Are you eligible for data science?

Here is all you need to hit on data science, machine learning, etc in a flawless manner.

Starting might sense difficult, but if directed properly, it may lead to wonders. The same happens with people who start learning data science and machine learning. People just directly dive into the actual machine learning code and try to grasp its working, whether they understand it or not. Rather than reading the documentation or the actual working of machine learning algorithms people believe that they would be able to crack the exact idea behind the code… Really? Am I kidding or the beginners?

I too used to be a beginner, at my initial level of learning ML, I used to just copy the code from notebooks, repositories, or from any blog created by other people, and with just a little change to the data section, I use to solve any ML problem, which made me pretend “I know Data science”, In front of a mirror but not in front of people who are the owner of the data science knowledge, who know the exact problem-solving. This went up for a long time, but suddenly one day I was been interviewed by a gentleman (a data scientist) that changed my perspective on learning data science, it changed my overall vision of what exactly learning data science denotes. As I used to apply for a data science role in companies that got me an interview.

When I entered the interviewer's room, I got to see a simple person sitting on a chair, a medium-sized table in front of him, and a laptop on top of the table. I was waiting, and the interviewer was already questioning the interviewee. I got to hear just the last three questions that the interviewee was going to answer, but he attempted two, and the remaining one he was not able to answer the question in a proper way. However, he answered all the questions except one, and his interview went exceptional according to me. Just because I wasn’t aware of any questions or terminologies the interviewer was questioning and further elaborated by the interviewee. But at that moment of time, my situation was remarkable because I was in a very panicked situation which I wasn’t before I left home.

thoughts striking at my mind at that specific moment

What would he ask me, I can’t even predict because I am not even familiar with the term data science or ML, I just use to run the code and felt that I know ML.

He got hired and was offered a good amount, with a junior data scientist post which he deserved as per my thought process at that time and even now if think about him, I feel he still deserved it.

After he left the chair my turn came up, the interviewer greeted me and behaved like he is one of my best friends, which made my fear a little low. He acted or might be in reality he was a down-to-earth guy. who really made me feel good and this doesn't at all vanish my panicking. He asked me for my intro, which is a state of art question for every interviewer. I was unable to utter a word as it was my first time being interviewed. Still, I tried hard to answer him, but it open up all as he started with the first horrifying question for me at that time. “What is sampling?” I was shut up and both of us were able to hear the pin-drop silence which made me panic more. My mind was blank.

I said

Ah.. samp..sampling ?? ahh I..II… I don’t remember it

Alright no issue, let us move to the next one “ What is pdf?”, said the interviewer.

My thoughts at that moment

Wow this is the easy one

pdf stands for portable document format, I answered. and I was quite happy about my answer because according to me I answered it write and also was confused by his question, that I am attending a data science interview, and why must this guy be asking me about pdf??

The interviewer was quite surprised by my answer he clapped and asked me a bunch more. I was not even able to answer a single, the interview was the worst ever interview of my life, but was a game-changing one. The interview woke me up from the misunderstanding that I knew data science and the same thing influenced me further to learn it in an organic and genuine way, And finally my data science journey got started. And today I have left back the destination. Even the destination might be thinking how this guy overtook me so effortlessly. The only reason behind this was ________.

Nothing is impossible and nothing is straightforward, was what I realized that day, learning anything in the world requires technique and roadmap, and without these duals, we are incomplete.

What I learned that day, changed my thinking mechanism forever, which even today motivates me. Learning from your own mistakes will make you huge. Yes. Believe me. And that’s the reality of life.

Shout out.

1. Knowing something doesn’t make, but exactness makes.
2. We usually copy codes from other sources, but during the time when we need to prove ourselves, we are not able to make it possible, because we realize that we haven’t coded anything manually yet.
3. We don’t take dependencies, documentation, requirements, and usage seriously, due to our overconfident nature. “what the hell has this guy written, I don’t need this info”
4. What we admire, we try to claim it for ourselves, but we fail because we lack in observing the hard work behind it.
5. Don’t ever make a fake identity in society, pretending “I know everything”. Ask yourself are you ready??
6. Try to learn and everything in such a way that if anything is asked of you you will be confident enough that yes, I can explain this very well. That day you will deserve a celebration.
7. Be honest with yourself. The world is no more real.
8. Don’t feel ashamed of asking >> Questions<<. Feel shame on knowing nothing that will play a crucial role in your life.
9. Make reading a habit. Just consider reading as a bad habit which will make you adopt it as soon as possible.
10. finally, be happy >> one life>> think of expanding it.

Coming to point

1. Perfection in python is a must. (Oop’s concepts, Pandas, NumPy, math, etc.)
2. Statistics, Probability, linear algebra with strong basic foundations.
3. Matrix, Vectors, arrays.
4. ML algorithms and their working.
5. Deep neural networks and their working.
6. RNN, CNN, DNN
7. Basic terminologies in ML and DL. I have provided the complete learning map below.
8. Natural language processing > word embedding, tokenizations, and further techniques.
9. Computer vision > Basic operations on an image like resizing, applying filters.

Advanced

1. Calculus
2. Try reading and understanding research paper
3. Generative model, language model, Encoder-Decoder, autoencoders, image models.
4. Attention mechanism.
5. Transformers, Bert, GPT, etc.
6. Deployment.

A beginners mind

A beginner thinks that ML, Data science is just writing the code and executing it.. that’s it (There involve a lot more tasks, where the data collection, data transformation, and analyzing takes almost 60% of the time. The post-process involves the deployment part. By just seeing the Data Science roadmap we feel the goal is easily reachable but that’s not at all the case, there is a lot to learn and we need to keep ourselves updated.

We dream and even try to achieve an expert badge in all the technologies so we try to work in everything but instead of being experts in all technologies we do not even achieve mastery in a single.

Conclusion

Don’t take anything seriously, This everything was just a lie, right? do comment.

I know the title made you expect something else from the article but got unexpected writings. Hope you fit the eligibility criteria.

Thanks.