Do I Need a Degree for Data Scientist Positions?
I find this interesting article discussing about whether we need a degree to find a job as a machine learning scientist
and feel there’s more to talk about.
Link to the article: http://machinelearningmastery.com/what-if-i-dont-have-a-degree/
It’s interesting because this question can indeed be diced into multiple interesting and hot questions nowadays as follows:
What can a formal academic degree provide us?
What are the employees looking for?
How effective can we educate ourselves without formal academic degree?
I definitely agree with the authors that you can become a machine learning scientist if you achieved the following:
Complete a course or read a book and track your progress and findings in a public blog as you go.
Compete in machine learning competitions and work to earn a modest ranking such as within the top n% for a competition. Partner with
skilled practitioners to acquire skills faster and achieve better results.
Complete small projects in machine learning, advertise the results on a blog and social media and release the code on public revision
control systems. Build up a collection of completed projects you can refer to, draw from and discuss.
Drawbacks of formal academic degree:
A degree is both time and money consuming.
A degree is a symbol for others, not necessarily represent your level.
A degree is for the average student instead of personalized.
A degree teachers older information.
We are living in an era the education system is undergoing tremendous change. With the knowledge barrier for individuals getting smaller
and smaller, those who can learn continuously and effectively are going to win the long run, instead of those who has the best degree from
traditional academic institution. Despite of the fact that traditional education provides the environment for study and prepares the students
for the future, the world is changing at unprecedented pace, making the academic knowledge outdated very quickly. What is important instead
is life-long continuous learning. From my personal experience in physical science research, in old times people who excel are usually those
who have gone to the best schools and have the best teacher/professors. However, nowadays, more and more elites from physical science field
have very average background in their education history. They are usually smart, highly self-disciplined and thirsty to learn though. On the
other hand, even a PhD from MIT or Harvard can’t guarantee that you will be successful in the next five or ten years.
The only constraints on your career should be yourself. You would have a very good start if you know the answers to the following questions:
- What do you want to learn?
- Where to find the resources to learn what you are interested in?
- Are you self-disciplined enough to reach your goal step-by-step?
Once you have positive answers to the above questions, you can learn more quickly and effectively than in formal schools. However, these are
indeed very challenging questions to normal people as they require a lot of curiosity, enthusiasm and persistence.
Do I Need a Degree for Data Scientist Positions?
I find this interesting article discussing about whether we need a degree to find a job as a machine learning scientist and feel there’s more to talk about.
Link to the article: http://machinelearningmastery.com/what-if-i-dont-have-a-degree/
It’s interesting because this question can indeed be diced into multiple interesting and hot questions nowadays as follows:
What can a formal academic degree provide us? What are the employees looking for? How effective can we educate ourselves without formal academic degree? I definitely agree with the authors that you can become a machine learning scientist if you achieved the following:
Complete a course or read a book and track your progress and findings in a public blog as you go. Compete in machine learning competitions and work to earn a modest ranking such as within the top n% for a competition. Partner with skilled practitioners to acquire skills faster and achieve better results. Complete small projects in machine learning, advertise the results on a blog and social media and release the code on public revision control systems. Build up a collection of completed projects you can refer to, draw from and discuss. Drawbacks of formal academic degree:
A degree is both time and money consuming. A degree is a symbol for others, not necessarily represent your level. A degree is for the average student instead of personalized. A degree teachers older information. We are living in an era the education system is undergoing tremendous change. With the knowledge barrier for individuals getting smaller and smaller, those who can learn continuously and effectively are going to win the long run, instead of those who has the best degree from traditional academic institution. Despite of the fact that traditional education provides the environment for study and prepares the students for the future, the world is changing at unprecedented pace, making the academic knowledge outdated very quickly. What is important instead is life-long continuous learning. From my personal experience in physical science research, in old times people who excel are usually those who have gone to the best schools and have the best teacher/professors. However, nowadays, more and more elites from physical science field have very average background in their education history. They are usually smart, highly self-disciplined and thirsty to learn though. On the other hand, even a PhD from MIT or Harvard can’t guarantee that you will be successful in the next five or ten years.
The only constraints on your career should be yourself. You would have a very good start if you know the answers to the following questions:
- What do you want to learn?
- Where to find the resources to learn what you are interested in?
- Are you self-disciplined enough to reach your goal step-by-step?
Once you have positive answers to the above questions, you can learn more quickly and effectively than in formal schools. However, these are indeed very challenging questions to normal people as they require a lot of curiosity, enthusiasm and persistence.
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