Kanth Mentorship Show
"Kanth Mentorship Podcast" is a podcast series hosted by Rajeev Kanth that focuses on career transition and development, particularly in Data Analytics, Data Science, Machine Learning, Data Engineering, Cloud Computing, AWS, Azure, DataBricks, SQL, Power BI, Tableau, Python Generative AI, Deep Learning, Computer Vision, LLMs, and Artificial Intelligence. The podcast covers various topics, including interview preparation, industry insights, and career path guidance, aimed at various individuals, including students, working professionals, and those re-entering the workforce.
Kanth provides personalized guidance for career transitions, discussing suitable pathways based on individual backgrounds and challenges. The focus is on the strategic enhancement of resumes and interview preparation, especially for jobs in India, the USA, the UK, Canada, and the UAE.
For more details and to listen to the podcast episodes, you can visit their pages on Apple Podcasts and Spotify.
Connect with Host: www.instagram.com/meet_kanth/
Kanth Mentorship Show
How To Explain Projects in Data Science Interview
Are you looking to get a break in data science but struggling to clear interviews? There’s a ton of variance in interview questions. You will get the IBM interview process which from what we have heard is really high level questions like, describe how a decision tree works? or Describe how this works or that works?
You should think about the interview process as being similar to an important test at school: if you walk into interview without preparing, you are probably going to be in trouble. When answering any interview question you should have three goals in mind Be clear, Be concise and Answer appropriately. Mastering the first, be clear, is mostly about preparation. You can only explain something clearly when you know what you’re talking about. Being concise is about carefully listening to and answering the question, but not more and Answering appropriately just requires you keep in mind who you’re talking to. Are you speaking to a data scientist with 15 years of experience? and use as much jargon as you want.
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