A lot of empty space with a lot of diverse terrain creates an issue for geoscientists plumbing the natural miracles of the biology and geology of Wyoming, not to mention the government officials responsible for clearly delineating and managing all that rangeland, let alone assessing the resources that lie beneath its surface.
As of 2020, the state is looking at a quintessentially 21st Century solution, one in which data scientists will play a key role: conducting a state-sponsored aerial imagery program that will capture every square inch of the state in tremendous detail and make it available to state agencies for up-to-date property assessment data. High-quality, high-resolution imagery takes up a lot of storage space and generates significant management and retrieval problems, but it also opens up opportunities for automated assessment and analysis, something that falls to competent and well-trained data scientists.
Due to the extremely high demand for graduate-prepared data scientists, minimum experience requirements are being relaxed a bit as a way to bring more new talent into the field. With just six years of quantitative experience now being standard, versus the ten years previously expected, jobs aren’t just becoming more available, they’re becoming more accessible too. Salaries in this field are highly competitive, and they are on average much higher than in all other computer programming, statistics, and analytics-based careers. In fact, the 2020 Robert Half Technology Salary Guide shows starting offers for data scientists in Cheyanne coming in at between $87,000 and $148,000.
Some of the biggest hiring sprees are going on at the University of Wyoming and the Wyoming Game and Fish Department, as well as large accounting, insurance and financial services firms, and of course every industry from software development and cybersecurity to logistics and industrial manufacturing also rely heavily on data-driven insights. The most successful companies in any industry are the ones that are the best at digging out of complex problems buried under mountains of data, and more and more they’ve come to rely on graduate-educated data scientists to pull it off.
Preparing for a Master’s Degree in Data Science
As the demand for educated professionals grows, there are more and more new graduate programs coming online at top universities to meet the demand. Those programs typically rise from legacy programs in computer science, mathematics and statistics, bringing in professorial talent with a lot of relevant knowledge and experience.
But the competition for getting into those programs can be fierce, so it takes serious preparation.
Undergraduate Degree and Master’s Prerequisite Courses
Undergraduate students looking ahead to a master’s degree should think of their undergraduate education as a foundation for graduate studies and go with a major in a quantitative field.
Graduate program applicants should expect the following requirements for admission:
- At least three letters of recommendation
- The completion of an undergraduate degree program in a quantitative field like mathematics, computer science, programming, engineering, or statistics
- Undergraduate GPA higher than 3.0
- Completion of undergraduate courses in topics like advanced mathematics, linear algebra, computer programming, calculus, statistics, and other relevant prerequisites
Relevant Personal and Work Experience
Graduate programs typically look for candidates who have some experience in data science or a related field. Ideal program candidates typically possess the following:
- A few years of professional experience in a quantitative field like data science, analytics, information technology, or programming
- Basic knowledge of programming languages
- Understanding of fundamental concepts like linear algebra, algorithms, and data structures
- Other experiences that exhibit proficiency in computer programming, database management, or mathematics
Standardized Tests
Unless you can qualify for an entrance exam waiver based on an extensive work history and education background; graduate schools would typically want to see high scores on standardized assessment tests.
That means that individuals applying for data science graduate programs will almost always be required to take the GMAT or GRE exam. Both exams are commonly required in many different graduate studies application processes, so you won’t have any trouble finding practice and study materials for them. Free practice exams can be taken online in preparation for the GRE and the GMAT.
The exams are broad-based, which means they will look at all of your academic knowledge, not just the parts that are most important in data science. But admissions committees will definitely pay the most attention to how you do on the quantitative components of those exams, demonstrating your competency and capability in areas like:
- Algebra
- Geometry
- Reasoning and critical thinking skills
Online Data Science Bootcamps to Prepare For a Master’s Program or Entry-Level Employment
Another path into the world of data science, open to anyone, runs through a data science bootcamp. If you didn’t happen to get all the qualifications or work experience you needed at the undergrad level, you don’t have to start over entirely; these fast-paced, intensive, highly practical courses will cram a hands-on education in basic data science into a few weeks or months and burnish your CV for either employment or a data science grad program application.
Those types of programs will deliver coursework in topics such as:
- Big Data and Hadoop data stores
- SQL and relational databases like Postgresql and MySQL
- Python and R programming
- HTML5 and CSS for information visualization
- Social media mining
- Machine learning and artificial intelligence
It’s all delivered cohort-style, through a series of set projects that you and your fellow students engage in. The experience is designed to mimic the same types of challenges you would face in the real-world, and often using genuine datasets from the worlds of finance, healthcare, or government. Instructors from those very same realms ensure that it’s realistic and conveys the same kind of cutting edge tools and techniques that are used live in the field today.
Most bootcamps don’t just shove the training down your throat and kick you out onto the street, either. They come with career services teams to help you put together a CV and portfolio projects that showcase your skills and training… just the thing for a job application, or to impress a master’s admissions committee.
MOOCs and Bridge Programs to Paper Over Essential Skill Gaps
You may not need to go through a complete bootcamp experience to get your skills to the level required for data science graduate admissions, however. Instead, you might just find that some particular skills have gotten a bit rusty, or that you missed one or a couple of critical undergraduate theory courses that would lay the groundwork for your more advanced studies.
