Engineering Data Analysis: Why It Matters

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A data scientist prepares a report on a computer.

Data is collected every second of every day from a vast array of sources. From the security cameras that deploy facial recognition technology when people enter a building to the mobile devices that track shopping, media, and communication habits, images and numbers are continually being collected by government agencies, consumer groups, and other organizations from all around the world.

This data contains information that can help businesses operate more efficiently and reach the right customers. However, to be of any value, data must be correctly interpreted. Misinterpreted data can lead to flawed insights that could disrupt an organization’s growth and stability strategies.

To ensure that these copious amounts of information are leveraged effectively, businesses and other groups are hiring data scientists to help collect, store, and analyze pertinent information. While these professionals come from a variety of backgrounds, the growing field of data science provides a number of rewarding opportunities specifically for engineers. The fields overlap in significant ways, which often makes professionals with an engineering background a good fit for a role working with data.

For those looking for a position that involves constructing systems or engineering data analysis, specialized knowledge in data can open up new opportunities for a rewarding position in the field. By earning an advanced degree such as a master’s degree in engineering with an emphasis in data science, individuals can be fully prepared to embark on a promising career.

What Is Data Analysis?

Data analysis involves gathering and studying data to form insights that can be used to make decisions. The information derived can be useful in several different ways, such as for building a business strategy or ensuring the safety and efficiency of an engineering project.

Data collection and analysis is becoming increasingly important across most every industry. Fields that collect this information include marketing, sports, entertainment, medicine, communications, government, criminal justice, electronics, and aerospace. Data can help companies make decisions on issues as diverse as how to engage their target audiences, what purchases to make, and how to organize their staff members. Ultimately, data science is not just about collecting and analyzing information. It is about being able to predict the future and verify the results of past decisions.

The amount of data generated is staggering. Roughly 2.5 quintillion (1018) bytes of data were generated daily in 2021. Considering that the average standard-definition two-hour movie stream on Netflix is around 2 gigabytes, the world generates as much data as a billion movies in that format per day. Moreover, it’s projected that the world will house 200 zettabytes of data by 2025. It’s also estimated that 90% of the data that exists in the world has been generated in the past two years. These are quantities that are hard to comprehend, much less use effectively.

And this information is not just numbers. Images and video are also an important component of the global collection of knowledge. A February 2020 poll released by the data compilation site Statista reported that 500 hours of video footage are uploaded to YouTube every minute. That translates to 720,000 hours — or a little over 82 years — worth of footage every single day.

While the sheer amount of data can seem overwhelming, the insights that can be derived from it can open up a wealth of new opportunities for industries around the world. Take the field of physics, for instance. Technological advances have allowed large-scale science experiments, such as the Large Hadron Collider in Geneva, to collect petabytes — 1015 bytes — of data annually. Without such large quantities, the resolution would suffer and the effectiveness of these experiments would decline. But if the right professionals are not in place to verify the procedures, those large amounts of information would be worse than useless. By gaining access to this critical data and learning how to properly handle it, physicists have been able to apply data analysis tools to extract useful data that has revolutionized how scientists view the mechanics of the world.

Data Science and Engineering

Engineering is one industry that has been particularly influenced by the growing need for data collection and analysis. As big data has begun to play a larger role in industries around the world, engineers have been called on to play an influential role in the way this information is gathered, stored, and leveraged. Professionals with an engineering background generally prove to be particularly adept at developing techniques for analyzing data groups to extract valuable information.

To succeed in a career as a data scientist, an engineer should possess the following qualifications:

Analytics expertise: Experience extrapolating information from large quantities of numbers will help you succeed in this role. Depending on where you work, knowledge of specific analytic tools will also likely be required.
Computer knowledge: Gone are the days of crunching numbers on a hand-held calculator — much less with pen and paper. The vast majority of your day will be spent working on a computer, so knowledge of coding, unstructured data, and cloud tools will increase your marketability.
Communication skills: It is important to be able to present your findings in a clear and concise way to ensure that your employer understands the information and can act accordingly.
Strong drive: In data science, you should regularly be looking for ways to improve how information is collected and processed. Being an intellectually curious self-starter will take you far in this role.

