(Lead Data Scientist, YouTuber)
You Tube Channel : Krish Naik
Krish Naik is a Lead Data Scientist at Panasonic IIC. He is focusing on business analysis, design, development, migration of major projects. He has been working in the software development and project implementation from last 7 years. He is one of the Best Data Scientist of India. He runs his own YouTube channel named “Krish Naik” where he uploads videos on Data Science, Machine Learning and Deep Learning. Recently he achieved “100k subscribers” on his channel. The main aim of his channel is to provide something back to the Data Science community in the form of knowledge. He uses Kaggle to improve his data science skill and solve many real world problems. Here he shares with us about his journey, career transition, data science, vision and his YouTube Channel.
Q1. Please share your tremendous journey with us?
My name is Krish C Naik, a B.E Computer Science graduate from PDA College of Engineering, Gulbarga, Karnataka. I started as a Dot net developer and later transitioned towards Data Science within 2.5 years. I also started my youtube channel named “Krish Naik” around 2017 December where I started uploading videos on Data Science, Machine Learning and Deep Learning.
The main aim of the channel was to provide something back to the Data Science community in the form of knowledge.
Q2. You are a software engineer. Why did you go for data Science?
When I was a software engineer working as a Dot Net developer, I did one of the project where I had implemented some personalization recommendation based on the user activity. At that point I had no clue that I was building an AI application.
After some time I got to know more about AI, Machine Learning, Deep Learning and Data Science and just in the first instance I could understand its importance as they was huge volume of data getting created due to smartphone, app, social networking sites and many more. The future looked bright so I thought of taking this path, and now I can definitely say I took the right decision.
Q3. You are very active on YouTube and recently your channel achieved 100k subscribers. How are you feeling for this milestone?
When I was preparing for Data Science I faced a lot of difficulties with respect to various concepts that are usually implemented in Machine Learning, Deep Learning. But after some years when I gained some more confidence after learning continuously, implementing complex projects I thought with the proper guidance anybody can learn this technology. So that is where I started the youtube channel and started uploading videos.
Reaching 100k subscribers within 1.5 years have really made me proud.
This was all possible because of the trust shown by the subscribers. So will be working continuously to upload more videos in this subject.
Q4. You participate in Kaggle too? How it is important to take part in Kaggle for any Data Scientist?
Yes I have participated in Kaggle as a team and also as a single member. If you are planning to make a career transition towards Data Science, participating in Kaggle is must. I have learnt various complex techniques and methods from Kaggle which I was not aware about it in my initial learning days. We also get pretty cool solution of various complex problems from kaggle kernels which are uploaded by other learners, Data Scientist and many experts.
Q5. You have been working from many years as a Data Scientist? Which classification problem have you faced maximum between Supervised and Unsupervised classification?
Q6. Which one is your favorite Machine Learning algorithm as a data scientist?
Currently Xgboost is my favorite Machine Learning algorithm.
Q7. You are an author too. What types of articles do you write?
I just have written one book “Hands On Python For Finance”.
I do not write articles, instead I make videos regarding Data Science, Machine Learning, Deep Learning in YouTube.
Q8. Please suggest some tips to newcomers to become a great data scientist?
Probably this answer is for everyone, whether you are a newcomer or experienced Data Science is a technique that can be applied to any domain. And trust we you cannot just be a good Data Scientist in a couple of months, you have learn continuously, improve your domain knowledge, keep working on complex projects. Always ask that how and why and
what and finally never give up, keep on learning!!