Interested in AI, machine learning and data science? Computer engineering may be the right career for you. Data science is rapidly becoming one of the most widely referenced skills in job ads for computer engineers. You’d be right, though, to wonder what it is. Let’s take a look at what you should know about data science.
What is Data Science?
Few fields are as multidisciplinary as data science. It has a strong foundation in both mathematics and statistics. The rise of computing power over the last decade has driven a need for computer engineers who can set up systems, write programs and deal with the warehousing issues.
Data science has increasingly become part of decision-making at corporations, government agencies and other large organizations. This means data science has seen an increase in demand for people who can create infographics, dashboards and similar visual products that convey insights.
AI and machine learning trends have also had a huge impact on the world of data science. Much of the work over a decade ago was handled using software packages like Microsoft Excel, a growing portion of the data science workload is now handled using programming languages like Python and machine learning libraries like Cuda, Conda and OpenCV.
At its core, data science is about applying a mixture of disciplines to produce insights. Large amounts of data can be analyzed using various deep learning processes to unearth patterns that might not be easily recognizable by a person.
Applications of Data Science
With the growth of the field in recent years, it’s almost impossible to produce an exhaustive list of ways to deploy data science. It is, however, instrumental in handling applications like:
- Trend analysis
- Network theory
- Machine vision
- Artificial intelligence
- Financial analysis
- Business intelligence
Many of these applications overlap. For example, the U.S. military frequently works with computer engineers who use trend analysis, network theory and financial analysis to track terrorist networks. Companies like Tesla are using machine vision and artificial intelligence to work toward the dream of producing the first fully self-driving cars. Analysts in Major League Baseball use data science to discover ways to improve player performance. If there is an industry where numbers matter, you can bet someone is figuring out how to use data science to do the job better.
Necessary Skills in Data Science
While computer programming skills are becoming much more deeply rooted in data science as a profession, it’s generally not a job that requires the level of coding skills demanded of advanced software engineers. Most programmers would consider the core coding skills in data science to be of intermediate skill, with the Python language often used by mathematicians and statisticians often considered a great beginner’s language.
That’s not to say that a language like Python isn’t powerful. In fact, many computer engineers are leveraging machine learning libraries for these languages to produce neural networks for AI applications.
The increasing value of data visualization also means that there is greater demand in data science for people who can communicate visually with both artistry and integrity. Similarly, experienced computer engineers are now moving into data science to develop better interfaces for dashboards, websites and other points of data access. Dropping a pie chart into a PowerPoint doesn’t impress as much as it used to, and even people who see themselves predominantly as coders often use libraries like PyPlot to create visualizations from data.
Reading and manipulating data is also important. Spreadsheet packages like Microsoft Excel and Google Sheets are in every computer engineer’s toolbox.
Many computer engineers also help with setting up infrastructure. A computer engineer may be asked to handle tasks like:
- Setting up servers for other engineers
- Configuring and maintaining databases
- Compiling and installing machine learning libraries
- Building data warehousing solutions
- Securing networks that are used for data science work
It’s safe to say that an engineer needs to be good with a computer to perform data science. They should also have an understanding of mathematics that extends past Algebra II into more advanced studies including matrices. It doesn’t hurt to know some calculus. Likewise, a solid foundation of statistics is essential to assessing the value of models and the validity of insights. Especially when a computer engineer gets involved with more complex mathematical structures like tensors, it’s wise to have built the knowledge base solidly around these academic fields.
What Does a Computer Engineer Do with Data Science?
Computer engineers in data science are frequently involved in setting up experiments and producing analysis. A sociologist, for example, might consult in data science to determine how to move forward with a project. The computer engineer would then help the sociologist with tasks like:
- Finding or developing data sources
- Cleaning up the available data to ensure machine readability
- Validating datasets
- Identifying statistical models that might be applied
- Collecting the necessary resources to perform analysis and warehouse information
- Validating derived insights
- Developing presentation materials
- Making data and insights available to others
Many of these tasks also involve critical subtasks. A computer engineer looking at the possible models that might be applied to a project may have to study existing work, consider potential pitfalls, identify possible biases and develop the code to implement the model. Similarly, they may have to assess which frameworks are best for the job, such as deciding between using PyTorch and Keras.
Computer engineers using data science also have to communicate issues to people with less grounding in math, computers and statistics. Someone working for a major corporation might have to help onboard C-level executives who need to adopt the features of a data-centric culture.
There has been a rise in positions like Chief Data Officer at companies. These individuals have to help organizations deal with challenges related to:
- Individual privacy
- Regulatory compliance
- Trade secrets
- Security
- Data infrastructure
- Fostering a data-driven culture
Communicating with others is becoming more important in data science. It’s not uncommon to have to send requests for data to vendors, academic institutions and government agencies. That often means sending emails back and forth. It also tends to involve keeping a traceable trail of interactions in case there’s an audit.
Computer engineers in data science should also be capable of presenting findings. This includes building reports and writing papers, and it may require some level of visualization. Generally, the more a computer engineer can do, the more value they will be to various projects. They are in more demand if they are good at communicating with the public and people who have lesser numeracy skills.
Who Gets into Data Science?
People who have a lot of curiosity and an unwillingness to let go once something has captured their attention tend to thrive in data science. That means it frequently attracts tinkerers, mechanics, programmers and game players. If others have often felt that someone comes off as a bit obsessive once they get an idea in their head, there’s a good chance they will have fun in data science.
Plenty of overlap exists among people who like to play certain types of games and those who want to build algorithms, develop models and code routines. They will encounter many people who enjoy playing games like chess, go and Dungeons & Dragons.
A lot of people who like to work with complex physical systems do well in this field, too. This includes people who build and repair engines, use drones, make models and understand chemistry. Makers often excel in working with datasets, too.
Some people come into data science from related areas. It’s not uncommon, for example, for a mathematician to start dabbling in coding because they need to cycle through information more quickly. Pretty soon, they find themselves installing databases and frameworks to expand their processing power. Eventually, they quit the tinkering and get serious about developing programming and machine learning skills to do what they desire. Statisticians, engineers and scientists will also frequently find themselves following a similar path.
As previously noted, there’s a growing demand for individuals who can bring an artistic vision to information. Presentation and reproduction graphics work can push people to look at where the information their working with is coming from. It’s not long before they’re using basic database queries to grab data for producing graphics.
Many people come to data science from other fields. A person working in finance may quickly get tired of looking at candlestick charts and want to get into the actual data and algorithms they’re looking at. Even executives may get breakdown a fresh dataset that is coming down the pipeline. Buyers and sellers are motivated to become data-centric, too.
The unifying features these people exhibit is work ethic and curiosity. Data science demands creativity and diligence, and that’s not always a common combination of qualities in a human being. There’s a reason the D&D crowd is overrepresented in these fields. A good dungeon master works hard and takes care of the little details with enthusiasm.
Conclusion
If you are an insatiably curious person who wants to be at the edge of innovations, there’s a lot of room to participate in data science. In fields ranging from ecology to spaceflight and sports to banking, many organizations need capable engineers to work in data science.
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