Mechanical engineers used to be the main driving force behind most businesses. However, with new digital technologies taking over manufacturing and product design, mechanical engineers have to develop new skills to keep up with the trends.
Robotics and big data are redefining the industry as we know it, and machine learning and artificial intelligence solutions are the go-to solutions for improving business practices. As a result, mechanical engineers are becoming data scientists in charge of developing ML and AI data-based solutions.
Difference between Mechanical Engineering and Data Science
If you have a background in mechanical engineering, you are probably facing the reality that your experience isn’t enough to cope with the latest technologies. Most advanced solutions today are completely digital, which means you need new skills to get things done. But where to start and what skills are the most important?
Both fields are critical when it comes to product development and system maintenance. However, most MEs today have to develop skills as data scientists to be able to provide the best results possible. Here are some of the differences between the two professions.
Machine engineering is a must if you want to ensure longevity for your business. It’s a practice that hasn’t changed much in the past few decades, and it still plays a critical role in manufacturing. It’s based on well-established theoretical knowledge still used to create systems and find mechanical solutions.
Engineers usually don’t have to learn new ME skills to stay relevant. The best MEs become masters of classic tools and methods applied to hardware design, machine control, etc. Again, most of these practices are well-known and don’t change with time.
It takes a lot of effort and hard work to become a successful mechanical engineer. Some go through years of college education, but some prodigies can create impressive solutions without higher education or a degree. As long as they spend a lot of time working on projects, they can master mechanics and excel in the field.
ME is a well-researched and well-established practice that has defined approaches and solutions to various problems. Most engineers spend their time looking for the best solution rather than making new ones. Whenever they find one, the solution will keep providing the same benefits for a long time.
Data science is a fast, ever-changing practice that relies on trends and data management. It uses advanced technologies such as AI and ML to develop new solutions to unique problems. In other words, data scientists never stop learning and evolving their skills to meet the current market demands.
Another major difference revolves around available tools. For example, data scientists use various software solutions to train ML systems in various settings. Moreover, data scientists have to choose the right tools based on the people around them. Other team members will have to work on the same project, which means they need to use the same tools as you.
When it comes to education, having a degree makes all the difference. Having a degree helps data scientists land a job easier, which is why less than 20% of them don’t have one. If you have a degree in another field such as ML or computer science, you can easily switch to data science. If not, you will probably have a hard time finding work.
With that said, data science is far more exciting than ME. Unlike mechanical engineering that focuses on finding an existing solution to a problem, data science develops new solutions to fix the problem. Moreover, AI and ML systems can find all kinds of issues that need optimization.
AI can automate most efforts, but since it’s programmed and set up by a human, it too can become obsolete over time. Some solutions last for years, while others have a lifespan of only a few days. Lastly, data scientists have a much higher salary than MEs.
The Shift Towards Complete Digitalization
As you’ve probably noticed, the entire world is evolving from an analog, mechanical method to a digital environment. That is only possible because of new technologies such as machine learning and AI. Mechanical engineers are still important, but they have to keep up with the latest practices to stay relevant. Of course, they already have some programming and robotics skills that can help make the transition to machine learning engineers and data scientists much easier.
If you have a background in mechanical engineering, you are well aware of your limitations. That’s where things get a little trickier. Instead of looking back at all the knowledge you’ve gathered over the years, you have to look to the future and focus on mastering the latest ML tools.
Existing Skills That Make a Difference
Of course, coming from a ME background, you’ll have some skills that will come in handy in your new profession as an ML engineer. For example, you know that tools, hardware, and machines have their limitations. In that sense, you won’t have to develop skills to keep up with the latest trends.
You also have a good understanding of technologies such as PID control and Kalman Filter. They still play an important part in most industries. You are also used to completing monotonous tasks and trying various solutions and tools to get the job done. All of these skills can help you become a better data scientist, but only if you expand them with a new skill set.
Developing New Skills to Master Machine Learning
Let’s say you’re an experienced mechanical engineer looking to become a data scientist or an ML developer. Your existing skills will give you a nice head start, but you’ll have to develop new skills to make it in this new industry. An ML solution company called Wizata has plenty of former MEs working as data scientists and ML engineers. Here are some of the must-haves if you want to become an ML engineer.
1. Statistics and probability
Data and statistics work hand in hand, especially for creating new ML systems. You need skills in conditional probability, likelihood, as well as techniques such as the Hidden Markov Model, Bayes Nets, Markov Decision Process, etc. Knowing these rules is a must as most ML models use algorithms based on statistics.
2. Programming and Computer Science
Most mechanical engineers don’t learn to program in school, so you’ll have to learn the basics to get started. That includes details on data structures, computability, complexity, architecture, and algorithms. All of these practices are a part of machine learning engineering, so you won’t be able to continue without mastering all of them.
3. ML Algorithm Application and Libraries
There are a few ML libraries you can use to build your own ML algorithms. Websites such as MLlib, TensorFlow, H2O, Spark, and other APIs offer all kinds of algorithms you can use.
However, making them work in a new application won’t be easy. You will spend a lot of time looking for suitable models that fit the data you’re working with. Not to mention the challenges you’ll face when trying to include neural nets, decision trees, and other methods.
4. Data Modeling
Data modeling and filtering are a major part of developing any ML system. ML engineers have to find patterns in available data sets, create strategies that will help filter the data, and set up backups in case of data loss.
5. ML Design
The end goal of any ML engineering job is to create an ML system that can learn from incoming data. The challenge is to create a system that fits into legacy systems to improve their efficiency, help with product design, etc. As an ML engineer, it is your job to find pieces that fit together, communicate the steps with other team members, and provide an interface everyone understands.
Transition Process Duration
It’s hard to estimate how long an average ME takes to develop the skills needed to transition to ML engineering and data science. Everything depends on the person’s ability to learn and the time they invest in the process.
Think of it as a long-term investment that will pay off in the end. Some people learn everything quickly, so their transition may take as little as a few months or maybe a year. However, if you have to focus on work while learning these skills, it might take you a few years to master them. You should hire an experienced ML engineer as a mentor to ensure that you learn the skills first.
As a mechanical engineer, you already have an excellent background in logic, math, and physics. If you expand your knowledge with statistics, data analytics, and modeling, you can easily build a successful career as an ML engineer.
It will take you some effort, but you will secure a higher salary and a better position in any company. Everyone needs an ML engineer these days, so think of this transition as an investment for the future.
Travis Dillard is a business consultant and an organizational psychologist based in Arlington, Texas. Passionate about marketing, social networks, and business in general. In his spare time, he writes a lot about new business strategies and digital marketing for Finddigitalagency.