There’s a problem that continues to plague the data industry, and many employers can vouch for it: big data is evolving at an unprecedented rate, but there’s simply not enough analytical talent to pull meaningful insights from the information provided. In an effort to bridge the skills gap, many universities are offering programs for big data analytics degrees, or at least offering more specific concentrations in the subject.

A job candidate with a big data analytics degree is often preferred by employers because he or she has already honed the necessary skills to be successful in the job. Here’s an overview of what university programs are teaching students today, and what skills employers can expect from future big data analysts:

Data Structure, Data Mining and Data Center Infrastructure

It wouldn’t be a degree in big data analytics without courses that dive into data structure, data mining and the infrastructures used to develop and manage analytical programs. Serial and parallel data structures, linked lists, graphs, trees, and algorithm design and analysis are often discussed in introductory courses and explored in depth as the program progresses. These courses often cover topics such as: data mining concepts and tools used during the knowledge discovery process; the processes for extracting valid, useful and previously unknown information; and ultimately, using the information to make better data-driven decisions.

During the course of the program, students will also become familiarized with data center infrastructure and management, with a focus on these technologies: virtualization, networking, server consolidation, green IT computing and network storage configurations. Courses are designed to explore the use of virtualized platforms, networking and infrastructure configurations and the deployment, analysis and management of applications. A thorough analysis of the tools and programs most commonly used in big data analytics gives students the hands-on experience needed to ease into a career post-graduation.

Computer Programming and Programming Language

In order to crunch both structured and unstructured datasets, proficiency in computer programming is still one of the most sought-after skills in big data analytics. As a result, universities that offer big data analytics degrees typically introduce computer programming concepts and systematic design techniques early on in the program. As the program progresses, more advanced courses, applied concepts, and real-world problems are introduced, ensuring that students are acquiring the skills and the confidence to handle complex situations later on in their careers.

When they are taught best practices for designing and writing efficient code, students should be able to write customizable programs that solve specific problems most of the times, data driven. The use of essential built-in data structures, user-defined data types, distributed transactions, client- and server-side programming, and methods such as HTML5, CSS, JavaScript, XML, PHP, Python and Ruby Rails should also become more familiar to students throughout their programming courses.

Analytical skills, Mathematical Modeling


It is true that a big component of big data analysis is data handling, algorithm implementation and visualization. To really excel in the field, however, a big data analyst must primarily be an analyst. Students should receive a thorough introduction to concepts likes designing pseudo-algorithms, mathematical logic and mathematical modeling, starting as early as their core mathematics classes. New ideas and new programs are generated only after the mathematical underpinnings of each big data problem are understood and explored.

Core classes like advanced quantitative methods, discrete mathematics, and fundamentals of applied information help students understand the algorithms behind the data manipulation giving them the foundation to see behind the code and understand the essence of each problem. Advanced classes like forecasting, modeling and web analytics, allow them to go beyond “of the shelf” programs and methods and start building their own solutions through new mathematical models that they invent. A successful analyst is the one that views the problem in a different light and then attempts to solve it.

Fine-Tuned General Career Skills

A quality big data degree program will often offer unique internship opportunities; provide hands-on learning to design and implement effective analytical programs; and refine skills in computing, problem-solving, communications and analytical reasoning. Internships – and in particular, internships with university partners or industry-leading big data organizations – allow students to apply classroom theory to tackle real-world problems and gain experience working in a field of their interest.

Although the focus of these programs tends to be on the computing and technical aspects of big data, it’s essential for students to grasp ethical concepts, too. An understanding of technology’s impact on society—and lessons in professionalism—will be helpful to students when they run into ethical or legal questions further down their career path. Additionally, hands-on projects and work experience can teach students what textbooks cannot, including working efficiently under pressure, clearly communicating results to business leaders and understanding their impact on overall business goals.

For employers looking to fill big data positions, a candidate with a formal education in big data analytics has its benefits. Not only are these students equipped with the skills necessary to adapt and thrive in a rapidly changing industry, but it is also much more likely that these candidates can provide the meaningful insights that employers need to make better business decisions.

(About the author: Dr. Athanasios (Thanos) Gentimis is an Assistant Professor of Math and Analytics at Florida Polytechnic University. Dr. Gentimis received a Ph.D. in Theoretical Mathematics from the University of Florida, and is knowledgeable in several computer programming/technical languages that include C++, FORTRAN, Python, R, SPSS and MATLAB).

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