Data Science in Mechanical Engineering – is it Useful?

It’s vital to know how to wade through information, especially in an era when over 1.7 megabytes of data is generated by every person each second. The ever-growing volume of data has prompted businesses to hire skilled data science professionals to help collect, store, analyze, and interpret information. According to the US Bureau of Labor and Statistics, the demand for data scientists is expected to grow by 15 percent by 2029[1]. This is about 11 percent more than the average growth of other occupations.

While data scientists come from different backgrounds, the exponentially growing field is more accommodative for engineers. Mechanical engineering overlaps explicitly with data science in several significant ways, making professionals from this background a perfect fit for a data science role. People with both mechanical engineering and machine learning expertise will be in high demand in the coming years.

So how is data science useful for mechanical engineering? In this article, we will discuss why mechanical engineers must learn data science and how to transition into a data science role. But before we get to that, let’s talk about data science for mechanical engineers.

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What is data science?

Data science is an interdisciplinary field of study that combines programming skills, domain expertise, mathematics, and statistics to extract knowledge and gain insights from large volumes of data. It includes predictive, descriptive, diagnostic, and prescriptive capabilities. This means that businesses can leverage data science to determine what happened, why it happened, what will happen, and what actions they need to take regarding the anticipated results.

Data science relies heavily on artificial intelligence and its subfields, such as deep learning and machine learning to create models and make predictions using algorithms and other techniques. Conceptually, the data science process consists of a five-stage life cycle, including:

• Capturing: It’s the process of data acquisition, data entry, and signal reception
• Maintenance: Includes data cleaning, staging, and warehousing
• Processing: Data mining, modeling, and summarization
• Communication: Data reporting, visualization, and decision making
• Analysis: Includes predictive analysis, text mining, regression, and qualitative analysis

All the stages mentioned above require different skill sets, techniques, and programs.

Data Science vs. mechanical engineering

It is vital to distinguish these two areas of expertise when it comes to data science for mechanical engineers. D.S. and M.E. use different tools, technologies, and even have different education paths. Let’s take a closer look at these elements:

Data Science

Tools: Has a wide range of tools. There is no particular way of doing work, and so your choice of tools might not be the same as the rest of your team.

Technologies: Data science technologies change frequently. Given that it’s a wide field, you’ll not only have to cope with changes in artificial intelligence but also in software engineering.

Education: According to studies[2], less than 30% of data scientists don’t have master’s or doctorate degrees. This means that interviewers tend to screen job candidates for a higher education degree.

Problem-solving: Data science has a wide scope of problem-solving requirements. It can be full of surprises sometimes. Don’t forget that data science seeks to solve problems with the help of data, and data is unpredictable-so is the lifespan of the solutions that you’ve put in place.

Mechanical Engineering

Tools: Has a limited set of tools. For example, there are specific tools designed for machine control, hardware design, etc. These tools don’t have to be updated often as they stay valid for a long time.

Technologies: Mechanical engineering technologies are not subject to frequent changes. They stay valid for a long time, and therefore you don’t have to keep learning to stay on top of the game. Technologies such as PID control and Kalman for example, are quite old, but still useful and up-to-date.

Education: To be considered a skilled mechanical engineer, you have to have many years of experience in a particular ME concentration. You don’t always need a higher education degree.

Problem-solving: Mechanical engineering can get a little bit boring if you’re an explorer by nature. Most of the problems in this field have well-defined and tested solutions that can stand the test of time. Machines are quite predictable and consistent, and therefore the main part of your job involves choosing the right tools and solutions.

Mechanical engineering and data science have different roles in science and technology, and therefore comparing them together might be improper. However, it’s important to point out the characteristics of both fields to get a sneak peek of what you are getting into if you want to transition from mechanical engineering into data science and vice versa.

Relationship between mechanical engineering and data science

Now that we’ve gone through the difference between mechanical engineering and data science, it’s only fair to discuss how the two fields reinforce each other.

