Oklahoma is home to both Fortune 500 companies and hot new tech startups that are counting on the revolutions in data science to build their success. Both Tulsa and Oklahoma City have vibrant tech scenes, with Business Insider ranking Oklahoma City as the top city in the country for tech talent.
But it may actually be humble government agencies that come up with the next big thing in data science in the Sooner state.
A 2020 report from the Partnership for Public Service and Microsoft dives into the ways that maturing technology in data science can improve disaster resilience and response and showcases opportunities in Oklahoma to revolutionize disaster prediction and management. The report looks at earthquake detection tools currently in use within the Oklahoma Geological Survey, similar to the data-intensive applications developed by researchers from Harvard and MIT.
Those systems don’t build themselves. It takes skilled data scientists to develop that AI and effectively use it to help forecast quake damage and response needs. Data scientists evaluate the data sources, put together theories, and write the code that can actually make use of the millions of data points that ultimately go into such a project.
Of course, government service is far from your only option in Oklahoma. In addition to being home to such Fortune 500 companies as Devon Energy and Chesapeake Energy, Oklahoma City has high powered data science companies that have made a name for themselves. And hot analytics startups like Tailwind can be found in OK city and Tulsa, thriving urban centers with low costs of living.
With strong demand for data scientists in Oklahoma, this is an ideal time to get your graduate degree and plug into the growing field of opportunities available in the state.
Preparing for a Master’s Degree in Data Science in Oklahoma
Anybody with plans to become a data scientist should begin preparing for a master’s degree during their bachelor’s degree program. Taking the appropriate courses and building a solid foundation in math, programming, and statistics is the only sure way in the door at most master’s programs.
Undergraduate Degree and Master’s Prerequisite Courses
Prospective grad students should be prepared to meet the following requirements:
- Earn a bachelor’s degree in a quantitative field such as statistics, applied math, computer science, or engineering
- Take courses in such key disciplines as linear algebra, calculus I and II, statistics, programming languages, and quantitative methods
- Have a minimum GPA of 3.0
Gaining Relevant Personal and Work Experience for Admissions
Typically, graduate schools also prefer applicants with highly relevant professional experience:
- Personal experience related to programming, database administration, mathematics, or statistics
- Strong communication skills
- At least five years of technical work experience in a related and relevant field
Oklahoma’s tech sector provides a number of options for jobs that may satisfy the requirements for experience. Some examples of job titles include:
- Database Engineer
- Data Architect
- Senior Big Data Engineer/Developer
- Hadoop Framework Designer
It’s not really enough just to have those jobs listed on your resume, though. Schools are going to want to see that you actually made a difference and learned something while you were there. That means working to a standard that will earn you some shiny letters of recommendation from supervisors.
Preparing to Excel on the Quantitative Sections of the GRE/GMAT Exams
Scoring in the 85th percentile of the GRE and/or GMAT is an excellent way for an applicant to demonstrate core competency in key skills related to data science. Both students who have taken these exams and the testing companies themselves advise preparing for these tests ahead of time and taking practice tests on sample quantitative problems to get comfortable with the material.
The GRE’s quantitative section is particularly important and evaluates the candidate’s skills in algebra, geometry, data analysis, and arithmetic. Candidates seeking a career in data science should pay particular note to statistics including standard deviations and probabilities. Test takers can find sample questions and free practice exams at the official GRE website.
The General Management Admissions Test (GMAT) evaluates a candidate’s quantitative, writing, and verbal abilities. Graduate school admissions departments expect high scores in all of these areas. However, the 37 questions that assess data efficiency and problem solving are particularly important. Candidates can take GMAT practice exams through Veritas Prep and The Princeton Review®.
Online Data Science Bootcamps to Build Skills For Your Master’s Program or for Direct Entry into the Industry
Of course, not everyone realizes they want to become a data scientist while they are picking out their undergrad courses. This can leave you with a degree, and subsequent work experience, that are not exactly what master’s programs are looking for in applicants. But you don’t have to go back to school to get the toolset you need to impress admissions committees. You can sweat your way through a data science bootcamp instead.
Actually, data science bootcamps are not physically demanding. But your brain will definitely be put to the test as it is pushed every day to absorb more information and learn relevant, real-world analytics techniques and tools. You’ll study subjects like:
- SQL, SQL Server, and MySQL databases
- HTML5 and CSS for data presentation
- Big Data and social media mining
- Advanced analysis techniques
You learn them through a cohort-based approach that takes on realistic projects, typically involving actual, live datasets, replicating the same sort of environment that you will face in the workforce. Instructors are usually fresh from the coal face, stocked up with the latest tips and techniques to make your experience cutting-edge.
Bootcamps exist that are aimed at every skill level in data science, from entry level to the extremely advanced. Your best bet to prep for a master’s application is something at the entry level, and a swath of online programs now fit the bill for that, including:
- Northwestern Data Science and Visualization Boot Camp
- Rice University Data Analytics Boot Camp
- The Data Analysis and Visualization Boot Camp at Texas McCombs
- University of Minnesota Data Visualization and Analytics Boot Camp
These particular offerings are unique in a couple of different ways. First, they are part-time, stretched out over six months of evenings and weekends so as to allow you to fill in your education on your own time, while holding down a real job. Second, they are backed by the resources and expertise of major universities, with existing, highly respected data science departments.
That means more professional instructors and more ancillary support, such as career services departments that have strong talent when it comes to helping you build up your resume, portfolio, or lining up demonstrations and interviews with potential employers. All those same features can work wonders in impressing master’s admissions committees.
