Texas has long been a major revenue-producing region for many industries, and the demand for data scientists has grown to match the rise in business across the state. Data scientists have the insights and skills needed to dramatically affect the economic impact of small and large businesses throughout Texas. Employment opportunities for data scientists span across a wide range of sectors, including the innovative mobile technology of AT&T, the sustainable natural energy specialists of Progressive Global Energy, the IT and Network Engineering consultants at Creospan, and the marketing and sales moguls at Ebay.
- Syracuse University - M.S. in Applied Data Science: GRE Waivers available
- SMU - Master of Science in Data Science - Bachelor's Degree Required.
- UC Berkeley - Master of Information and Data Science Online - Bachelor's Degree Required.
- Syracuse University - Master of Information Management Online
- Villanova Business - Master's in Analytics and Study Data Mining, Predictive Analytics Online
Salaries for data scientists in Texas are competitively high and, in light of the vast scope of influence within the profession, job growth projections remain favorable in the coming years. While the demand for experienced data scientists is strong, there is a resounding shortage of Master’s-educated candidates in every corner of the industry. This has caused a spike in salaries, as countless companies are seeking qualified candidates to fill the void.
A major player in the economic market for data scientists in Texas is San Antonio-based Kforce Government Solutions, which provides data analytics and information technology solutions to both civilian and government defense clientele. Because Kforce often works with time-sensitive and high-risk security programs, their services must have an emphasis on accuracy and stress exact calculations. Thus, Kforce prefers hiring data scientists with a Master’s degree, or higher, level of training. This demand for data scientists with graduate-level education is fast becoming the “new normal,” as security measures are amped up and evolving marketing trends stress the use of more sophisticated algorithms and data mining techniques.
Preparing for a Master’s Degree in Data Science in Texas
Candidates applying for admission to a Master’s Degree in Data Science program are required to possess preexisting employment experience, high scores on proficiency exams, and meet a variety of program-specific prerequisites. Due to the steady rise in applicants and the nature of the field, the process of acceptance is highly competitive.
Undergraduate Degree and Master’s Prerequisite Courses
Admission to a Data Science Master’s Degree program in Texas assumes the completion of a Bachelor’s Degree in a related field, such as Applied Mathematics, Engineering, or Business Analytics. Further, a minimum GPA of 3.0 for undergraduate studies is often required.
Relevant Personal and Work Experience
Previous work experience (five years professional experience, preferred) in the field of data analysis or data management is helpful, and usually required, when entering graduate-level data science studies. Additionally, applicants should exhibit a proven ability to understand complicated data statistics and communicate their findings to others in a clear, concise manner.
Texas offers a variety of entry-level employment opportunities that would allow prospective data scientists to gain the foundation needed to begin a Master’s program of study. Internships are also widely available. Such companies and positions include:
- Systems Engineer at Texas Instruments in Dallas, TX
- Software Development Intern at Powershift in Austin, TX
- iOS Developer at Proximate Technologies in Plano, TX
Preparing to Score Within the 85th Percentile on the GRE/GMAT
Many graduate programs place a heavy emphasis on the proficiency of applicants with regards to their GRE/GMAT scores. It is essential to prepare for these examinations, as graduate schools for data science typically expect results to be above the 85th percentile when considering admission for prospective students.
Most admissions directors for data science graduate programs stress the importance of the Quantitative Section of the GRE/GMAT. However, many expect scores to be high in the Verbal and Writing Sections of the exam, as well. They will be looking for a substantial working knowledge within the data science field and the ability of the applicant to communicate that knowledge effectively.
The quantitative reasoning portion of the Graduate Record Exam (GRE) tests aptitude for the following:
- Basic arithmetics such as integers, square roots, factorization, and exponents
- Algebraic expressions, linear and quadratic equations, graphing, and functions
- Geometrical equations, proofs, and the Pythagorean Theorem
- Specific data analysis topics including standard deviation, permutations, tables, probabilities, statistics, etc.
The official GRE website offers practice exams and guides to help students prepare for the Quantitative Section of the exam.
The Graduate Management Admissions Test (GMAT) examines the above topics, as well as formulating word problems to gauge the student’s ability to analyze initial information and come to appropriate conclusions based upon the data presented.
The GMAT website provides similar test prep resources and guides. Additionally, practice exams can be accessed through Veritas Prep and The Princeton Review.
Gaps in Functional Knowledge Can Be Filled With MOOCs and Bridge Courses
If the student’s test scores indicate a lack in proficiency, further education can be supplemented with MOOCs and Bridge Courses, found both online and in live forums.
Massive Open Online Courses (MOOCs) function more as an independent study resource. MOOCs allow students access to recorded online classes, lectures, and materials with the purpose of filling any gaps in the prerequisite knowledge needed to begin a Master’s-level program in data science. These courses provide introductory foundations within the student’s chosen emphasis, depending upon their needs.
