Big-Data Science’s Dependency on the Skilled Data Engineers

As the field of data science matures, a distinct specialization is emerging: data engineering. Tech giants like Facebook, Amazon, and Google recognize the value of data engineers relative to data scientists. That’s why they’re targeting candidates with skills to build critical infrastructure like data pipelines and warehouses. The best computer science degrees keep up with this trend by helping graduate students develop high-level data engineering skills. The University of Illinois Online Master of Computer Science in Data Science (MCS-DS) degree is one of these degrees: it offers a comprehensive full-stack data sciences education to add this fast-growing career path to the employment opportunities of its graduates.

Why Data Engineering Matters?

Data science is one of the most in-demand career fields in computer science according to LinkedIn, the job openings increased 56% over the past year. These big data detectives unearth valuable insights through analysis of massive datasets. At the highest level, their skills are essential for developing machine learning algorithms and artificial intelligence (AI) applications. However, in order to work their magic, data scientists need data. And not just any data — they need a clean dataset. That means they need raw and messy data converted to a consistent format that can be used with the data scientist’s analytic tools. As computer science students know well, this simple-sounding task becomes increasingly challenging and time-consuming as a dataset grows in scale. In fact, some data scientists spend as much as 80% of their time “wrangling” or “munging” data before it’s ready to be analyzed. That’s where data engineering comes in. Data engineers evaluate, parse, and clean datasets, using programming languages like Python and R to build data pipelines and warehouses. This infrastructure efficiently delivers clean datasets at scale for data science to produce big data products. The data engineer’s specialized expertise becomes crucial to a company’s success as it grows; a startup that can only afford to hire one data scientist might have no choice but to direct 80% of their hours to data engineering. This inefficiency becomes a crippling as the company scales up. Just as data scientists and data engineers can sometimes be distinct roles within a company, top professionals in these fields can sometimes come from distinct educational backgrounds. While a data scientist typically might focus on math and statistical analysis, data engineers are often system thinkers and programmers at heart. As the data industry continues to develop, it’s becoming apparent that specializing in data engineering early in your education is a significant advantage for your career.

A Data Science Education For Data Engineers

The University of Illinois is one of the top-ranked computer science schools in the country, with an incredible history of pioneering research dating back to the 1940s. Its Online Masters of Computer Science in Data Science (MCS-DS) degree provides a top-tier advanced education for data scientists, and students that want to pursue a career in data engineering have the flexibility to choose courses that prepare them for that direction. The degree requires graduate-level coursework in data mining, cloud computing, data visualization and machine learning for all students, through courses such as Introduction to Data Mining, taught by Jiawei Han, author of the well-known textbook Data Mining: Concepts and Techniques. Computer science graduate students that want to become data engineers have plenty of opportunities to dive deeper as they fulfill their degree requirements. Advanced courses include data engineering-centric classes such as Theory and Practice of Data Cleaning and Data Curation. MCS-DS students with a data engineering focus can complete their education with a Data Mining Capstone Project. In this hands-on course, students learn the latest data mining research techniques in an online seminar. They also complete a major project that applies data mining techniques to solve a real-world challenge. This kind of interactive, face-to-face learning experience puts the MCS-DS on a higher level than typical online computer science programs.

How Do Data Scientists and Data Engineers Work Together?

A good way to explain how both these roles function is by using the analogy of civil engineers and architects. Architects are responsible for developing initial plans, while civil engineers work on finding structural limitations and implementing these plans. Similarly, in the world of data, data scientists direct analytical plans while data engineers focus on the technical implementation of core solutions. Both are required to work closely in order to integrate and streamline data to enable business solutions. Once a data scientist has worked on developing a model to process data, a data engineer will figure out how to incorporate this within the overall data processing pipeline. Through each step, both professionals must have good communication skills in order to easily consolidate ideas.

Is Data Engineering a Good Career?

According to a tech job report published in 2020, the data engineering jobs saw the highest year-on-year demand and it was the fastest-growing tech occupation, beating data scientists. Simply put, as long as businesses continue to use data to draw insights and make strategic decisions, there will always be a demand for data engineers in one form or the other. The rapid digital transformations, especially post-pandemic, have resulted in an explosion of data. This has given rise to a hike in demand for competent data engineers. Some notable tech companies that hire data engineers are Amazon, Accenture, and Capital One. And since data engineers make more than $110,000 a year, as per Glassdoor, it also emerges as a popular and well-paying career choice. In conclusion, if your aim is to target a complex business problem, integrating data engineering principles is likely to be the solution. Digital automation and data-driven insights are the future of business, and it is only a matter of time before every organization learns how to efficiently use data and harness optimum business solutions. Armed with this knowledge of what is data engineering, if you are keen to learn more, explore the extensive range of courses on data sciences on Emeritus, offered in collaboration with globally renowned universities.