As home to three national laboratories, three research universities, and many nonprofit institutions, New Mexico has been involved in many technological revolutions over the years. The atomic bomb was developed at Los Alamos in the 1940s. At another national laboratory in New Mexico, Sandia National Laboratory, work has continued for years in such areas as nanotechnology and microsystems, systems engineering and integration, extreme-environment testing, and high-performance computing, modeling and simulation.
- Grand Canyon University - B.S. in Business Information Systems and M.S. in Data Science
- SMU - Master of Science in Data Science - No GRE 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
The University of New Mexico Health Science Center recently received a $15 million grant from the Centers for Medicare and Medicaid Services to expand its telecommunications infrastructure to form a telehealth system that virtually connects staff and patients at 30 hospitals statewide, consolidating data to give healthcare informaticists richer insights into treatment plans, patient outcomes, and readmittance rates.
Raytheon in Albuquerque has some of the most talented master’s-prepared data scientists in the business taking on the monumental task of deriving meaningful insights from data-intensive research and development projects, evolving and advancing technologies for military and commercial use. They are involved in, among other things, designing solutions for software and automation, missile flight tests, and the very data acquisition systems that collect even more of those sweet data.
While they work on a vast array of different technologies and in different industries, one thing all these organizations have in common is this: they absolutely demand data scientists educated at the master’s level capable of handling ever more data intensive work and finding solutions to the kinds of problems only data-derived insights can solve.
Preparing for a Master’s Degree in Data Science in New Mexico
Planning for a Master of Science in Data Science begins at the undergraduate level. While studying for a bachelor’s degree, prospective data scientists in New Mexico should make sure to do the following in preparation for an eventual career:
- Take the proper undergraduate coursework in mathematics, computer science and statistics
- Complete related work experience in areas like database administration, computer programming, and/or quantitative analysis
- Prepare for and pass the GMAT or GRE, paying particular attention to scoring high on the quantitative section
- Use bootcamps, bridge courses or MOOCs to fill any gaps in knowledge
Undergraduate Degree and Master’s Prerequisite Coursework
Admissions requirements for master’s-level data science degree programs in New Mexico usually include:
- Bachelor’s degree in a related field like computer science and programming, mathematics, statistics or engineering
- Prerequisite course requirements such as calculus I and II, database management and design, computer programming languages, statistics, probability, and principles of operating systems
- Achieve at least a 3.0 GPA in undergraduate studies
Relevant Personal and Work Experience
Admissions officers for New Mexico’s data science graduate programs are also seeking applicants with relevant personal and work experience:
- At least five years of work experience in a technical field, using quantitative skills
- Hold recommendation letters from employers or others familiar with relevant work or personal experience
- Personal experience in skills such as coding, mathematics, statistics, and/or database administration
Jobs in New Mexico that could help applicants meet these requirements include:
- CrossAsset code development with particular attention to Java and C++ programming languages at Numerix Risk Analytics Co. in Santa Fe
- Human Resources Workforce Analytics Specialist, with experience in statistics, data analysis and database management, at Los Alamos National Laboratory in Los Alamos
- Logistics Management Specialist, including skills in budgeting and planning, for the Department of the Army in Santa Fe
- Quality Assurance/Quality Control Supervisor, working in engineering and design collaboration, for Affordable Solar in Albuquerque
Excelling on the Quantitative Sections of the GRE and GMAT Examinations
Standardized testing is a very common entry requirement for master’s programs of all stripes. But it has a special place in data science master’s admissions. The goal when taking the GRE or GMAT examinations is to earn a score in the 85th percentile or better on the quantitative sections of the exams.
GRE- The Graduate Record Exam (GRE) revised general exam’s quantitative reasoning portion evaluates aptitudes in:
- Arithmetic questions on roots, exponents, integers and factorization
- Algebraic questions on quadratic equations, linear equations, graphing and algebraic expressions
- Geometric questions on the Pythagorean theorem, and the properties of quadrilaterals, circles, triangles, and polygons
- Data analysis with questions on subjects like Venn diagrams, probabilities, permutations, interquartile range, graphs, tables statistics and standard deviation
Students can prime themselves for the quantitative reasoning portion of the GRE through:
- Taking GRE Practice Exams offered by the Princeton Review
- Completing the Educational Testing Service’s (ETS) Math Review
- Taking GRE Practice Exams and other preparatory options offered through Kaplan Test Prep
Two subject area tests of the GRE are also relevant to Data Science:
- Physics (Physics Test Practice Book):
- Special relativity
- Lab methods and specialized topics
- Classical mechanics
- Statistical mechanics
- Quantum mechanic
- Atomic physics
- Optics and waves
- Mathematics (Mathematics Test Practice Book):
- Probability, statistics and numerical analysis
- Discrete mathematics
- Introductory real analysis
The quantitative portion of the Graduate Management Admission Test (GMAT) evaluates data analysis capabilities. The Quantitative Section, one of the four main sections of the GMAT, entails 37 problem solving and data analysis questions to be completed in a time limit of 75 minutes. Valuable study aids for the GMAT include:
Data Science Bootcamps to Prepare for a Master’s Program and Acquire Relevant Job Skills
Most master’s programs want to see more than just a high score on a general admissions exam, however. You will need some subject-specific expertise in analytics and programming, and if you didn’t choose the right bachelor’s program or have a related job, you will have to find some other way to pick that up.
That’s where data science bootcamps come in. These short, intensive courses run for a few weeks or a few months and jam in a ton of specific education in data science tools and techniques. They focus on real-world problem solving using the tools at the cutting-edge of the industry today, including:
- Hadoop and Spark for Big Data analysis
- Programming in R and Python, with analytics libraries like Numpy
- SQL data stores and query building through MySQL or SQL Server
- Data visualization through libraries like D3.js or off-the-shelf tools like Tableau
Bootcamps are usually relatively inexpensive, although they are aimed at various levels of data science—some are extremely advanced and correspondingly more demanding. But basic entry-level programs are where you want to look if you’re preparing for a master’s.
