:
    • Data interpretation
    • Arithmetic
    • Algebra
    • Geometry

Test takers may review the types of questions found in the GRE’s quantitative section or study sample questions with rationales and tips here.

GMAT Examination

The GMAT examination includes a quantitative reasoning section, consisting of 37 questions concerned with data sufficiency and problem solving. The quantitative section measures a candidate’s ability to analyze data and draw conclusions using reasoning skills.

Test takers may find a number of study tools, including sample questions and official guidebooks, here.

Earning a Master’s Degree in Data Science in Florida

Master’s degrees in data science fall under a number of different titles:

  • Master of Information and Data Science (MIDS)
  • Master of Science in Statistics: Data Science
  • Master of Computational Data Science
  • Master of Science in Data Science

Although a number of colleges and universities in Florida offer master’s degrees in data science, the proliferation of online programs are giving students the opportunity to earn an advanced degree on their own time. Students of online programs may still need to attend the university or college on a few occasions to complete an immersion experience.

Online programs routinely offer part-time or accelerated options to accommodate students’ diverse scheduling needs:

  • Full-time programs take between 18 and 24 months to complete
  • Part-time programs take about 32 months to complete
  • Accelerated programs take about 12 months to complete

Curriculum and Core Coursework

The curriculum of a master’s degree in data science consists of about 30 credits of coursework, including electives that round out this multidisciplinary education. The core curriculum focuses on:

  • Ethics and privacy
  • Statistical analysis
  • Research design
  • Communicating results
  • Storage and retrieval
  • Data Cleansing
  • Mining and exploring
  • Data visualization

Other coursework assures graduates are proficient in:

  • Data mining
  • Experimental statistics
  • Exploring and analyzing data
  • Applied machine learning
  • Statistical sampling
  • File organization and data management

You can customize your electives and other studies to focus on any of these particular aspects of data science, or other subjects you feel will drive market demand by the time you are graduating.

Program Competencies and Objectives

A master’s degree in data science is designed to prepare graduates to work with unstructured data and derive insights using the latest tools and analytical methods. Competencies draw on insights from a number of areas of study, including the social sciences, computer science, management, the law, and statistics.

Graduates of master’s degree programs in data science are prepared to:

  • Ask relevant questions
  • Visualize and analyze data
  • Interpret results
  • Retrieve permanent data
  • Communicate findings
  • Understand the ethical and legal responsibility of the discipline

Career Opportunities for Data Scientists in Florida with Advanced Degrees

Data scientists are said to make discoveries while swimming in data. They are able to identify rich data sources, join them with other incomplete—often messy—data sources and bring structure to them so as to make further analysis possible.

The following job descriptions 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 Florida.

Data Scientist, Accent Technologies: Melbourne, FL

Responsibilities:

  • Work with big data from multiple sources to create integrated views that can be transformed into relevant insights for decision making
  • Structure project plans, set expectations, and communicate with internal business partners
  • Present and communicate result sets in a way that’s appropriate for target businesses

Qualifications:

  • Must be able to work with large data volumes
  • Must have experience with data analysis tools available in the Microsoft Azure platform
  • Must have database architecture/warehouse experience
  • Must have experience collecting, cleansing, and converting data, data mining, data mapping, and data integrity
  • A master’s degree or higher
  • At least 7 years of experience pulling data from databases, running assessments on the data, cleansing data, and turning results into valuable business predictors

Senior Data Scientist, Altamira: Tampa, FL

Responsibilities:

  • Work in a dynamic and collaborative environment using established tools, technologies, techniques, and processes
  • Evaluate the operational usefulness of technology development and demonstrate the capability of the technology through test and evaluation

Qualifications:

  • Knowledge of Defense intelligence all source analysis
  • Knowledge of Operations research/systems engineering and analysis
  • Knowledge of Intelligence community database and tools
  • At least 10 years of experience or an advanced degree
  • At least 3 years of experience in the intelligence community

Data Scientist, 1st Merchant Funding: Miami, FL

Responsibilities:

  • Delivering into production key machine learning innovations
  • Setting priorities, mentoring, developing talented staff and providing key input into work product
  • Building products from the ground-up, including developing product strategy with a special focus on risk

Qualifications:

  • Minimum of a master’s degree in computer science, machine learning, operational research, statistics, or a related quantitative field
  • At least four years of hands-on experience in predictive modeling and analysis

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