Out here in the middle of all nature’s grandeur, Big Sky Resort has discovered that big data is a very big deal when it comes to forecasting conditions.
Snowfall, temperature and the conditions they create, including the type of snow (wet, dry, heavy, light powder), all translates directly into revenue and economic survival for resorts like Big Sky.
Crunching numbers to develop useful models is complicated stuff, and no less so when applied to problems like snowfall and conditions forecasting, peak seasonal visits, traffic, and how passable the roads are. Factors considered in model development include:
- El Nino or La Nina years in the North Pacific – in these years snowfall ranges between 97-112 percent of normal
- Elevation of weather stations – 8,900 feet, 9,000 feet, and 9,600 feet
- Wind speed and direction
- Snowfall and snow-water equivalent
- Temperature – average 25 Fahrenheit
- Customer behavior – a factor that requires an entirely new predictive model and data
The ability to make accurate models that predict and respond to customer preferences translates into the potential for millions in profit at Big Sky, as well as better customer satisfaction – all made possible by data science.
They may not be as important as the conditions on the slopes, but other vital sectors in Montana like healthcare, government, and transportation are equally as reliant on the skills of qualified data scientists these days. It’s all reflected in the growing demand for these professionals throughout the state, and the growing number of graduate schools offering top flight master’s degrees in data science to help supply the talent needed to keep up with that demand.
Preparing for a Master’s Degree in Data Science
As one of the most competitive academic fields out there, data science master’s programs prefer would-be students to come from a strong background, both academically and professionally. You need to have the right base skills to take advantage of the advanced education they offer, and they are not shy about rejecting candidates who can’t meet those high standards.
Undergraduate Degree and Master’s Prerequisite Courses
Ideally students start preparing for master’s-level studies in data science as undergraduates. You can do this by earning a bachelor’s degree in a quantitative field like applied mathematics, statistics, engineering, or computer science. A student’s GPA should be at least 3.0, and undergraduate coursework should include prerequisites like:
- Linear algebra
- Calculus I and II
- Quantitative methods
- Statistics
- Programming languages
Relevant Personal and Work Experience for Admissions
Prospective students are also preferred – if not required – to come from a professional background that includes a handful of years of relevant work experience in a quantitative field.
In Montana, relevant professional experience could potentially be gained through jobs like these:
- With an important hub in Billings, the Burlington Northern Santa Fe Railway hires data analysts to determine the most efficient transportation routes based on factors such as weight, load size, train car availability, and personnel availability
- Medical providers such as Saint James Healthcare in Butte and Saint Peters Hospital in Helena employ data technicians and infection control analysts to ensure the best patient outcomes
- Government agencies such as the City of Great Falls and State of Montana employ network analysts and IT maintenance professionals to analyze problems, troubleshoot solutions, and maintain network security
Applicants can supplement their professional experience with personal experience in related subjects such as:
- Coding
- Database administration
- Mathematics or statistics
Preparing to Score Within the 85th Percentile on the GRE/GMAT Exams
With enough relevant work experience, you can usually qualify for a waiver of the entrance exam requirements. But if not, graduate programs will require one of two exams: the GRE or GMAT.
In either case, students should aim to score in at least the 85th percentile on the quantitative sections. Candidates can prepare for these exams with a variety of pre-testing resources.
The Graduate Record Exam (GRE) revised general test’s quantitative reasoning section covers the following subjects:
- Data analysis, covering topics like statistics, standard deviation, interquartile range, tables, graphs, probabilities, permutations, and Venn diagrams
- 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
Students can prepare for the quantitative reasoning section with resources such as:
- Educational Testing Service’s (ETS) Math Review
- Princeton Review’s GRE practice exam
- Veritas Prep’s GRE practice exam
The GRE is also comprised of two relevant subject area tests, covering the following topics:
Physics – physics test practice book
- Classical mechanics
- Electromagnetism
- Optics and waves
- Thermodynamics
- Statistical mechanics
- Quantum mechanics
- Atomic physics
- Special relativity
- Lab methods and specialized topics
Mathematics – mathematics test practice book
- Calculus
- Algebra
- Introductory real analysis
- Discrete mathematics
- Probability, statistics, and numerical analysis
The Graduate Management Admissions Test’s (GMAT) quantitative section covers topics that relate to data analysis. One of the four main parts of the GMAT, the quantitative section is comprised of 37 questions to be completed in 75 minutes. All questions pertain to data sufficiency and problem solving. Students can prepare for the GMAT using resources such as those provided by the Princeton Review and Veritas Prep.
Online Data Science Bootcamps to Build Skills For Your Master’s Program or for Direct Entry into the Industry
All that testing and experience building can take quite a long time, but there is another option for candidates who are in a hurry and don’t mind an intensive challenge to get the skills and knowledge needed to successfully enter a master’s program: enroll in a data science bootcamp.
Bootcamps are actually offered for many different purposes and levels of expertise in data science, some of them aimed at people already in the industry in high-level roles looking for a new challenge and an opportunity to add something new to their skills portfolio.
But getting the basics down as a way to prepare for a master’s program is better served by an entry-level bootcamp, one that delivers core skills and information such as:
- Hadoop data stores and Big Data analytics
- Machine learning and Artificial Intelligence
- SQL databases and language use
- R and Python programming
- Data visualization techniques
- Specialized analytics libraries like Numpy
Unlike college courses, bootcamps focus on learning by doing, and are less about exploring potential than drilling these skills into your brain quickly. You do this through group projects using live data on realistic business problems, all while being advised and supervised by instructors with plenty of actual experience in the field.
