what will be the best choice for you: industrial engineering or data science? this is what we’ll discover in our post today.
Which is better industrial engineering or data science
According to bls the average salary in industrial engineering is $95,000. While data scientists earn better by $5,000, meaning $100,000 every year. both occupations have almost similar salaries but when we look at the job opportunities and growth rates there is a big difference.
It is expected that industrial engineering will be growing by 14% in the next 10 years given 230,000 new jobs occupations. The data science Market is expected to grow by 31% in the next 10 years, offering 11.5 million in 2026 Which is mind-blowing.
Data science was categorized as the best job in the United States, you can reach a mind-blowing salary in just a short period of time. In just 5 years you can reach a salary of $170,000 a year which is impossible in any other engineering occupation.
So if you are asking about the money and job opportunities there is no doubt that data science is the best job to opt for. Data science is one of the hottest computer science fields that you can minor in or take a master’s in after getting your bachelor’s degree in computer science.
On the other hand, Industrial engineering is still a powerful field that offers a lot of flexibility and good potential to work in multiple Industries like mechanical, Aerospace, aeronautics, robotics, computer science anything that involves Improvement of production for Quality Service Industrial engineers are involved too.
But you don’t have to choose data science, just because it is the best job in the United States, or either choosing industrial engineering for its good salary and flexibility. You have to consider in which position you are meant to be working.
Are you made to be a data scientist or an industrial engineer? this would be explained later in our article.
The difference between industrial engineering in data science
To become a data scientist or industrial engineer there is a different process and academic Study you have to follow.
To work in industrial engineering, most students have to complete their Bachelors in industrial, producing, or Manufacturing engineering.
While in data science you have to complete a bachelor’s in computer science, IT, business, and math. But the most common Bachelor’s that students take to prepare for data science is computer science.
After completing your Bachelor’s in computer science, it is better to opt for a master’s in data science or even have a Ph.D. if you want to widen your knowledge in the field and become an expert.
In general, employers prefer to have a master’s degree rather than only a bachelor’s. It doesn’t mean that you should have a master’s in data science to find a job in the field. It is just the best option that is going to put you on the pedestal.
In industrial engineering, you are not required to study or take a master’s degree after finishing your Bachelor’s. You just have to complete some training and then apply immediately for jobs working with manufacturing companies, like automobiles, food, aerospace, mechanical, and so on.
For instance, if you work as an industrial engineer with an automotive company, your goal will be to enhance their productivity, such as raising the number of cars produced every year or reducing their cost of production.
While in data science you’ll be using your mathematics programming skills to create predictive algorithms helping businesses to increase their sales, and revenue and improve their services. your job will be focusing a lot on math, statistics, programming, and analysis.
The difference between the curriculum of data science and industrial engineering
If we compare the subjects students study in industrial engineering in terms of mathematics topics we’re not going to find a big difference, both majors are required to complete the following math courses:
The only subject that differentiates between data science and industrial engineering is discrete math; it is included in data science majors and not in industrial engineering.
But the principal thing that makes industrial engineering different from data science is physics and chemistry. Industrial engineering students have to complete some General Physics and chemistry in addition to other topics including:
Data scientists don’t study any of these courses, they focused only on mathematics common courses that we mentioned above, in addition to principal computer science topics related to programming including:
which should you Major on industrial engineering or data science
If you are passionate about mechanical design and love to work in a humid environment surrounded by noisy production machines, in addition to working with teams or maybe leading them, to finally improve the physical product, like a car component, smartphone, Aeronautics piece, or any service a company provides. then industrial engineering is a good choice for you.
Industrial engineering is a job for people who are extroverted, meaning sociable and like to be surrounded by people. In addition, you must have the resistance to deal with the pressure and even sometimes work longer hours than a normal schedule.
But if you are interested in mathematics and fascinated by numbers and statistics. In addition, if you have a passion to work in front of computers, data science would be a good choice. Especially if you are an introverted person, who loves to work alone and doesn’t get bored working long hours analyzing data and creating algorithms programs, and solutions.
Data science is more flexible than industrial engineering, you would have the opportunity to work remotely in addition to working as a freelancer with some companies on the side of your job.
You can reach incredible salaries up to $200,000 per year only from your job, especially if you have a Ph.D. or a master’s in the field, Which would allow you to retire early if you have a good Financial strategy.
Industrial engineering doesn’t allow you to work remotely, in the majority of cases you have to be present in manufacturing, and industrial plants managing a group of teams, and ensuring the production line of products.