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Should I go to college to become an AI engineer?

AI engineer

In anticipation of the development and growth of AI and IT, there are probably many people who have wanted to become AI engineers in the future since they were students.

I think it’s only natural for students who want to be AI engineers to have doubts about which university they should go to to become an AI engineer.

However, among such people, there are many people who do not have much knowledge about AI engineers, such as what kind of work AI engineers do and what skills are required. Huh?

If you want to become an AI engineer, you need to know the work content and necessary skills.

In this article, we introduce the necessary knowledge and the advantages and disadvantages of each path to become an engineer.

Please refer to it when choosing your career path.

Table of Contents

  • What is an AI engineer?
    • Differences with Data Scientists
    • AI engineer job description
  • Should I go to college to become an AI engineer?
    • At university, you can learn cutting-edge and more advanced technology!
    • You can comprehensively learn IT-related knowledge at university!
    • High starting salary for college graduates
  • Disadvantages of going to university for those who want to be AI engineers
    • high tuition
    • Delayed work experience
  • Who would you recommend going to college?
  • Points to consider when choosing a university to become an AI engineer
    • 1. relationship with the curriculum
    • 2. Research results/equipment
    • 3. Deviation value
  • [2022 latest version] List of deviation rankings of universities with information-related departments
  • Science and Technology Research Funds (KAKENHI) Acquisition Ranking by Universities and Research Institutions in AI
  • How to become an AI engineer without going to college
    • attend a vocational school
    • go to school
  • 5 steps when you are wondering whether to go to university to become an AI engineer
    • Organize why you want to be an AI engineer
    • Organize what kind of AI engineer you want to be
    • Think about what you need to do to achieve your ideal future
    • List the environments where you can learn what you need from 3
    • Set criteria and choose the best method from among options
  • summary

What is an AI engineer?

AI engineers refer to engineers involved in AI in general, and mainly develop and implement AI so that machine learning can be performed efficiently.

However, there are various definitions, and depending on the company, there may be differences such as AI engineers for research jobs and machine learning engineers for development jobs.

Please be careful when applying.

Differences with Data Scientists

A data scientist analyzes data for a given problem and designs a model.
Then AI engineers implement the model that is being designed.

However, data scientists also touch the field of machine learning, so be careful when applying as there are cases where they are recruiting as machine learning engineers.

AI engineer job description

The job of an AI engineer is to program, develop and implement AI.

After the AI ​​development is over, give a large amount of data and let the AI ​​learn.
Here, we will improve the accuracy of AI.

In addition to raising the accuracy, verification is also done at the same time, so we also make corrections.

Finally, after the analysis work, AI development will be used, but it is
basically the domain of data scientists, so you can think of it as not being done.

Should I go to college to become an AI engineer?

Regarding the question of whether it is necessary to go to university to become an AI engineer, the
conclusion is that it is not essential.

However, there are many benefits to going to university and aiming to become an engineer.
Here are three benefits.

  • You can learn cutting-edge and more advanced technology at university
  • You can comprehensively learn IT-related knowledge at university
  • High starting salary for college graduates

At university, you can learn cutting-edge and more advanced technology!

First of all, you can learn more cutting-edge and advanced technology at university.

Majoring in AI at university has the advantage that it is easier to learn more advanced and specialized knowledge compared to other vocational schools and programming schools.

This is because at university you can delve deeper into the fields of AI and computer science as academics and theories.

In addition, while in school, students can quickly learn the latest technology under professors who are conducting cutting-edge research, so they can respond to technological innovation.

You can comprehensively learn IT-related knowledge at university!

The second merit is that you can comprehensively learn IT-related knowledge.

Even if you major in AI, you can comprehensively study not only the AI ​​field but also IT-related academics at university.

This is because the university curriculum is systematically designed to develop IT specialists.

Even if you are a beginner, if you study intensively for four years, you can master the study of the IT field and solidify the foundation of knowledge that will allow you to be active on the front lines of an IT company.

High starting salary for college graduates

The third is that starting salaries are high for university graduates.

The starting salary for new university graduates as AI engineers is set at around 220,000 yen per month, which is the same as other occupations at many companies.

However, in recent years, the competition to acquire young and talented AI engineers has intensified, so it seems that more and more companies are setting up a “special quota” in the AI ​​field even for new graduates, and increasing the starting salary significantly. .

Also, since major companies tend to hire engineers who have graduated from college, it is also true that there is a large difference in starting salaries compared to engineers who have graduated from technical colleges or high schools.

Disadvantages of going to university for those who want to be AI engineers

AI engineers require advanced and specialized knowledge and skills.

In that case, at first glance, going to university to become an engineer may seem like there are no disadvantages, but it does exist.