A dedicated career services team is waiting when you get toward the end of the course to help you prepare for putting the best possible face on your experience, through resume prep, portfolio building, and interview coaching. Although it’s designed to help people get direct employment in the field, you might find that it works just as well for getting you a spot in the master’s program you prefer.
Bridge Programs and Massive Open Online Course (MOOC) Options to Fill Gaps in Functional Knowledge
Data science graduate programs may require their admitted students to complete a bridge program before taking core-subject courses. Bridge programs catch students up with a core data science subject – for example engineering, programming, mathematics, statistics, or computer science – if they have any holes in their academic history. This can be either a single course at the undergraduate level, or a series of several, and will be offered directly through the university as a way to make it easy for you to transition to your graduate courses. Examples of the subjects covered can include:
Fundamental bridge programs:
- Algorithms
- Linear algebra
- Data structures
Programming Bridge Programs
- Programming in languages like Python, JAVA, C++, and R
MOOCs are online courses made up of video lectures by preeminent professors, sample problem sets, and interactive user forums. MOOCs allow students to interact with each other as well as with participating teaching assistants and professors over the web. Students can find MOOCs in any number of subjects, including data science, mathematics, programming languages, physics, and engineering. While MOOCs may be academically rigorous, their online, asynchronous nature also makes them ideal for anyone with discipline and self-direction who needs to fill in skills gaps before applying to a master’s program.
Earning a Master’s Degree in Data Science
Like most other states in the nation, Montana is still catching up with the emerging field of data science. Although it’s a small state with a limited number of advanced universities, students can find some relevant resources:
- Bachelor’s of Science (BS) in Statistics, with a concentration in data science – Butte
- Master of Science in Data Science — Missoula
- Big Data Analytics Certificate – Missoula
- Cyber Security Professional Certificate (undergraduate) – Missoula
But just as with bootcamps, many master’s degrees are available online today. You’re not restricted to only the local selection of schools. Colleges and universities offering a master’s in data science online provide students with a convenient class schedule and flexible completion options:
- 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 certificate – can be completed in one to two semesters; approximately 12-15 semester credits
Graduate programs are approximately 30 semester credits, and students can choose from several types of relevant degrees:
- Master of Science (MS) in Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Data Mining and Applications Graduate Certificate
- Online Graduate Certificate in Data Science
Core Curriculum
Graduate degrees in data science cover core topics such as:
- Experimental statistics
- Data research design and applications
- File organization and database management
- Data storage and retrieval
- Network and data security
- Machine learning and artificial intelligence
- Information visualization
- Statistical sampling
- Ethics and law for data science
- Data mining
Programs culminate with an immersion experience, where students work on an actual project in teams to achieve concrete goals. In addition to evaluating core data science principles, professors and potential employers also rate students on their ability to work together.
Key Competencies and Objectives
Students who earn their master’s degree in data science should be able to exhibit these core competencies and apply them in the workplace:
- Teamwork to achieve specific goals
- Interpretation and communication of results
- Development and implementation of sophisticated data analyses
- Ability to conduct association mining and cluster analysis
- Data survey management and implementation
- Development of innovative design and research methods
Career Opportunities in Montana for Graduate-Prepared Data Scientists
Whether it’s analyzing wildlife health and numbers in Glacier National Park or developing models for snowfall and snow quality, data scientists find their services just as necessary to small startups in Bozeman as they are for massive federal agencies like the Bureau of Land Management. More and more, industries around the country are coming to rely on data science to conduct their core business, and as the growth continues, the pipeline for accomplished graduates hasn’t kept up.
That means good money for those students who manage to complete master’s programs in data science. According to Burtch Works, in their 2019 salary study for Data Scientists and Predictive Analytics Professionals, most quantitative professionals come from a traditional educational background holding either a master’s or a PhD in the field. And they are well-paid for it, with starting salary offers in Billings in the range of $87,000 and $149,000, according to the 2020 Robert Half salary guide.
The following job listings are shown as illustrative examples only and are not meant to represent job offers or an assurance of employment.
Manager II of Predictive Analytics Application Development with BNSF in Billings
- Working within Burlington Northern Santa Fe Railway’s predictive analytics department, this role involves using big data to improve the efficiency of transport
- Job duties include developing a technology strategy for predictive analytics, developing cross-functional relationships with business intelligence and information management, and creating an environment that fosters innovation
- Applicants must have at least five years of relevant big data work experience and an undergraduate degree; a master’s degree in a relevant field may substitute for some years of work experience
Biological Science Technician (GS-07) with the Bureau of Land Management in Missoula
- Responsible for conducting inventories, studies, and data analysis to create an informed report for the Department of the Interior
- Duties include conducting marine life inventories and water quality samplings, plus the analysis and aggregation of data from these fields
- Applicants must have at least the equivalent of one year of graduate studies in a field such as statistics, mathematics, physics, or biology
Software Engineer with Neuralynx in Bozeman
- Involves working with Neuralynx’s engineering group to create software that helps scientists understand how the brain works
- Candidates must be detail oriented and able to communicate well, as they carry out duties that include software test automation, application of current technology, and understanding of clinical research
- Applicants should have the equivalent of a master’s degree in computer engineering or computer science