There may be no greater concentration of actuarial expertise among the general public than the betting floor of the average Vegas sportsbook on Super Bowl Sunday.
Grinding the odds in realtime, and backing them up with real money, even gamblers who don’t have any formal training in data analysis are performing plenty of calculations with enormous real-world impact in a betting market that approaches $270 million each year. That’s just on the legal side… the American Gaming Association reports that the actual global transaction total in bets for the game is more like $7 billion.
- SMU - Master of Science in Data Science - Bachelor's Degree Required.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
That kind of money gets the attention of casino operators, and they’ve been gobbling up data science talent and leveraging big data to hone their share of the take from both sports betting and other odds-based gaming. The annual Sports Betting Community Digital Summit brings together professionals working on hard problems in meeting regulations, blocking problem gamers, and building fan engagement online and offline. Big money is at stake, and big paychecks go out to the most highly qualified data scientists… up to $250,000 per year in 2020 for those in management, according to analytics firm Burtch Works.
But Nevada isn’t all about gambling. As an international hub for data scientists, Nevada attracts some of the most qualified professionals in the field.
A master’s degree in the field is the best guide for navigating the opportunities in Nevada..
Preparing for a Master’s Degree in Data Science
Graduate programs in data science are seeing a lot of applicants these days. That makes them pretty selective when it comes to admitting students, and so they typically choose those who can bring a strong academic and professional background to the table.
Undergraduate Degree and Master’s Prerequisite Courses
Academically, programs are looking for students who have majored in a quantitative field like applied math, computer science, statistics, or engineering during their undergraduate studies. Students should earn a cumulative GPA that is not less than 3.0.
Transcripts should reflect a course history that includes prerequisite courses like:
- Calculus I and II
- Quantitative methods
- Linear algebra
- Programming languages like Java and Python
Relevant Personal and Work Experience
Work experience is another important qualification that prospective students must develop. Graduate-level data science programs typically require students to have a professional background that includes:
- A minimum of five years of technical work experience in data science
- Work experience demonstrating quantitative abilities in areas such as coding, math and statistics, or database administration
- Analytical reasoning ability
- Knowledge of data structures, algorithms, and analysis of algorithms
- Knowledge of programming languages, especially Python and Java
If you already live in Nevada, you can leverage an advantage you have over students in states that aren’t major professional conference destinations. As the host of some of the most important conferences in the field of data science each year, prospective students in the state can get involved with the dozens of leading data science companies that participate in events like the International Conference on Data Mining and the Big Data Innovation Summit.
Relevant work experience can also be gained working with some of the state’s largest employers:
- Collecting and analyzing customer information to improve services with some of Nevada’s leading hospitality establishments like Wynn Las Vegas, the MGM Grand Hotel/Casino, and Bellagio
- Working to maintain computer network security with some of the state’s largest closed networks for employers like the Clark County and the Washoe County school districts
- Working in data collection or management with a Nevada startup tech firm like Lucine Health Sciences, which specializes in aggregating medical data
Demonstrating Basic Proficiencies by Scoring Within the 85th Percentile on the Quantitative Sections of the GRE/GMAT
Graduate programs will specify if they require either the GRE or GMAT, which students should aim to pass with a score in the top 15%. Scoring well on the quantitative section of these exams is vital, but applicants should also score well on the verbal and writing sections since communication skills are also very important when it comes time to present company leaders with the valuable insights drawn from big data sets.
The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section evaluates the following:
- Arithmetic topics including integers, factorization, exponents, and roots
- Algebraic topics such as algebraic expressions, functions, linear equations, quadratic equations, and graphing
- Geometry, including the properties of circles, triangles, quadrilaterals, polygons, and the Pythagorean theorem
- Data analysis, covering topics like statistics, standard deviation, interquartile range, tables, graphs, probabilities, permutations, and Venn diagrams
Students can prepare for the quantitative reasoning section by reviewing Educational Testing Service’s (ETS) Math Review. Full GRE practice exams are available through the Princeton Review and Veritas Prep.
The Graduate Management Admissions Test’s (GMAT) quantitative section evaluates skills in data analysis. The quantitative portion is comprised of 37 questions that must be completed in 75 minutes. All of these questions pertain to data sufficiency and problem solving.
Data Science Bootcamps to Prepare For Master’s Program Applications and Acquire Hands-On Skills
If you didn’t happen to pick the right path to meeting the stringent requirements for a master’s in your bachelor’s program, you still have options. If you don’t have a lot of time, or a lot of money, then one of your best bets will be enrolling in a data science boot camp.
A bootcamp is just as hardcore as it sounds. Once dominated by small, private companies, they are now increasingly offered by major universities. The concept is simple: cram as much intensive learning into a period of a few weeks or months as any student can reasonably handle. Working with fellow students, you’ll contribute to projects using real-world data in exercises designed to replicate the same kinds of demands you would get in a professional workplace, using cutting-edge tools like:
- Numpy Python analytics libraries
- Hadoop data stores
- Postgresql or MySQL databases
- HTML or CSS data visualization tools
- Machine learning and AI processing techniques
It all happens with expert instructors with real-life experience guiding you along the way. Typically, the programs will also offer a career services component, designed to help prepare you for interviews, line up potential employers, and build your portfolio and resume out for the best presentation.
