Online Master's in Data Science for Jobs in New Mexico

The study of big data, otherwise known as data science, is finding some unique applications in New Mexico. Companies such as BigByte.cc in Albuquerque are using big data to offer companies redundant infrastructure (i.e. housing for their data centers), business continuity planning, and cloud computing solutions. In a more novel utilization of technology, criminal profilers in New Mexico are using big data to analyze brain imaging scans of criminals. This represents a unique application of technology for 7072psychology to assess and test theories of criminality. Additionally, the Earth Data Analysis Center at the University of New Mexico uses data science to analyze and generate data sets for mapping purposes for local, state and federal agencies. This helps to create interactive maps to aggregate wildlife and hazard areas across the United States.

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 National Laboratory in the 1940s. At another national laboratory in New Mexico, Sandia National Laboratory, work has continued for years in areas such as nanotechnologies and microsystems, systems engineering and integration, extreme-environment testing, and high-performance computing, modeling and simulation.

Innovations in New Mexico are leading to more potential opportunities for data scientists. For example, the University of New Mexico Health Science Center is working on expanding existing tele-health infrastructure to form a telehealth health system of 30 hospitals statewide. A $15 million grant from the Centers for Medicare and Medicaid Services was awarded to them to help in their efforts.

Data scientists in New Mexico who hold master’s degrees work for companies such as Raytheon Ktech in Albuquerque, performing research, development and project management tasks to developing new technologies for government and commercial usage. They are involved in, among other things, designing solutions for software and automation, airborne flight tests of missiles, and control and data acquisition systems.

Another employer for data scientists in New Mexico is Qualis Corporation in Kirtland. This defense and space industry company uses data scientists to test and administer the nuclear certification process, planning for future technologies, researching and defining requirements for systems.

Preparing for a Master’s Degree in Data Science in New Mexico

Planning for a Master of Science in Data Science in New Mexico 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, computers and science
  • Complete related work experience including skills such as database administration, hacking, computer programming and/or quantitative skills
  • Prepare for and pass the GMAT or GRE, paying particular attention to scoring high on the quantitative sections of these exams
  • Use bridge courses or MOOCs to compensate for 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:

  • Possession of a bachelor’s degree in a related field like computer science, computer programming, mathematics, statistics or engineering
  • Fulfilling prerequisite course requirements such as calculus I and II, database management and design, computer programming languages, statistics, probability, and principles of operating systems
  • Achievement of at least a 3.0 GPA (on a 4.0 scale)

Relevant Personal and Work Experience

Admissions officers for data science graduate programs are also seeking applicants with certain personal and work experience, such as:

  • At least five years of work experience in a technical field, using quantitative skills
  • Hold recommendation letters from employers or others familiar with the applicant’s academic, work or personal experience
  • Personal experience in skills such as hacking, coding, mathematics, statistics, data mining, and/or database administration

Jobs in New Mexico that could help applicants to 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

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:

Two subject area tests of the GRE are also relevant to Data Science:

  • Physics (Physics Test Practice Book):
    • Special relativity
    • Electromagnetism
    • Lab methods and specialized topics
    • Classical mechanics
    • Statistical mechanics
    • Thermodynamics
    • Quantum mechanic
    • Atomic physics
    • Optics and waves
  • Mathematics (Mathematics Test Practice Book):
    • Probability, statistics and numerical analysis
    • Algebra
    • Discrete mathematics
    • Calculus
    • Introductory real analysis

The quantitative portion of the GMAT – The Graduate Management Admission Test (GMAT) evaluates the test-taker’s data analysis capabilities. The Quantitative Section, one of the four main sections of the GMAT, entails completion of 37 problem solving and data analysis questions in a time limit of 75 minutes. Valuable study aids for the GMAT include:

Filling Gaps in Functional Knowledge Through MOOCs or Bridge Courses

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, schools often offer them bridge courses. Given at the pre-master’s level, 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 mathematics 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 completion of MOOCs. These online courses include video lectures, problem sets, interactive forums, and the support of teaching assistants and professors. MOOCs are offered in a variety of ways, including private, invitation-only courses; as well as pubic, open-access courses. Either way, MOOCs can help an aspiring graduate student to prepare for a graduate program in data science through filling in crucial knowledge gaps. Examples of course titles of MOOCS pertaining to data science include:

  • Data Science and Machine Learning Essentials, offered by Microsoft
  • Learning from Data, offered by California Institute of Technology
  • Introduction to Data Science, offered by the University of Washington

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 such as Las Cruces, Albuquerque and Santa Fe. Degrees in data science and related areas that are attainable by those who meet the proper criteria 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

Master’s degrees in data science programs usually entail about 35 credits and can be completed in two to three years on a part- or full-time basis.

Online graduate data science 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 graduate data science programs are usually completed in a year and a half. For students who study part-time, online graduate data science programs are generally completed in a little under three years. Accelerated online graduate data science programs can be completed in 12 months.

Graduate certificate in data science and related areas are also available to students, and take from one year to a year and a half to complete a 12 to 18 credit program. Employers who are looking for data scientists with master’s degrees, however, do not always accept certificates.

Core Coursework, Internship and Immersion Experience

In a master’s-level data science degree program, a student will usually take courses such as:

  • Experimental statistics I and II
  • Data science
  • Database management
  • File organization
  • Data and network security
  • Visualization of information
  • Data mining
  • Statistical sampling
  • Quantifying the world
  • Immersion
  • Graduate internship

During the graduate internship, students will work in a real-life data science setting, practicing the skills and competencies that they have learned. Performances are evaluated by employers and professors, depending upon a school’s policies. 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. Performances are evaluated by data science professionals as well as professors.

Key Competencies and Program Objectives

A graduate of a master’s in data science program should display the following objectives and 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, visual 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 2016 Salary Guide for Technology Professionals, data scientist salaries in the Albuquerque area rose from a 2015 range of $94,245 (low) to $126,498 (high) to the 2016 low of $99,735 and high of $140,681. This is good news for new graduates of data science master’s programs in New Mexico as they search for employment. (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 New Mexico, completed in March 2016.)

Data Scientist with Altamira in Las Cruces- This open-source technology company specializing in national security sought a data scientist with security clearance to develop new analytical methods involving geospatial data and spatial-temporal visualization.

Applicants must have an advanced degree in a related field, as well as experience in programming languages including R, Java, C++, Python and Perl

Data Scientist with USRA in Albuquerque- This nonprofit research corporation working in planetary science, biomedicine, engineering and astrophysics sought a data scientist to perform technical analysis, simulations and modeling in deep-space object characterization and operations. Responsibilities include identifying potential data products, leading 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 advertised for a data scientist to work in financial crime 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.

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