Bridge Courses are offered through graduate schools and are provided to students who are already accepted into the program but need to complete additional coursework specific to their area of study. For instance, students who are admitted to a Master’s of Data Science program with an undergraduate degree in an unrelated field can benefit from bridge courses in subjects such as:
- Python for Data Science
- Introduction to Linear Algebra
- Fundamentals of Data Structures and Algorithms
Bridge courses usually run for 15 weeks and, once completed, often become the gateway into Master’s-level coursework.
Earning a Master’s Degree in Data Science
As the field of Data Science grows and the various industries it enhances become more diverse, colleges and universities are creating greater programs of study to accommodate the influx of new students entering this exciting area of expertise. Master’s programs in data science are fast becoming the most populated, as well as the most competitive, in higher education arenas.
Because of the pressing demand, graduate schools are offering innovative avenues of study, both online and in live classrooms.
The following programs can be found throughout the state of Texas:
- Texas Tech University in Lubbock, TX
Master of Science (M.S.) in Data Science
- University of Texas at Austin in Austin, TX
Master of Science (M.S.) in Information, Risk, and Operations Management
Focus: Business Analytics
- University of Texas at San Antonio in San Antonio, TX
Master of Science (M.S.) in Applied Statistics
Programs of study which are available online include:
- Southern Methodist University in Dallas, TX
Online Master of Science (M.S.) in Data Science
- Texas A&M University in College Station, TX
Online Master of Science (M.S.) in Applied Statistics
A typical M.S. program of study includes about 36 credit hours, on average. In a traditional student setting, these credits are usually completed in 3 semesters. Some accelerated learning programs can be completed within 12 months time, or 2 semesters. And, though part-time programs are more limited, some schools allow extended completion times with coursework spread out over 5 semesters.
Curriculum and Core Coursework
Below is an example of the core coursework students can expect within the Master’s program of study:
- Advanced Statistical Methods
- Scripting Languages
- Database Concepts
- Applied Multivariate Analysis
- Big Data Strategy
- Predictive Analytics
- Business Intelligence
- Prescriptive Analytics
- Data and Text Mining
- Decision Theory and Business Analytics
- Advanced Topics: Data Science Project
- Advanced Topics: Project Management
Key Competencies and Objectives
Completion of all coursework and graduation with a Master’s degree in data science will provide students with the ability to step into the workplace as a competent data scientist. The following skillsets can be expected from graduates:
- Network/cyber security
- Data collection, analysis, and real-time application
- Data mining
- Database queries and management
- Data cleansing
- Programming languages, such as Python, R, and SAS
- Database queries
- Statistical research
Career Opportunities for Data Scientists in Texas with Advanced Degrees
The demand for data scientists with advanced degrees in Texas is strong and is expected to grow even stronger in the coming years. Contributing to the overwhelming need for highly trained professionals in this field is the glaring lack of skilled manpower. In fact, according to a report conducted by McKinsey Global Institute, the future of data science throughout the nation, Texas included, is unbridled. The report stated that by 2018, “the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” These numbers provide a promising platform for data scientists with graduate-level education and experience.
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 Texas in March 2016.
Senior Data Scientist with AT&T
- Responsible for designing and implementing processes and layouts for complex, large-scale data sets used for modeling, data mining, and research purposes
- Design and implement statistical data quality procedures around new data sources
- Visualize and report data findings creatively in a variety of visual formats that appropriately provide insights to the organization
- Applicants must have Bachelor of Science in Computer Science, Math, or Data Analytics, and 5-8 years experience, or equivalent graduate degree.
Senior Data Scientist- Real Estate Predictive & Big Data Analytics with CyberCoders
- Develop measures of similarity between residential properties
- Value property characteristics in a given market, i.e. estimating the value of 300 additional square feet in market X after controlling for other property characteristics
- Develop valuation logic
- Cluster geographic submarkets using a combination of property, demographic, consumer, and economic data
- Extract property information from unstructured text
- Applicants must have graduate-level degree in Mathematics, Statistics, Data Science, or Engineering and a strong knowledge of, or experience with, Python, R, SQL.
Data Scientist with Progressive Global Energy and Natural Resources
- Perform numerical modeling, data analysis, correlation and model development on complex multi-discipline projects utilizing existing commercial software
- Develop new modeling software
- Develop and regularly apply advanced geomechanics technologies in the areas of mini-frac analysis, formation sanding potential, wellbore stability assessment, pore pressure prediction, stress analysis, and mechanical property determination
- Proficiently design and perform complex specialized tests and simulations including design and construction of modeling apparatus and data acquisition systems
- Applicants must have graduate-level degree in Math, Statistics, Engineering, or Data Science. A Ph.D or 10 years experience is preferred.