Las Vegas has been established as one of the leading centers of technological innovation since 1978 when it hosted its first Consumer Electronics Show (CES).
- 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
Since the advent of data science Vegas has used its status as the leading conference city to host shows such as:
Big Data Innovation Summit – Taking place in January 2017, this event presents a detailed overview of how to make customer data actionable. Featured speakers hail from top positions in global public and private institutions, including:
- Vice president of data analytics from Stanley Black and Decker
- US Department of Commerce’s chief data officer
- Chief data scientist from the leading big data company Banjo
- Speaker from the World Economic Forum’s Global Agenda Council
International Conference on Data Mining (DMIN) – Taking place annually in the summer, this conference is part of the World Congress in Computer Science which also take place in Las Vegas. DMIN features speeches by the world’s top data scientists, with 2016’s topics including data mining algorithms, machine learning tasks, data mining applications, and data mining integration.
As an international hub for data scientists, Nevada attracts some of the most qualified professionals in this field to work in healthcare startups like Lucine, based in Henderson, and its multi-billion dollar hospitality and tourism industries. Places like the Mirage Casino and Hotel use data scientists to develop models that predict customer behavior, which allow casinos to maximize the relevant services they provide. Data scientists may even want to use their innovative skills to develop their own algorithms to strike gold as bookies.
Navigating the opportunities for data scientists in Nevada is done best with a master’s degree in this field as a guide. Data science graduates know how, where, and when they can best apply their skills at any of Nevada’s business opportunities.
Preparing for a Master’s Degree in Data Science
Graduate programs in data science are selective when admitting students, and choose those who can demonstrate a strong academic and professional background.
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.
Academic 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, hacking, math and statistics, database administration, and data mining
- Analytical reasoning ability
- Knowledge of data structures, algorithms, and analysis of algorithms
- Knowledge of programming languages, especially Python and Java
Nevada offers many opportunities for prospective students to gain relevant work experience:
As the host of some of the most important conferences in the field of data science each year, prospective students 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 through employment 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 such as 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
Passing exams such as the GRE and GMAT is another way to demonstrate key competencies in the field of data science. Graduate programs will specify if they require either of these examinations, which students should aim to pass in the top 15th percentile. Scoring well on the quantitative section of these exams is vital, however applicants should also score well on the verbal and writing sections as communicating is also important in data science.
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 students’ 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.
Bridge Programs and Massive Open Online Course (MOOCs) for Prospective Graduate Students that Need to Bridge Gaps in Functional Knowledge
Student’s whose background lacks certain fundamentals can make up for these 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 augment a student’s background. These are online forums 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
While prospective students can find undergraduate and graduate programs in mathematics, statistics, computer science, engineering, and other fields related to data science, Nevada is still catching up with this newly established field and does not yet offer any programs specifically in data science.
However, students have several options of data science master’s programs that are offered online. 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
- Network and data security
- Experiments and casual inference
- Machine learning and artificial intelligence
- Information visualization
- Statistical sampling
- Ethics and law for data science
- Data mining
- Quantifying materials
- Scaling data – macro and micro
- Advanced managerial economics
- Applied regression and time series analysis
- Visualization of data
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 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
- Apply programming languages like GitHub, SAS, Python, Java, and Shiny by Rstudio
- Conduct database queries
- Be familiar with hash algorithms, cyphers, and secure communications protocols
- 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 match this to the anticipated or stated preferences of established customers based one 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, completed in March 2016. Employers often list the requirement of a master’s degree in, “a quantitative field,” because many of today’s candidates did not have the option of obtaining a master’s degree in data science at the time they completed their graduate education.
Health Research Analyst I with HMS (Health Market Science) 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)
- Incumbents are responsible for 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