Career-Shift, A Personal Perspective
What is the biggest obstacle in shifting your career to Data Science or Machine Learning:
- No Masters/PhD?
- Lack of programming skills?
- You don’t know how to write advanced SQL queries?
- Less number of people in your network, offline/online?
- Not knowing Statistics?
- Don’t know Python well?
- You are not very good at Pandas and Scikit-learn?
- Can’t get yourself to see anything till the end?
- Not intelligent/talented enough to understand technology?
- Not sharp enough to think of smart ways of solving both business and tech problems?
- You think data science will soon die and AI is the future?
- You have not memorized 1000 interview questions?
No. It’s none of the above.
The more I am learning how to shift my career to data science, the more I am observing that the most fundamental aspect is what Jim Rohn called your personal philosophy of life:
The more I am learning how to make myself valuable to the data science marketplace, the more I see the deciding factor is: “the way I look at, think about and approach my life, the people, the situations and this world”. Not only that, I have also found our professional, personal, family, financial, social and every other aspect of life, is built on our personal philosophy. If we want to solve some really hard problems, like shifting/changing your career, your personal philosophy is the first place you must look at before trying to learn Python or Statistics. personal philosophy on life is the first place you must contemplate before you ask “Why I continue to fail, why I am unable to master programming required, why I am unable to get a grip on the Mathematics behind”. This will include examining your beliefs, like why you do what you do, what is the purpose behind career shift. e.g. instead of asking questions that pull you down, the ones I just wrote above, better questions to ask are “How can I master programming”, “How can I get the mindset of asking smart questions. From where I can learn it. If I want to succeed then what should be the first step. If I don’t know how then where I can find it”. A personal philosophy of asking questions that empowers you instead of making you ineffective at solving life’s problems.
Money is a very important factor and the problem is almost everyone chooses it as the primary and the first factor in a career change. I think money needs to be in second place after the why. Why do you want to change your career? Do you think 10 years later you will be enjoying data science? What if five years into a data science career you want to choose a completely different career because data science, machine learning and AI didn’t satisfy and fulfill you as a person? Do you like data science enough that you are dying to do a PhD, not because it will give you edge over other candidates but because of the person it will make you? Do you understand and more important can feel what Luke Posey is saying:
I am just asking some unusual and hard questions because many years later you may get up and find yourself unsatisfied and unhappy. I don’t want that to happen. I have done many mistakes in my life, yeah, lot of failures that most people generally don’t go through, that has made me understand that life is more than achieving something, life is more than your monthly paycheck, life is more than the tags and certificates we have, life is more than what we fear, life is more than being smart and talented. Life is more about expanding yourself, finding and exploring yourself, listening to other’s viewpoints and finding the so-called diamond in your failures, finding and appreciating the good in people.
https://en.wikipedia.org/wiki/School_of_Hard_Knocks
It’s a miracle that today you are alive and can write a program to clean data. Many who were there yesterday, are not here today to visualize data, to solve problems, to live. Then there are others who have given up on their dream of becoming a data scientist or machine learning engineer. I think everyone has a mission in their lives, that we are supposed to follow on this planet. Money is a very very important thing in life. You can’t survive without it in this world. Money is one dimension that speaks for success in life. When you know money is a by-product of your thinking and your way of living, you will free yourself to focus more on what you want and need to do.
I am saying this because a career in data science or machine learning is going to be highly competitive. With the internet, the world is a well-connected place now and we can communicate and reach and touch lives like never before in human history. Last 50–70 years of advances in technology have happened at such a speed that the last 3000 years feel like they had almost no development at all. We were on light-speed innovation in the last 50–70 years. With this comes a big change, when you apply for a data scientist or machine learning engineer position, you are competing globally. 20 years ago all job competition was local, not anymore. The next 20 years are gonna be skyrocket changes with ML and AI, the changes we have never seen before. It will feel like science-fiction coming to life. Such fierce competition and speed of change require a new kind of mindset else we will all be victims of stress, worry, and failures. It doesn’t have to be this way.
No matter what kind of advancement we are going to have and at no matter at what speed we are going to have those changes and no matter if the old structure is breaking down and no one knows yet what the new structure is, the principles of success and happiness will be still same old, the principles that Og Mandino told us in his scrolls in 1968 or Swami Vivekananda taught us in 1895 or Jim Rohn taught us four decades ago. You as a person are the most important thing in your life, you as a person are much stronger than your problems and failures. Maybe these career problems were given to you so that when you solve them, you will know it wasn’t this tag of a data scientist or machine learning engineer that made you happy, it is what you become out of it while solving your problem, that was the message all along.
