How To Become A Data Scientist In 1 Year (Learn From A Real World Example) | Summary and Q&A

TL;DR

A mechanical engineering graduate shares his journey of learning machine learning and data science, securing multiple medals in Kaggle competitions, and landing a job in the field within a year.

Key Insights

• 🏑 Passion and drive are crucial for success in the data science field.
• 💄 Kaggle provides an excellent platform for learning, showcasing skills, and making connections with industry professionals.
• 💪 Building a strong portfolio with unique and innovative projects is valuable for job opportunities.
• ⚖️ Balancing theoretical knowledge with practical application is essential for growth in data science.
• ❓ Overcoming educational and background limitations is possible with dedication and continuous learning.
• 🤗 Networking and seeking mentorship can open doors to job opportunities.
• 🤔 Problem-solving skills and the ability to think outside the box are highly valuable in the industry.

Transcript

hello everyone and welcome to my new video and as you know i usually don't make videos like this i make videos about coding but this episode is something special and um i have invited tunnel who have known for a while now and he's also a gaggler and he has done some good work in kaggle and in his professional life and he managed to learn machine le... Read More

Questions & Answers

Q: What initially sparked your interest in machine learning and data science?

Tanul had a desire to do hands-on work and solve real-world problems, which he felt was lacking in his mechanical engineering studies. Machine learning presented an opportunity to be a unique engineer and tackle new and diverse challenges.

Q: How did you discover Kaggle and why did you choose it as a learning platform?

Tanul found Kaggle to be the ideal platform for building a portfolio and learning from the best in the data science community. Kaggle's competitions offered practical experience and the chance to interact with top data scientists.

Q: How challenging was it to find a job in data science coming from a Tier 3 college and a mechanical engineering background?

Finding a job was difficult due to the perceived limitations of his educational background and lack of experience in the field. However, Tanul's achievements in Kaggle, gaining recognition from mentors and industry professionals, helped him secure job offers.

Q: How did you manage your time between studying, competing on Kaggle, and preparing for job interviews?

Tanul had the advantage of the lockdown, which allowed him to focus solely on Kaggle. He devoted around eight to eight and a half hours per day to Kaggle during this time. He emphasized the importance of continuous learning and balancing theory with practical application.

Summary & Key Takeaways

• Tanul Singh, a graduate of a lesser-known college, successfully transitioned from mechanical engineering to data science in less than one year.

• He excelled in Kaggle competitions, securing five medals out of six and earning the rank of 17th in Kaggle Notebooks Master.

• Tanul's passion for problem-solving and innovation led him to machine learning, and he found Kaggle to be the perfect platform for learning and showcasing his skills.