This time, I will explain two disadvantages of going to a university: “High tuition” and “Late work experience”.

high tuition

One of the disadvantages of going to college is the cost of tuition.

The tuition fee for four years of study at a university’s information-related department is quite high, at 5 to 6 million yen at private universities and 2.5 to 3 million yen at national universities.

This is because, in addition to the facilities and equipment unique to science departments, the admission fee is also high.

On the other hand, the tuition fees for technical colleges are about 2.2 million to 2.5 million yen in total for the two years until graduation.

However, depending on the school, there are also 3-year and 4-year courses, and the total tuition fee for such a long-term course is about 4 million to 5 million yen, which is almost the same as the tuition fee at a private university.

Delayed work experience

Another disadvantage is that university graduate engineers take time to gain practical experience.

This is due to the fact that the curriculum is organized over four years and that there are more lectures than at vocational schools.

Therefore, if you are majoring in AI engineering at university, we recommend that you participate in a long-term internship at an IT company in parallel with your university studies to gain practical experience.

Who would you recommend going to college?

First, in the future, those who want to conduct basic and applied research as “researchers” at research institutes and manufacturers.

This is because, at university, you have opportunities to enter laboratories, engage in full-scale research activities, and write technical papers.

If you are ambitious and produce results while in school, you will be hired as a university employee, and depending on the situation, you may be able to play an active role as a university professor or laboratory staff.

Second, those who want to work for a large company in the future.

Many well-known large companies tend to hire new graduates for AI engineering positions, implicitly seeking college graduate-level abilities, even if they do not clearly limit their educational background.

Also, some major companies apply for students who have graduated from elite universities overseas, which are the homes of AI and IT technology education, so it is better to have a high educational background.

Points to consider when choosing a university to become an AI engineer

I think that some people who are thinking about going on to university do not know what to consider when choosing a university.

Therefore, here are four points that you should keep in mind when choosing a university.

When choosing a university, please refer to

  1. Relationship with the curriculum
  2. Research results/equipment
  3. Deviation value

1. relationship with the curriculum

Compatibility with the curriculum is an important point when considering your own future.

If you don’t find your field of interest or what you want to major in the curriculum, but you go to that school, you’re putting the cart before the horse.

Before deciding where to go to school, do your homework and consider all options.

2. Research results/equipment

Research achievements and facilities are important points in checking whether the university is cooperative with your research and whether the research environment is in place when you conduct research and write a thesis on your own.

National universities and famous private universities that receive a lot of subsidies from the government have substantial research facilities.

As mentioned above, universities conduct more advanced research. I highly recommend it as it is worth studying and will be very useful for your future career.

3. Deviation value

If you want to get the title of a university graduate, it is desirable to have an educational background that is more advantageous for job hunting.

As mentioned above, many highly educated and talented people apply for high-income jobs at large companies.

Therefore, if you aim to work in such an environment in the future, it is recommended that you go to a school with a high deviation score as much as possible, even among universities where you can learn what you want to do.

[2022 latest version] List of deviation rankings of universities with information-related departments

Many universities have computer science departments.

Among them, the universities with high deviation values ​​are summarized in a table in a ranking format.

         University name/Faculty/Department Deviation value
Department of Computer Science, Faculty of Engineering, Kyoto University 78
Keio University, Faculty of Environment and Information Studies, Environmental Information 75
Information Engineering, Tokyo Institute of Technology 75
The University of Tokyo, Science 2 75
The University of Tokyo, Science 1 75
Meiji University, Faculty of Information and Communication, Information and Communication 73
Aoyama Gakuin University, Faculty of Social Information, Social Information 72
Keio University, Faculty of Science and Technology, Academic 5 71
Waseda University 71
Waseda University, Faculty of Science and Engineering 71
Osaka University, Faculty of Engineering Science, Department of Information Science 71
Yokohama National University, Faculty of Science and Engineering, Department of Mathematics and Electronic Information Engineering 71
Nagoya University, Faculty of Informatics, Department of Human and Social Informatics 70
Aoyama Gakuin University, Faculty of Social Information, Social Information 70
Doshisha University, Faculty of Culture and Information Studies, Cultural Information 70

Science and Technology Research Funds (KAKENHI) Acquisition Ranking by Universities and Research Institutions in AI

People who want to enter a university for the purpose of doing research related to AI must be wondering how much money they spend on their research.

Therefore, we have summarized the ranking of research funding in the table.

      University name  number        Amount of distribution
University of Tokyo 144 2072,388,000
Kyoto University 76 961,169,000
Osaka University  68 860,057,000
Tokyo Institute of Technology  58  502,740,000
Kyushu University  49 428,877,000
Nagoya University  15 398,830,000
Hokkaido University  36 353,330,000
Tohoku University  38 290,540,000
University of Tsukuba  38 249,710,000
Nara Institute of Science and Technology  19 230,740,000
Waseda University   38 229,668,000
Ritsumeikan University   28 204,229,000
Kobe University   19 185,120,000
 University of Electro-Communications  twenty five 184,716,000
Tokyo University of Agriculture and Technology  9 157,040,000

 

How to become an AI engineer without going to college

Earlier, I mentioned that going to university is not essential to becoming an AI engineer.