Filling Gaps in Functional Knowledge Through Massive Open Online Courses and Bridge Courses
There are a couple other options you can look at for building your skillset if you are only missing a few particular areas of interest in your background. These are both far less expensive than bootcamps and far less time-consuming, and allow you to tailor your training in a few discrete areas.
Massive Open Online Courses (MOOCs) – If a candidate has not been trained in key skills during his or her education or experience, Massive Open Online Courses (MOOCs) are one way to acquire that knowledge. Usually offered by major colleges or universities as online versions of their classrooms that are opened to everyone, they require self-discipline to get through, but give you the option of completing your studies on your own time, and selecting a program that is a good fit for your particular needs.
Bridge Programs – Many graduate programs offer bridge courses designed to enable candidates to supplement their skills in areas that apply to data science. These are basically undergraduate level classes that are offered in summer term and replicate the studies you would have undertaken in your bachelor’s program if you had chosen a more quantitative degree plan. Two types of bridge programs are available:
- Fundamental bridge programs – courses in data structures, algorithms and their analysis, and linear algebra
- Programming bridge programs – training in such essential programming languages as C++, Java, and Python
Earning a Master’s Degree in Data Science in Oklahoma
You have on-the-ground options for data science graduate studies right here in Oklahoma, but if you don’t find what you want you can always enroll in a high-quality online program.
Data Science Programs Offered by Oklahoma Universities On-Campus and Online
Master’s Programs – Two universities in Oklahoma offer interdisciplinary master’s programs that have online options:
- Master of Science in Data Science and Analytics in Norman
- Master of Science in Business Analytics in Oklahoma City
Graduate Certificates – In addition, a number of graduate certificates are offered in Oklahoma City:
- Graduate Data Mining Certificate Program (online)
Credits may transfer to the MS program at a later date
- Graduate Certificates offered in conjunction with SAS®
- Data Mining Certificate (core level)
- Predictive Analytics Certificate (advanced level)
- Marketing Data Science Certificate (expert level)
Of course, the wonder of high speed internet means you also have the option of selecting from among highly regarded online programs from out-of-state universities. These kinds of programs offer a high degree of flexibility to working professionals. Students can avail themselves of a number of different formats depending on the time they have available to commit to their studies.
For instance, accelerated programs can be completed in as little as 12 months, while part-time programs generally take 32 months. Master’s degrees offered in standard full-time mode take 18 months.
In addition to the online coursework in the initial part of these programs, most require at least one on-campus appearance during the final semester. These immersion experiences offer intensive coursework on campus and the opportunity to interact with professors and peers.
Degree programs available include:
- Master of Science (MS) in Data Science
- Master of Information and Data Science (MIDS)
- Master of Science in Data Science (MSDS)
- Data Science Certificate
- Data Mining and Application Graduate Certificate
- Graduate Certificate in Data Science
- Online Certificate in Data Science
Core Curriculum Content
The coursework varies in different master’s programs, but you’ll find that the core courses offered in all of them will cover essential skills required for data science positions. Those subjects will include:
- Applied regression and time series analysis
- Machine learning and artificial intelligence
- Data storage and retrieval
- Data mining
- Data research design and applications
- Ethics and law for data science
- Network and data security
- Experiments and casual inference
- File organization and database management
- Information visualization
Most online programs offer students the chance to apply their training to real-world problems during the immersion experience, working in small teams to uncover effective solutions.
Key Competencies and Objectives
Graduates of data science programs acquire a wide range of proficiencies in core areas:
- Statistical sampling
- Research design
- Data cleansing and munging
- Database management and file organization
- Machine learning and artificial intelligence
Career Opportunities for Oklahoma Data Scientists with Advanced Degrees
Graduates of Oklahoma’s data science master’s degree programs aren’t hurting for potential employers. The Oklahoma Department of Commerce reported that the state is home to such prominent companies as IBM, Boeing, Dell, Google, and GE among others. These companies are all in industries that rely heavily on data science for their success.
In addition, Oklahoma houses one of the four sites of the highly prominent data science firm WPA Opinion Research—one of the leading data science and opinion research firms for Republican candidates and politicians.
Oklahoma’s prominence in the oil and gas industry also contributes to the high number of data science jobs in the state. In addition to analyses of such topics as supply and demand, the oil and gas industry requires data scientists to help its web-based monitoring and field automation services.
Naturally, with all that competition, you can expect to be paid top dollar for your work. According to Robert Half’s 2020 Technology Salary Guide, entry-level data scientists in Tulsa make more than $98,000, while those in the top of the field—the ones who stopped off and got master’s degrees—make more than $167,000. That’s better than you’ll pull down out in the oil fields.
These listings are just to demonstrate the types of data science jobs available in Oklahoma and should not be construed as an assurance of employment or current job offers.
Data Scientist with WPA Research in Oklahoma City – Candidates must have extensive familiarity with Python for data analysis and machine learning. Other valuable skills include R, Java, SQL and NoSQL, or other analytical and programming packages. Applicants with a master’s degree in data science, statistics, computer science, or an applied quantitative skill are preferred.
Senior Data Scientist – Predictive Analytics with a Client in Tulsa – Candidates will join a team that is building a unique technology platform for the real-time discovery and manipulation of data for the oil and gas industry. Applicants must be passionate about creating efficient and fast algorithms for data analysis and machine learning. Required skills include AI, machine learning, statistical analysis, and proficiency in programming technology and languages such as Python, C++, R, Java, and SQL. The position requires at least a master’s degree in statistics, physics, computer science, or a related field with a focus on data analysis.