In those cases, you have a couple of other options to shore up your essential skill gaps that don’t take much time or cost a lot of money.
Bridge Programs – Students who meet all of the other program requirements but still find they are lacking in certain subjects can take part in a bridge program. Many students may not have extensive experience in one or more subjects like linear algebra or programming languages. These bridge programs are offered by the graduate school in order to help students fill in gaps in knowledge before the graduate program starts.
MOOCs – Massive Open Online Courses, or MOOCs, are another way for students to catch up on subjects in which they may not feel totally comfortable. These programs are not offered as part of the graduate school, but instead by private companies like IBM or Microsoft, or big-name universities like Stanford and MIT. They can be perfect for those who either need a refresher or a basic understanding of data science fundamentals, allowing asynchronous study online with elementary interaction with TAs, professors, or other students while giving you the flexibility to pick courses you need without major commitments in time or money.
Earning a Master’s Degree in Data Science
Completing a graduate degree program can result in a number of different degrees. The following degree programs can lead students to a career in data science:
- Master of Science in Data Science (MSDS)
- Master of Science (MS) in Business Intelligence & Analytics
- Master of Information and Data Science (MIDS)
- Master of Science (MS) in Business Intelligence
There are, of course, a pretty limited number of schools in Wyoming in the first place that offer advanced studies, and fewer still that have a master’s program in data science on tap. But students in Wyoming have no shortage of online programs available to them. Choosing an online graduate degree program can be a great choice for those who don’t live near campuses offering data science programs and for those looking for a more flexible course schedule.
Data science programs are typically comprised of about 32 credit hours. Full-time students are able to earn their degrees in between 18-24 months. For students who wish to complete their degree more quickly, there are options available that allow them to complete an accelerated program in 12 months. On the other hand, students who may already be working full-time may wish to only take one course per semester. For these students, there is a part-time option that must be completed over the course of as many as 32 months.
Curriculum and Core Coursework
Graduate courses will cover a wide range of topics, including many of the following advanced data science subjects:
- Experimental statistics
- File organization
- Database queries
- Data and network security
- Data visualization
- Advanced coding
- Data mining
- Applied machine learning
- Applied regression
- Time series analysis
Many graduate programs will also require students to complete a culminating capstone course at the very end of the degree program. These types of immersion experiences are commonly offered on a campus and offer students the opportunity to network with other students, meet the faculty, and explore additional learning opportunities as they engage in a realistic project designed to tie together all the information and techniques previously learned in the course of study.
Key Competencies and Objectives
Graduate programs are planned and operated with the purpose of preparing students for a data science career. These programs instruct students in the newest methods and technologies in order to give them relevant training and experience for their present or future jobs. Upon receiving their master’s degrees, students should become proficient in the following core competencies:
- Statistical analysis
- Interpreting, visualizing, and communicating data
- Familiarity with ethical and legal areas of the data science industry
- Understanding of programming languages like Python, SAS, R, GitHub, Shiny, and many others
- Proficiency in cryptology, network security, algorithms, and ciphers
- Data mining and management
- Computer programming
Career Opportunities for Data Scientists in Wyoming with Advanced Degrees
After earning a graduate degree, you’ll have no shortage of opportunities to put all that training into practice. The job market for data scientists in Wyoming is growing, and the best educated graduates are in high demand.
That kind of demand pushes salaries up, which can offer a real advantage in a low-income state like Wyoming. The median annual wage in the state for 2019, according to the Bureau of Labor Statistics, was $48,630. Comparatively, Robert Half’s 2020 Salary Guide in Technology puts even low-end starting offers for data scientists in Cheyanne at nearly $90,00 per year, with those at the top range of the field, with the highest education and experience, commanding nearly $150,000.
The following job listings have been provided as illustrative examples only, and should not be interpreted as job offers or assurances of employment.
Senior Data Analyst with William E. Wecker Associates, Inc. at Jackson – Candidates applying for this role are required to hold a graduate degree or Ph.D. in statistics, applied mathematics, data science, or a related field. Ideally, applicants should be very familiar with SAS, Stat, R, S-Plus, Python, and Mathematica, and other similar packages. The Senior Data Analyst has a wide range of responsibilities including analyzing complex data sets and working with other project managers and consultants to solve problems for clients.
Data Scientist/Analyst with Cheyenne Regional Medical Center at Cheyenne – This position is ideal for someone looking to start out in the data science career field. Candidates must have completed a bachelor’s degree in a relevant field and be familiar with data management, analysis, statistics, information technology, and the healthcare system. Ideally, applicants should have two years of experience involving data science.
Management and Data Science Analyst with U.S. Department of Veteran Affairs at Sheridan – An individual in this role should either have extensive experience in a data science or data analyst position, or have completed at least three years of a graduate level degree program or higher. This role requires the individual to become expertly acquainted with QSV Data Informatics in order to interpret the outcome information related to the Veterans Affairs Healthcare Department.