Data science for mechanical engineers

As we’ve highlighted earlier in this article, most mechanical engineering foundations have been valid for many years, with some of them dating back to nearly a hundred years ago. We’ve also highlighted that one of the main problems mechanical engineers face is the limited set of available tools. Clearly, this domain needs a touch of technology.

Data science methods can help mechanical engineering teams to foresee any of these problems before they happen. Appropriate analysis, visualization, and prediction techniques can be used to assess equipment performance and other factors that might negatively affect the workflow.

Why is mechanical engineering important to data science?

Mechanical engineering is also equally important to data science. So it’s appropriate to say that the relationship between the two disciplines is a symbiotic one. Machine learning, for example, is an integral unit of data science. The future of machine learning will be deeply rooted in physics- The more we want to use machine learning in solving real-world problems, the more we will need professionals who can work with non-linear and dynamic systems in an environment with constantly changing factors.

Mechanical engineers have long been working in different areas. They have much to offer due to their experience with sensors, systems, and fluids. For instance, they can collect and convert energy from sources such as the sun, wind, and waves and account for turbulence in airplane designs.

Read more about Data Science vs. Data Engineering

Why should mechanical engineers learn data science?

Mechanical engineers with a background in data science can easily connect the dots in massive datasets within an organization. Besides that, there are several other benefits that a mechanical engineer reaps by studying data science.

Read on to find out:

• By learning data science, mechanical engineers gain value over a short period. This means that they can demand a higher salary or switch to a higher-paying job.
• Data is the backbone of various decision-making processes in every organization. Engineers are exposed to data in their scope of work, meaning that their decision-making skills are already highly based on predicted data outcomes. But it’s all different when dealing with massive datasets. Learning data science can help you handle big data and make effective decisions based on that. And as a result, you can handle the works of data scientists and data analysts within your organization.
• By studying data science, mechanical engineers can understand different programming languages, making it easy to create scalable and efficient solutions.

The transition from mechanical engineering to data science

How do you make a transition from mechanical engineering to data science? Data science is like a cocktail of different mechanical engineering necessities. And therefore, switching your career would require you to expand your knowledge and skills in data science tools. Mechanical engineers have the best foot at the door when it comes to transitioning into data science. They have a strong background in physics and mathematics, which helps learn machine learning tools, data analytics, and other technological aspects of data science.

Engineers with many years of experience working in different fields might feel at ease when trying their hand in data science. However, the transitioning process might be extremely daunting for beginners due to their inexperience in the market. Unlike their veteran counterparts, freshmen don’t have a statistical mindset, an essential aspect of data science.

To patch this gap, new mechanical engineers should work harder to become experts in handling large volumes of structured and unstructured data. We’ve rounded up a list of three main skill sets that you need to master to make the cut into data science.

It might also be interesting for you: The transition from Business Intelligence to Data Science

Programming

Mechanical engineers are conversant with materials and processes, not codes. Programming takes a backseat (or no seat at all) to what they focus on during their day-to-day tasks. In fact, many of them end up on factory floors instead of the design offices.

On the other hand, data scientists are hardcore software engineers who build models that store large data volumes. This means that their level of programming competency needs to be over the roof. To transition into data science, you will need to master different programming tools. There are various online courses that you can enroll in to learn and sharpen this skill set. You can learn each data science tool in isolation, and it can be broken down into granular modules.

Let’s have a look at some of the most critical programming tools that you should master for a smooth transition into data science.

• Python: It’s the most widely used programming tool in data science. This is because of its wide range of uses. Data scientists use python in domains such as machine learning and deep learning to develop algorithms such as random forests and decision trees. One of the main reasons why this language stands out from the rest is its flexibility. You can create solutions for a wide range of use cases if you combine it with a strong aptitude for quantitative reasoning.
• Java: Java remains a favorite tool for many data scientists due to the level of scalability it provides. Its ability to build complex applications from scratch and deliver results fast is unmatched. Despite it being considered a language for beginners, java is quite powerful. It can be used to execute complicated tasks such as data mining, analysis, natural language processing, and implementation of machine learning processes.
• R: R has outpaced several other languages to become one of the most popular languages in data science. This is because of the powerful scripting language that makes it viable for handling complex datasets. It’s mainly used to perform statistical operations.
• SQL: SQL is an important tool used to handle unstructured data. It gives access to data and statistics, making it a vital resource for data science. It’s the standard and most used language for relational databases.
• Scala: Scala is a great language for data science amateurs. It’s ideal for handling huge datasets and can be interoperated with java to unlock new opportunities for a data scientist.