These programs typically use a cohort-style approach, pitting you and your fellow students against a series of projects that use real data and realistic scenarios to develop hands-on skills in data analysis and presentation. You’re guided through all of it by experienced instructors who have already made their mark in the industry.
Once almost entirely conducted in-person, now bootcamps are starting to move online, which opens up options to New Mexico students, including the University of Arizona Data Analytics Boot Camp.
The added advantage of joining a university-run boot camp is clear when you consider the extra resources that are available from a highly respected data science department, including experienced instructors and a fully-developed career service department that can help you prep your CV for maximum value when it comes time to apply for a master’s program.
Filling Gaps in Functional Knowledge Through MOOCs or Bridge Courses
You might not need to go through a full bootcamp experience, however, if you find that there are only one or a few small holes in your skillset and knowledge. Two other options exist that can help you plug those gaps quickly and effectively.
Bridge Programs – If an applicant has been accepted into a graduate program in data science but is lacking key functional knowledge in one or more crucial areas, the school itself will often offer them an opportunity to enroll in one or more bridge courses. Basically identical to undergraduate courses that you might have taken in a pre-master’s program in data science, these courses intend to bridge gaps in functional knowledge before a student begins graduate-level data science coursework. For example, a student with an undergraduate engineering degree might have a functional knowledge gap in programming and need to complete bridge courses to qualify for the master’s in data science program. Areas in which bridge courses are offered usually include:
- Mathematics, with courses including algorithm analysis, linear algebra and data structures
- Computer science, with courses related to database management and administration
- Programming, with courses in programming languages such as Java, C++, R and Python
Massive Open Online Courses (MOOCs) – Another way in which prospective graduate data science students can bridge functional knowledge gaps is through MOOCs. These online courses include video lectures, problem sets, interactive forums, and the support of teaching assistants and professors. MOOCs can help an aspiring graduate student prepare for a graduate program in data science by allowing them to pick and choose the specific areas for study that they know they are lacking.
Earning a Master’s Degree in Data Science in New Mexico
With the many novel applications of data science in New Mexico, obtaining a graduate degree in this up-and-coming area is becoming more popular among bachelor’s-educated technology professionals. Data science and related master’s degree programs are found both online and across New Mexico in cities like Las Cruces, Albuquerque and Santa Fe. Degrees in data science and related areas include:
- Master of Science in Data Science
- Master of Science in Translational Informatics: Data Science
- Graduate Certificate in Business Analytics
- Master of Information and Data Science
Data science master’s programs usually involve about 35 credits and can be completed in two to three years on a part- or full-time basis.
Online data science master’s degrees are also typically 30 to 40 credits in length but offer more flexible options and may be accessed from anywhere in the world. Traditional online programs are usually completed in a year and a half. For students who study part-time, online options are generally completed in a little under three years. Accelerated online graduate data science programs can be completed in 12 months.
Graduate certificates in data science and related areas are also available, and take from one year to a year and a half to complete (12 to 18 credits).
Core Coursework, Internship and Immersion Experience
In a master’s-level data science degree program, a student will usually take courses covering:
- Experimental statistics I and II
- Database management
- File organization
- Data and network security
- Visualization of information
- Data mining
- Statistical sampling
During an optional graduate internship, you’ll have the opportunity to learn not only from your faculty advisors, but also actual data scientists on the job. Online graduate data science programs may also require internships at selected sites across New Mexico.
An immersion experience is also a requirement of most master’s in data science programs. During this experience, students participate in group case study work on a specified topic. They get the opportunity to collaborate with other students and faculty on a set of problems and use their newly-learned skills and competencies to come up with solutions.
Key Competencies and Program Objectives
As a graduate of a master’s in data science you’ll be armed with these competencies:
- Develop statistical techniques to form relevant questions
- Collect and analyze appropriate data and make informed decisions
- Use technical skills in data and network security, database management, machine learning, data mining, and programming to make sense of unstructured, complex data
- Use various approaches to visualizing data such as creative coding and information design principles
- Apply analytic and mathematical principles to data science, learning how to make strategic, informed decisions
- Analyze data, interpret results, and communicate findings clearly
- Use a global perspective in developing a well-rounded approach to solving problems and applying concepts to global businesses and matters
- Understand ethics, data security and legal responsibilities
Career Opportunities for Data Scientists in New Mexico with Advanced Degrees
According to the Robert Half International 2020 Salary Guide for Technology Professionals, data scientist salaries in Albuquerque range between $97,000 and $165,000 depending on the specific industry, experience, and skillsets that a candidate can bring to the table. This is good news for new graduates in New Mexico as they begin the job search, because education is one of the key factors in bumping your salary up toward the higher end of that range. The following job listings are shown as illustrative examples only and are not meant to represent job offers or provide any assurance of employment.
Data Scientist with AFRL in Albuquerque- This nonprofit research corporation working in planetary science, biomedicine, engineering and astrophysics hires data scientists to perform technical analysis, simulations and modeling in deep-space object characterization and operations. Responsibilities include identifying potential data products, leading the development of new data products and determining what data warehouse and analysis tools to implement.
A graduate degree in data science, mathematics, engineering or physics is required for this position, as well as four years of research experience.
Data Scientist – Financial Services Analytics with Oracle in Albuquerque- This information technology giant hires data scientists to work in financial crime investigations and compliance. Responsibilities include analyzing, developing and troubleshooting software programs, writing code and creating new architecture.
Requirements include an advanced degree in a related field.