Data is collected every second of every day from more sources than you are probably aware of at any given moment. From the security cameras that capture your face when you enter a building to the mobile devices that track your shopping, media and communication habits, there are images and numbers continually being collected by government agencies, consumer groups and other organizations from all around world.
To ensure that these copious amounts of information are leveraged effectively, business and other groups are hiring professionals known as 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 a number of significant ways, which often makes professionals with an engineering background a good fit for a role working with data.
Whether you are looking for a position where you can construct systems or analyze information, specialized knowledge in data can open new opportunities for a rewarding position in the field that also uses your knowledge of engineering. And with a master’s degree in engineering with an emphasis in data science, you will be well on your way to a promising career.
The rise of data science
The collection and analysis of data is becoming increasingly important across most every industry. Fields that collect this information include marketing, sports, entertainment, medicine, communications, government, criminal justice, electronics, aerospace and many more. Data can help companies made decisions 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 world and verify the results.
The amount of data the world is producing is increasing immensely. According to IBM, 90 percent of the global data that currently exists was gathered in the last two years alone. Every day, the world creates 2.5 quintillion – or 10^18 – bytes of data. To put that number into terms that are easier to digest, considering that the average movie is around 2 gigabytes, the world creates the equivalent of approximately a billion movies worth of data every single day. It is a quantity that is 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. For example, The Washington Post reported that every minute 100 hours of video footage is uploaded to YouTube alone. That translates to 144,000 hours – or about 16 and a half years – worth of footage every single day.
The sheer volume of data that is being collected opens up worlds 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 – 10^15 bytes – of data annually. Without such large quantities, the resolution would suffer and the effectiveness of these experiments would decline. But likewise, if the right professionals are not in place to ensure that the procedures are good, 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 then been able to apply data analysis tools to extract useful data that has revolutionized how scientists view the mechanics of the world.
Data and engineering
Engineering is one particular industry that has been influenced by the growing necessity of 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 that 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 to 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 handheld 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 a self-started who is intellectually curious will take you far in this role.
Career opportunities in data science
Because the intersection between data and the field of engineering can prove invaluable for leveraging this information effectively, career opportunities are plentiful for the qualified professional. A background in engineering usually signifies to employers that you have the analytical skills that 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, demand for computer and information research scientists – the category which houses data scientists – is expected to increase by 11 percent between 2014 and 2024, a faster than average rate. The BLS further reported that the compensation for these positions is generous. In 2015 the median annual salary for a computer and information research scientist was $110,620. Similarly, the job and salary website Glassdoor reported that the average annual salary for a data scientist in the U.S. is $113,436. But depending on your position and experience, it is possible to make much more. The source reported that some data scientists make closer to $150,000 a year.
Data scientists are hired across most every industry by companies as diverse as Airbnb, Twitter, Capital One and Allstate. Some job titles worn by engineers working in data other than data scientists include:
- Data engineer: This role is specifically responsible for designing and building the systems through which data is collected and ensuring that the architectures operate smoothly.
- Chief data officer: In this leadership position, you will shape policies and procedures related to how your organization uses data. You will also likely oversee staff teams to ensure that all the components of the process are operating correctly.
- Data specialist: As opposed to a data engineer, as a data specialist you will primarily be responsible for the collection of data. Research is an important component of the role, and you will likely look not just at the data itself, but examine the method by which it was obtained.
- Data analyst: Similar to a data specialist, as a data analyst you will work to translate numbers into meaningful pieces of information for your company. You can apply these skills to more specialized roles, such as in a position as a social media data analyst.
While these positions are all promising choices for your career in data science, they are far from your only options. Each industry has its own unique job titles for the professionals who work with their data.
Your Master of Science in Engineering degree
If you are interested in starting – or furthering – your career in data science, earning an MS in engineering can be a strategic next step. A higher degree not only deepens your knowledge of the particular field that you are studying, but will also prepare you to take on a leadership role in an organization.
When you pursue an online engineering master’s degree at University of California, Riverside, you can choose to complete your degree with an emphasis in data science. In the program, you can complete specialized coursework in topics such as machine learning, statistical mining methods, data visualization and advanced computer vision. The specialization will prepare you to specifically 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 specifically 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 not only increase your knowledge of data, but demonstrate to employers that you have this particular area of expertise.
At UCR, the engineering master’s program is designed to be completed in 13 months. For convenience, there are three start dates available, which means that you can begin during the time of year that is most convenient for your schedule. At any time, you can choose to take the next step in your career in data science.