Math and statistics

Statistics and math, in general, are often the most daunting domain to crack for aspiring data scientists. However, having a mechanical engineering background means that you have a rigorous understanding of these disciplines. In this case, you don’t need to take an advanced degree to master the number of statistics necessary to perform effective analysis.

Math and statistics are essential in data science because they form the basic foundation of every machine learning algorithm. You need to strengthen your statistics game to be able to put weight on your insights.

Several statistical functions and algorithms are used to analyze raw data and build models that predict outcomes. You will need linear algebra, statistical tests, metrics, regression, and conditional probability to get started. There isn’t any particular order of learning statistics until you fly off the handle in terms of the efficacy of your models.

Domain knowledge

Businesses work under significant financial and time limitations while maintaining their place in the highly competitive market. This is why they hire data science professionals hoping to get solutions to the problems they face.

Anyone with a background in programming and statistics can train a model with minimal effort. However, it takes a data scientist with substantial subject matter experience to give viable solutions to problems in an organization.

This is why domain knowledge is an important skill to have as an aspiring data scientist. It’s often neglected because it’s not as hard to pick up as mathematics and programming. But in the real sense, it’s like the icing on the cake. Below are three main aspects of domain knowledge that every aspiring data scientist should have in mind:

• The source of the problem that an organization is trying to solve
• Knowledge of domain data collection mechanisms
• In-depth knowledge of how the business operates

Keep in mind that domain knowledge can be acquired while you’re working. It isn’t quite difficult, and neglecting it could hurt your career.

Where do you fit in data science?

Data science is a broad field that accommodates different job roles. If you’re looking to make a career transition, you need to do your research well to understand the various opportunities in the data science industry and know the skill set required for the role you are eyeing. Below are some of the typical job roles in data science:

• Data Analyst: Data analysts collect, process, and analyze large data sets. They work on ways in which data can be used to solve problems. Besides programming and mathematical skills, data analysts also need to possess data visualization and data wrangling skills. This role acts as a bridge between data science and business analysis.
• Data Engineer: A data engineer works in various settings to build systems that collect, manage, and convert data into meaningful information that data scientists and business analysts can use. They mainly work with programming tools such as Java, Scala, Apache, and Hadoop. Their ultimate goal is to make data accessible so that businesses can use it to optimize their performance.
• Data Scientists: They possess technical skill sets to solve complex problems and the curiosity to discover the problems that need to be solved. They’re partly computer scientists, partly trendspotters, and partly mathematicians. And because they act as links to the IT and business worlds, these professionals are highly sought after and well-paid.
• Business Analysts: Business analysts are a little bit different from the rest of the team. While they understand how data technologies work, their focus is mainly on the business model. They identify how data can be used to gain actionable business insights for business success.

Data science in mechanical engineering: summary

Technology has popularized data science professionals, making the hype around mechanical (and other types of engineering) engineering die out real quick. However, it’s not the end game for you if you deal with mechanical engineering. You still have a chance to make a comeback into the limelight by learning data science.

After all, there is a huge demand for mechanical engineering and data science professionals.

All you need to do is learn programming, up your math game, and find a project that you can hop on to sharpen your skills. If you can learn bending movement, fluid mechanics, and the law of gearing, then learning how to play with data is a walk in the park.

References

[1] Bls.gov. U.S. BUREAU OF LABOR STATISTICS, Occupational Outlook Handbook. URL: https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm. Accessed Sep 27, 2021.
[2] Medium.com. Where do data scientists come from?. URL: https://medium.com/indeed-engineering/where-do-data-scientists-come-from-fc526023ace.  Accessed Sep 27, 2021.