So here, I will introduce how to become an AI engineer without going to college.

This time, I would like to introduce two options: ” going to a vocational school ” and ” going to a school .”

Attend a vocational school

The vocational school has a curriculum that allows you to learn AI development practically, so there is an environment where you can acquire practical skills.

In addition, many vocational schools take a two-year system, so they do not require as much time and tuition as universities.
In addition, there are a number of training courses for qualifications.

On the other hand, the disadvantage of a vocational school is that it is difficult to accumulate more detailed research and paper achievements like a university.

In job interviews at major companies, many students who have done specialized research at difficult universities also apply, so specialized students may be at a disadvantage.

Go to school

AI-related schools, such as programming schools, are tools for easily learning AI development.

Basically, there is no entrance exam, and anyone can learn as long as they pay the tuition fee. is characterized by

You can learn by narrowing down the knowledge and fields you need, and in recent years there has been an increase in the number of courses that you can take online without going to school, so it will be very convenient.

On the other hand, the disadvantage of school is that it does not become an educational background.

Since a school is not a formal educational institution, even if you graduate, it will not be recognized as an academic background, and it will be an environment where you can acquire knowledge and skills.

Therefore, it is recommended for people who do not need an academic background, such as working adults who have already graduated from university or junior college, and university students at W School.

5 steps when you are wondering whether to go to university to become an AI engineer

Earlier, I mentioned that there are various paths to becoming an engineer.

I have also said that regardless of your career path, what you must wear will not change. (Whether you go to college or a vocational school, what you need to become a data scientist will not change.)

Therefore, here, I will introduce how to deal with when you are wondering what kind of career path you should take in order to become an AI engineer.

Here’s how to find your career path in 5 steps.

  1. Organize why you want to be an AI engineer
  2. Organize what kind of AI engineer you want to be
  3. What is required to achieve 2
  4. List the environments where you can learn what you need from 3
  5. Set criteria and choose the best method from among options

Organize why you want to be an AI engineer

First of all, I will organize why I want to be an AI engineer.

I think there is a reason why I want to become an AI engineer. Let’s start by sorting out why.

If you can’t think of a clear reason, or if you can think of a reason why an AI engineer isn’t the best choice for you, you might want to reconsider your choice to become an AI engineer in the first place.

Going to college means spending a lot of money and a lot of time.

Aiming to become an AI engineer with an ambiguous motive is extremely risky.

This step is important, so do it carefully.

Organize what kind of AI engineer you want to be

The second step is to organize what kind of AI engineer you want to be.

There are many types of AI engineer jobs. In that case, it is also important to think specifically about what you want to do and what you want to make.

As a result, the knowledge and skills you need to acquire will change, and your career path will also change.

Think about the goal you want to achieve and work backwards.

Then you should be able to find the best option.

Therefore, I recommend that you first decide what you want to be as an AI engineer and set that goal.

Think about what you need to do to achieve your ideal future

The third is to think about what is necessary to realize the ideal future.

Now that you have sorted out what you want to do in the future and what kind of AI engineer you want to be in Step 2, let’s think about what knowledge and skills are necessary to realize that vision.

It may be difficult to list detailed knowledge when you haven’t opened the door to becoming an AI engineer yet, but please do your own research using the Internet, etc., and think of your own approach.

If necessary, it may be a good idea to consult with seniors in the field.

List the environments where you can learn what you need from 3

Next, list the necessary things, knowledge, and the environment in which you can learn and acquire the skills mentioned in 3.

It is better to list more candidates and options in more detail, such as vocational schools and programming schools, not just universities.

For example, when mentioning a university, it would be best if you could think about “which university, which department, which department”.

Set criteria and choose the best method from among options

Finally, set criteria and choose the best option from among the options.

After exhaustively enumerating possible paths and options in Step 4, choose the most appropriate method from them.
At that time, if you create your own non-negotiable standards and compare each means, it will be easier to choose a course.

For example, it is recommended to set certain constraints in terms of time and cost, and to compare the feasibility of two options.

If you follow these steps and find that “going to college” is the best choice for you, we recommend going to college.

Summary

What did you think?

I hope you have understood that there are many ways to become an AI engineer in the world, and that each path has advantages and disadvantages.

The important thing is to thoroughly analyze your ideals and current situation, investigate and enumerate all possible means and paths, compare them, and find the optimal solution based on your own criteria.

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