How Is Data Science Useful In General Engineering?

By 2025 it is estimated that 75.44 billion IoT (Internet of Things) connected gadgets will be in use. This figure represents a three times increase compared to the number of IoT-capable devices in 2019. An increase in these devices would also mean there will be a lot of data management and processing.

But even with this expected trend, Data Science is not always understood and seen as a complete problem-solving approach in many engineering disciplines. So, how is this discipline useful in general engineering?

First, what is Data Science?

Data Science is an interdisciplinary field related to machine learning and big data. The discipline leverages scientific methods, processes, algorithms and other technologies to extract insights from structured and unstructured data.

The data science workflow involves several complex processes such as data warehousing, cleansing, processing, staging, clustering, modeling, summarizing and acquisition. Its main goal is to extract knowledge from data and make predictions about future events.

The importance of data science is in general engineering

From chemical engineering, structural engineering to other disciplines like civil engineering, here is why data science is inevitable in general engineering:

  1. Engineering deals with data

Like more engineering streams, general engineering demands skills in Science and Mathematics. Every engineering graduate will consistently work with data, no matter their specialization. Data is the key that always drives the decisions of any engineer. Often, this data is expansive and massive.

Engineers need to be equipped with the necessary Data Science skills to handle comprehensive and detailed data. For indoor work, marketing and business functions, data scientists come in handy to help engineers deal with such massive data. Data scientists are less commonly found for hands-on work, such as in constructions, processing plants, and so forth. Hence, engineers need the right skills to avoid messing up with the data sets gathered by sensors in their work environment.

With the right data science skills, processing plants wouldn’t need to hire other companies to automate data gathering and analysis for their in-house engineers. They would save more on operating costs and adapt quickly to the constantly changing manufacturing environment.

  1. Engineers need to predict the future

To build a successful product or service, engineers have to foresee its potential failures before they happen. They need to understand the market, identify the trends and develop new products accordingly. In addition, they must know which features are essential to keep users satisfied.

This is where data science comes in. It helps engineers forecast the outcomes of their projects and products based on real-time data. With this information, engineers can make better decisions, improve their products, and save on costs.

Imagine you were in charge of reducing the operational costs of a car manufacturing company. The best idea would be to focus on sourcing cheaper materials? Well, probably not. It turns out that car manufacturers spend a lot of money to ensure that their cars meet safety standards.

Mercedes-Benz came up with a dataset containing 377 descriptors that would answer if a vehicle has passed the safety test or not. The algorithm can predict if cars have attained all the traffic standards quickly and with low carbon emissions.

Uber engineers have also developed a system that predicts its demand. The company can send drivers to places where they are likely to find customers more efficiently. The prediction uses data science to maximize sales.

The two examples above show how predicting the future is of essence to engineers. They can save time, operation costs, and manufacturing costs through the prediction and use their machines more.

  1. Data science makes things easier

Engineers often find themselves trapped in a cycle of trial and error when dealing with large amounts of data. As mentioned earlier, data scientists are trained to analyze data efficiently and they make use of several data analysis tools to predict outcomes accurately. Thus, they can provide valuable insight into the data set and help engineers make better decisions.

For instance, it is extremely challenging for engineers to predict any molecule’s biological response in chemical and medical engineering. In practice, it would mean that many clinical tests and trial and error experiments are needed before accessing the safeness and reaction of any molecule. This dataset has a series of molecules characterized by over 1776 descriptors. While it is almost impossible to predict the biological response of any molecule with such massive descriptors, data science makes it possible and easy.

  1. Data science is the future

In the next few years, we’ll see a rise in the number of IoT-enabled devices. More people will start using data science to solve problems as it’s easy to use and has many applications.

Engineers should also not be left behind either. They are the people who will be building some of the complex systems and machines we will have in the future.


Data science is indeed valuable to any engineer out there. With such skills, engineers will solve their own problems and employ machine learning without a computer scientist.

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