can industrial engineers work as data scientists?

There is increasing demand for data scientists in industrial settings. However, many industrial engineers may not have the training or experience to work as data scientists. In this article, we explore whether industrial engineers can work as data scientists and provide tips on preparing for a career in data science.

Can industrial engineers work as data scientists?

The answer to this question is a resounding “yes.” Industrial engineers have the skills necessary to work as data scientists. They are experts in statistics and operations research, which are essential for data science. They also have experience working with large data sets and analyzing them to find patterns and trends.

In addition, industrial engineers are good at solving problems and making data-based decisions. This makes them well-suited for a career in data science.

Many industrial engineers are making a move to data science, and for a good reason. Industrial engineers have the skill set necessary to be successful data scientists. They are experts in problem-solving and data analysis, two key components of a successful data science career. Additionally, industrial engineers have strong math and statistics skills, which are also essential for data science.

There is not weird to ask this question reasons because data science is the second fast job growth in the united state is snowballing. In fact, according to recent estimates, the number of jobs in data science will grow by 22% by 2030. This means a significant shortage of qualified candidates for these jobs.

7 steps to becoming a data scientist from an industrial engineer background

1 – Start by learning statistics and programming

Industrial engineers who want to transition into data science should start by learning statistics and programming. Statistics will allow you to analyze data, while programming will help you understand how to manipulate that data. Having a good foundation in mathematics is essential, as many data science concepts are rooted in mathematics.

Once you have a strong foundation in statistics and programming, you can start learning the tools and techniques used in data science. This includes algorithms, big data platforms, and machine learning. It’s essential to focus on hands-on experience, so try to find projects or datasets you can work on.

There are many resources available for those who want to study data science. Online courses, MOOCs, and boot camps are all great options. Many books and articles can help you get started.

2 – Study machine learning

When it comes to studying machine learning, there are a few different ways people tend to go about it. One is to take online courses, which can be a great way to learn the basics of the subject. Another approach is to read books on the topic. Finally, many people attend boot camps or intensive courses that teach all aspects of machine learning.

No matter which route you choose, there are key things to remember when studying machine learning. 

First, ensure you understand the basics of statistics and linear algebra, as these concepts are essential for understanding many algorithms used in machine learning.

Additionally, try to practice implementing algorithms yourself, as this will help you better understand how they work. And lastly, be sure to focus on building models that perform well on real-world data sets.

3 – Deep learning

Deep learning is a subset of machine learning that is concerned with algorithms that learn to do things like recognize objects in pictures or understand natural language. Deep learning has been successful in tasks like these because it can learn features automatically from data without needing hand-crafted features.

This makes deep learning particularly well-suited for problems where the number of features is too large to be specified by a human. Deep learning has become very popular in the past few years, and many online courses are available that teach how to do deep learning.

However, most of these courses are aimed at people who already have some background in mathematics and computer science, which is the case for industrial engineers like you. But If you struggle in math, it might be difficult to learn deep learning on your own. One way to learn deep learning is to find a data scientist willing to mentor you.

4 – Take courses in databases.

There are a variety of database courses that students can take. Some of the most common courses include an introduction to databases, SQL, data mining, and big data. Students can find these courses at colleges and universities, as well as online.

When selecting a course, it is crucial to consider your goals and objectives. If you want to learn how to use a specific database management system (DBMS), then you should look for a course that focuses on that system. If you are interested in learning more about data analysis and mining, then you should merge these two topics, but the preference is to teach them each one alone.

5 – Learn big data

Big data is a broad term for data sets that are too large or complex for traditional data-processing applications. The techniques used to analyze and extract value from big data can be applied in various fields, including business, science, and engineering. However, the challenge for many people is learning to use these techniques.

One way to learn big data is to attend a seminar or workshop. These events provide a hands-on introduction to big data tools and technologies. Experts usually teach them in the field who can help you get started with big data quickly. Another way to learn big data is to watch online tutorials.

Many different tutorials are available, ranging from introductory videos to more advanced courses, watching these tutorials teaches you how to use popular big data tools and technologies such as Hadoop and Spark.

6 – Take internships

One way to gain the skills needed to become a data scientist is to take internships. Internships can allow you to learn about different aspects of data science and decide which area you want to specialize in. They can also help you build your network of contacts in the industry, which raise the ability to find jobs as soon as possible without girding.

7 – Network with other data scientists

In addtion to important internships, Networking with other data scientists in your area is very important.

Some likely numerous meetups and groups focus on data science. Attending these events will allow you to learn from others in the field, share your own experiences, and develop valuable connections.


In conclusion, industrial engineers can work as data scientists, but they may need to gain additional skills to do so. Data scientists are in high demand, so there are many opportunities for industrial engineers who want to make the switch. However, it is essential to be aware of the differences between these two roles to make the best decision for your career.

you could read this related article: can an industrial engineer become a business analytics

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