Texas has been a battleground state for most of its history – the real kind initially, so we’d like to think the political dust-ups we’re seeing in more recent history aren’t anything to get too worked up about. It is interesting, though, that the weapons of choice among pollsters and politicians fighting for supremacy these days are algorithms and big data analytics.
Home-brewed machine learning models for voter targeting are under development that will register and turn out millions of new voters across Texas. With insights into the unique makeup of Lone Star state voters and a learning algorithm that will adjust on the fly, powered by Google and open-source Python packages, even political strategists can’t ignore the might of big data.
There’s money in contracting services to political campaigns, to be sure, but the big money opportunities for data scientists span a wide range of sectors. Take your pick – with old money oil and gas companies and telecom stalwarts with major operations here right next door to tech innovators and green energy start-ups, data scientists are in short supply, and the bidding war is well underway to attract the top talent.
Positioning yourself for one of those jobs means earning a master’s degree in most cases. With the data science job market on fire these days, you could actually find more competition getting into a high-quality graduate program than landing your dream job once you have that degree in hand.
Preparing for a Master’s Degree in Data Science in Texas
Candidates applying for admission to a Master’s Degree in Data Science (MSDS) are typically expected to have some experience, high scores on proficiency exams, in addition to all the prerequisites you’d expect. With the steady stream of computer science and applied mathematics guys flooding the field, the admissions process can be highly competitive. Preparing to be a contender starts early… in most cases, with the selection of an appropriate bachelor’s degree program.
Undergraduate Degree and Master’s Prerequisite Courses
Anything other than a degree in a quantitative field isn’t going to impress admissions committees. They’re looking for candidates who have completed a bachelor’s in a STEM field like applied mathematics, engineering, or, in a pinch, business analytics. In any case, your course load should be heavy with classes in statistics, math and programming.
Further, you’ll need to deliver up some evidence that you weren’t sleeping through those courses: a minimum aggregate GPA of 3.0 or better is often required.
Relevant Personal and Work Experience
Previous work experience (five years professional experience is preferred) in the field of data analysis or data management is helpful, and often 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. Internships are also widely available.
Preparing to Score Within the 85th Percentile on the GRE/GMAT
Another way that your academic capabilities will be weighed is through good old standardized testing. Your scores on either the Graduate Record Exam (GRE) or Graduate Management Admissions Test (GMAT) could be given significant weight, with the expectation that you land somewhere in the 85th percentile.
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 of data science and the ability to communicate that knowledge effectively.
The quantitative reasoning portion of the GRE tests your aptitude in the following areas:
- Basic arithmetic such as integers, square roots, factorization, and exponents
- Algebraic expressions, linear and quadratic equations, graphing, and functions
- Geometric equations, proofs, and the Pythagorean Theorem
- Specific statistical topics including standard deviation, permutations, tables, probabilities, etc.
The official GRE website offers practice exams and guides to help students prepare for the Quantitative Section of the exam.
The GMAT takes you through all the above topics, as well as formulating word problems to gauge your ability to analyze initial information and come to appropriate conclusions based upon the data presented.
Build Your Skills for a Master’s and Get Job-Ready Through a Data Science Boot Camp in Austin, San Antonio, Dallas, Houston, or Online
If you’re not confident that your skills are quite up to standard, you have a few options for getting there. One of the latest, and fastest, is by enrolling in a data science boot camp.
As you would expect from a training program referred to as a bootcamp, you’ll be undergoing an intensive period of practical, hands-on training that can last from one to nine months, covering all the essential tools and tasks data scientists are expected to be familiar with.
Boot camps come in all shapes and sizes, from very advanced ones that require candidates to already have a master’s degree and several years of experience, down to entry-level courses that get your foundational skills in place. They rarely spend much time on theory and the conceptual elements of data science, and instead put you to work on a series of practical exercises where you are expected to produce real results in cooperation with your classmates and with the support of experienced and knowledgeable instructors.
For years, boot camps were offered by independent providers and industry groups, but today, many universities are starting to offer them too. Texas is particularly blessed in this respect, with three different prestigious state schools and one private university offering up programs:
All four programs are offered part-time, both on-site and online, with evening and weekend courses that run for six months. Each focuses on giving you the knowledge and skills necessary to solve complex real world problems with big data by covering:
- Statistical modeling and forecasting
- Important data analysis and visualization libraries like:
- Big Data Analytics with Hadoop and other databases
Of course, each program has its respective strengths as well:
- UT Austin and UTSA – Offering a building-block approach to carefully build your skills to maximize retention, these programs are taught by world-class instructors with a curriculum rooted in current market demand
- Rice – Closely integrated with big-money Energy Corridor analysts and employers, the program offers major networking opportunities
- SMU – As a nationally-ranked private research university, the school delivers experiential training in cutting edge techniques
All four programs go beyond basic data science training by offering significant career support as well, giving you the option to not only bring your master’s program application up to par, but giving the option to bypass a master’s program entirely and head straight into a career.
Gaps in Functional Knowledge Can Be Filled With MOOCs and Bridge Courses
If a boot camp isn’t really your speed, you still have plenty of options to cover any current gaps in knowledge or skills that a data science program will care about.
Massive Open Online Courses (MOOCs) function more as an independent study resource. Put on by both private companies and major universities, and sometimes partnerships between the two, MOOCs allow students access to recorded online lectures and materials on dozens of different subjects. These courses provide introductory foundations that you can pick and choose discretely, based on your particular interests or needs.
Bridge courses are offered through graduate schools themselves 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 MSDS 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 offer a direct pipeline into your master’s-level studies.
Earning a Master’s Degree in Data Science
As the field of data science grows and the various industries it benefits become more diverse, colleges and universities are creating more 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:
- Master of Science (M.S.) in Data Science
- Master of Science (M.S.) in Information, Risk, and Operations Management
- Focus: Business Analytics
- Master of Science (M.S.) in Applied Statistics
Programs of study which are available online include:
- Online Master of Science (M.S.) in Data Science
- 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 and Prescriptive Analytics
- Business Intelligence
- 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 and visualization
- Database queries and management
- Programming skills in languages such as Python, R, and SAS
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. QuantHub, a website specializing in the assessment of data science talent, found that 83 percent of companies are investing in Big Data projects currently, but projects an 85 million shortage in global tech talent by 2030. That environment leads to considerable churn among data science positions, with the average turnover happening in only two years.
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.
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 at Unnamed Employer listed on 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.