Some bootcamps are aimed at individuals who are already working in the data science field, helping them hone their expertise on highly specialized subjects. But if you are looking for an entry-level, introductory bootcamp as a way to get some foundational career training or to simply polish up your master’s application, you can consider part-time, online programs, like these two options available to Nevada residents:
With one of these options, you’ll enjoy all the benefits that come with attending a highly reputable university and studying under professional instructors. With a rare part-time schedule, they last six months, but allow you to participate on evenings and weekends to fit your existing work schedule.
Bridge Programs and Massive Open Online Course (MOOCs) for Prospective Graduate Students that Need to Bridge Gaps in Functional Knowledge
Students whose background lacks certain fundamentals, but otherwise meet all the right admissions standards, can sometimes make up for any holes in their transcripts through bridge programs. These are university classes in a specific subject assigned to students after they are admitted to a graduate program in data science. Once students complete the bridge program they will have the requisite foundation to go forward with core data science subjects at the graduate level.
Universities typically offer two types of bridge programs:
- Fundamental bridge programs in subjects like linear algebra, algorithms, analysis of algorithms, and data structures
- Programming bridge programs that focus on languages such as Python, Java, and C++
MOOCs (massive open online courses) are an informal supplemental option to build your skills. These are online classes, often offered by major universities, in specific subjects complete with fellow students, sample problem sets, and recorded lectures from leading professors. While not formally recognized for academic credit, MOOCs can be used to bolster personal experience qualifications. Subjects covered include data science engineering, programming languages, statistics, and mathematics.
Earning a Master’s Degree in Data Science
Prospective students can find undergraduate and graduate programs in mathematics, statistics, computer science, engineering, and other fields related to data science on the ground in Nevada. However, students have several options for data science master’s programs that are offered online, as well. These are a popular choice because of the flexible academic schedule they provide.
Online programs may give students additional options when it comes to completing the approximately 30 semester credits that make up a traditional master’s degree in data science:
- Traditional completion time – approximately 18 months or three semesters
- Accelerated completion – completion in as little as 12 months or two semesters
- Part-time – completion in as much as 32 months or five semesters
- Graduate certificates – completion in one to two semesters
The most relevant data science programs result in credentials such as:
- Master of Science (MS) in Data Science
- Master of Information and Data Science (MIDS)
- Master of Science (MS) in Applied Statistics with a focus on data mining
- Graduate Certificate in Data Science
Core Curriculum and Immersion
Core curriculum topics covered in a data science master’s program include:
- Experimental statistics
- Data research design and applications
- File organization and database management
- Data storage and retrieval
- Machine learning and artificial intelligence
- Information visualization
- Statistical sampling
- Ethics and law for data science
- Data mining
- Applied regression and time series analysis
Programs culminate with an immersion experience where students are assigned to teams that have a specific goal to achieve. The immersion segment allows students to apply the principles of data science they have learned to real-world challenges. It also gives professors and prospective employers a chance to observe students’ competencies and their ability to work as part of a group.
Key Competencies and Objectives
Anyone with a master’s degree in data science should be able to exhibit these key competencies:
- Participate in teams to achieve specific goals
- Interpret and communicate project results
- Develop and conduct sophisticated data analyses
- Conduct association mining and cluster analysis
- Run an analysis of survey data
- Develop innovative design and research methods
Career Opportunities in Nevada for Data Scientists with Advanced Degrees
There are many opportunities for data scientists throughout Nevada, from its bustling cities to its resource-rich areas of natural beauty.
Located in Henderson, Lucine Health Sciences is a good example of data science being applied in Nevada. Lucine improves healthcare simply by providing relevant data to consumers, physicians, and industry experts. Lucine’s data scientists develop tools to aggregate healthcare data and align it with the preferences of established customers based on their profiles and activity.
Data scientists also work with employers like the Bureau of Land Management to survey Nevada’s natural mineral wealth for future mining prospects. This involves incorporating the newest advances in mineral survey techniques with big data algorithms that can predict the best places for future exploratory work based on a number of variables.
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 Nevada.
Health Research Analyst I with LexisNexis Risk Solutions in Las Vegas
- This position involves statistical and epidemiological analyses of healthcare data
- Duties include designing healthcare databases, using and developing analysis software, developing data cleaning criteria, and creating models to present conclusions to management
- Applicants can qualify for this position with a master’s degree in applied statistics or an equivalent field plus related work experience
Senior DevOps Engineer with a Las Vegas company served by IT Strategic Staffing
- This position involves working on contracts to develop machine learning applications for large-scale data science operations; duties include using distributed computing such as NoSQL, Hadoop, and Cassandra, applying complex problem solving skills, and using shell scripting on Unix/Linux
- While a BS in computer science or equivalent field plus five years of related work experience is a requirement for this position, candidates can potentially qualify with a master’s degree in data science and three years of related work experience
System Engineer III with the Sierra Nevada Corporation in Sparks
- This position involves working with the company’s enhanced flight vision systems (EFVS); designing a software integration system that allows for aircraft operations in low-visibility environments by combining analysis of data such as 3D imaging radar, infra-red video, LIDAR, and other sensor data
- Applicants can qualify for this position with a master’s degree in systems engineering or a related technical field and less than five years of professional work experience