I managed to set my foot in the tech world on my own.
All interviewers asked me why I transitioned from chemical engineering to programming, and I had to prove them that I had the required skills, whereas computer science students or software engineering students simply had to show a CV.
In December 2017, I completed my bachelor’s and I had an assured job to start in July 2018.
Between the end of university and my new jobFrom January 2018 to July 2018, I was free!No school, no work, just 6 months of relaxing and waiting to start my new job as a web developer.
However, I simply can’t do nothing.
That’s when I learned about the craze for data scientists.
Everywhere, I was reading it was the sexiest job, and all that jazz.
I figured I had to check it out.
I started learning data science on Dataquest.
Their approach fitted more my learning style and since I had a lot of free time, I knew I could complete the curriculum fast.
In fact, I crammed the entire data scientist and data engineer paths in 2 months.
The experience was amazing in my opinion.
I learned a lot, and you get to complete many projects, which will build a very solid data science portfolio.
However, the main drawback for me was that I did not understand what I was applying.
It worked, but I lacked the theory behind the algorithms.
Therefore, I took Andrew Ng’s Machine Learning course.
Again, I had heard about this course and I think it is probably one of the highest rated course on Coursera.
I absolutely loved it and learned much more about the math behind machine learning.
However, I did not like that the course was taught in Matlab/Octave, so it was hard to translate what I learned in Python.
I learned a great deal about the fundamentals of machine learning, but I still felt I was lacking some knowledge.
Learning data science while workingIn July 2018, I started my new job.
I was very excited, but I was juggling between learning more about web development and data science.
So I started doing data science on my spare time (after work).
I decided to read the book An Introduction to Statistical Learning.
A friend recommended this book, saying it was the best introduction to data science.
I gave it a shot, and I was pleasantly surprised!.Reading this book was actually enjoyable.
I was very serious in my study.
I was taking notes and I forced myself to apply every algorithm in Python by building very small projects.
This really helped master most of the traditional machine learning algorithms and most importantly, I gained confidence in my skills.
Then, something weird happenedI was browsing Facebook at work (yes, at work, I admit it), and I saw an ad for a data scientist position at one of the largest banks in Canada.
The ad simply said:We’re looking for data scientists!.Take the quiz!I thought I had nothing to lose.
I took the quiz and got 11/13.
Even I was impressed with my score!A week later, I get a phone interview.
After one hour (and after telling the interviewer how I transitioned from chemical engineering to programming), the recruiter decided to make go through the final step.
The final step consisted in completing a data science project and presenting it to senior data scientists.
I decided to rework a project I completed while studying with Dataquest, and I presented it.
A week later, I got a job offer and I accepted it.
In January 2019, I started my new job as a data scientist.
Since January 2019Since then, the learning and work opportunities have just multiplied.
I get to collaborate with incredibly smart and motivated people, and the company encourages us to learn, explore, experiment and innovate.
I feel very lucky to be in that type of organization, and I am very proud that I managed to learn data science and acquire the relevant skills by myself to land a job.
Now, deep learning is a natural next step, which I am already working on right now.
Can you do the same?With the right attitude and mindset, yes.
I firmly believe that anyone can accomplish the same.
I am far from being an exception.
I did it this way because it fitted my personality and my learning style.
I could:learn at my own pacelearn enough theory for me to understandcomplete many projects to get hands-on experienceHowever, there were many drawbacks as well:I didn’t know if I was learning the right thingI didn’t know the structure of a data science curriculumI had to gain confidence in my skills and complete many projects to prove that I could be a data scientistWhat I did is hard.
You have to be passionate and disciplined about a subject to accomplish what I did.
Yet, I found this way was definitely the most rewarding.
So, do I need a master’s to be a data scientist?No.
I think that companies are not looking at diplomas anymore, and are looking for skilled people instead.
In the end, what really matters, is your set of skills.
Whether you decide to pursue a master’s or not, realize that it is only a different way of reaching the same result.
In any case, make sure you gain the following skills:Proficiency in Python and SQLLearn some SparkLearn software development best practicesLearn version controlSome containerization is a good bonusBe a good presenterIf you are an aspiring data scientist, I wish you best of luck!.I absolutely love the field, and I think that the classes I took online really helped love and understand this field.
In the end, the key is being passionate about a subject and to be willing to share your passion with as many people as possible.
Then, the opportunities will come.
I wish you a lot of success!.. More details