4 Data Science Careers

Because the intersection between data and the field of engineering can prove invaluable for leveraging information effectively, career opportunities are plentiful for the qualified professional. A background in engineering usually signifies to employers that you have the analytical skills you need to thrive in one of these roles, opening doors for positions you may not have considered when you first began your education.

The job market is currently friendly for engineers who would like to pursue a career in data. According to the U.S. Bureau of Labor Statistics (BLS), demand for computer and information research scientists — the category that houses data scientists — is expected to increase by 22% between 2020 and 2030, a growth rate that is substantially faster than the 8% rate the BLS predicts for the labor market as a whole. The BLS further reports that the compensation for these positions is generous. The 2020 median annual salary for a computer and information research scientist was $126,830. Similarly, the job and salary website PayScale reported that the median annual salary for a data scientist in the U.S. was approximately $97,000 as of December 2021. But depending on your position and experience, it is possible to make much more. The source reported that some data scientists make closer to $134,000 a year.

Data scientists are hired by a diverse array of companies across almost every industry, from insurance and hospitality to banking and social media. The following is a cross-section of some specialized roles for data scientists:

Data Engineer

Also known as database administrators, data engineers design, build, and organize systems that collect, store, and secure data for others to access and analyze. The databases they create can be built around the specific needs of a user. They can also periodically test databases to ensure their operational efficiency. The BLS lists the 2020 median annual salary for this role at $98,860.

Chief Data Officer

A chief data officer, or CDO, oversees the collection and application of an organization’s data. This C-suite leadership position also builds different policies concerning data collection and compliance, and develops data strategies that align with an organization’s goals. According to PayScale, the median annual salary for CDOs as of November 2021 was around $176,000.

Data Specialist

Data specialists focus on data collection. Research is an important component of the role, which often requires diving into the methodologies used to produce given data sets. PayScale reports the median annual salary for data specialists as of November 2021 was around $49,8000.

Data Analyst

Data analysts translate numbers and other forms of data into actionable pieces of information that can be used to explain current situations and predict future behaviors. According to PayScale, the median annual salary for data analysts as of December 2021 was approximately $62,400.

Earn a Master of Science in Engineering Degree

Data science is a burgeoning and important field. As more data is generated, qualified professionals to gather and make sense of it become increasingly necessary. For those interested in integrating data analysis within an engineering environment, earning an MS in engineering can be a strategic next step. A higher-level degree not only deepens an individual’s knowledge of a particular field, it also prepares them for leadership roles.

The University of California, Riverside’s Master of Science in Engineering program gives you the option to specialize your degree with an emphasis in data science. In the program, you can complete coursework in topics such as machine learning, statistical mining methods, data visualization, and advanced computer vision. The specialization will prepare you to enter a role working with data after you graduate.

This MS in engineering with an emphasis in data science can also be a strategic move if your undergraduate degree is in a subject not related to data science. If you already have a bachelor’s in mechanical engineering, for example, the master’s degree can be your opportunity to increase your knowledge of data and demonstrate new expertise to employers.

At UCR, the engineering master’s program is designed to be completed in 13 months. There are three start dates available, which means that you can begin during the time of year that fits your schedule best. At any time, you can choose to take the next step in your career in data science.

Recommended Readings:

An Engineer’s Role in Machine Learning

What Is Data Science?

What’s the Difference Between Data Visualization and Data Analytics?

Sources:

Allconnect, “How Much Data Are You Using to Stream Your Favorite Netflix Shows?”

CERN, Storage

CIO, “What Is a Data Analyst? A Key Role for Data-Driven Business Decisions”

CompTIA, What Is a Data Specialist?

Datapine, “Your Modern Business Guide to Data Analysis Methods and Techniques”

Indeed, “What Is a Chief Data Officer?”

TheNextTech, “How Much Data Is Produced Every Day 2021?”