Data science interview tips (product and technical interviews)

Tina Huang · Intermediate ·📄 Research Papers Explained ·5y ago

Key Takeaways

Data science interview tips for product and technical interviews, including strategies for acing interviews and preparing for common data science questions

Full Transcript

during technical interviews always communicate that is number one like literally just talk through everything that you're doing even if you're saying i'm just gonna think about this now this is i'm just thinking out loud like say stuff like that and make sure that you repeat the question the way that you understand it to the interviewer multiple times to make sure that you're on the right track because with these questions especially if you're nervous it's very easy to stray off track like we're not exactly answering a question that's being presented and you might be answering something that's like slightly off but if you're not communicating with your interviewer then the interviewer doesn't know that you're doing something wrong until much later and that by that time a lot of time probably has passed so that's not good for you another thing is don't try to find the most optimal solution for something um always try just to put a solution that works first and then be like hey i think i can optimize this even further and then talk through like the optimization process and start doing these optimizations yeah like if you think too much and be like what is the most optimal solution i can put on here then most likely you're just gonna like get nervous really confuse yourself so yeah just get something down on paper first and for non-technical interviews maybe like product or business sense interviews check out consulting interviews that's an excellent structure to follow the way the step-by-step process is very very good and also be very logical about what you're doing so if someone asks you a question like hey like there's something that went wrong in this feature and we're trying to figure out how to solve it for example that's a pretty common question for a product interview don't like think very thoroughly about it like don't just start rambling like be like okay to diagnose this problem we first will consider like this step and then from here if this step shows a certain result then it means you know this thing and if it doesn't then we will do the next step and then you just kind of go on from there like be very very structured in your thinking and not just like ramble on for technical interviews you should also be very careful about that like don't just like start typing stuff and be like oh my god like i feel really awkward just you know maybe maybe the interviewer things i don't understand and just like start typing things never never never do that think through exactly what it is that you're going to do write down the steps in english and then start writing stuff down trust me like it will save you so much more effort and so much more time to figure it out the first time like understand the logic as opposed to start typing stuff or stop writing stuff and then trying to figure that out then horse correcting is only necessary you know afterwards like you don't want to do that as your number one choice like you want to try to do things like properly the first time don't be afraid to take time to think about things it's totally fine to be like i need to i'm just thinking through this right now or like give me a moment um anything like that

Original Description

This is a small clip from the study with me session where someone asked me for my best data science interview tips. Thought it might be helpful for those of you prepping for product and/or technical interviews :) ______________________________________________________________________ Other videos you might be interested in How to learn data science in 2021: https://www.youtube.com/watch?v=Axu4tJl8gbM The resume that got me into FAANG as a data scientist: https://www.youtube.com/watch?v=vx-x-yXXE9I ______________________________________________________________________ Subscribe: https://www.youtube.com/channel/UC2UXDak6o7rBm23k3Vv5dww/?sub_confirmation=1 ______________________________________________________________________ Real SQL interview question walkthrough series: https://www.youtube.com/watch?v=Td-cmLfQ7uU&list=PLVD3APpfd1tuXrXBWAntLx4tNaONro5dA Check out StrataScratch for SQL interview prep: https://stratascratch.com/?via=tina ______________________________________________________________________ About me Hi, my name is Tina and I'm a data scientist at a FAANG company. I was pre-med studying pharmacology at the University of Toronto until I finally accepted that I would make a terrible doctor. I didn't know what to do with myself so I worked for a year as a research assistant for a bioinformatics lab where I learned how to code and became interested in data science. I then did a masters in computer science (MCIT) at the University of Pennsylvania before ending up at my current job in tech :) I have accepted that I will forever be a lazy person so have since decided to embrace that. My motto is to always minimize effort and maximize outcome! ______________________________________________________________________ Contact youtube: youtube comments are by far the best way to get a response from me! linkedin: https://www.linkedin.com/in/tinaw-h/ email for business inquiries only: hellotinah@gmail.com *If you're reaching out through linkedin, pleas
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This video provides tips and strategies for acing data science interviews, including product and technical interviews. The speaker shares their personal experience and insights on how to prepare for common data science questions and improve technical skills.

Key Takeaways
  1. Research common data science interview questions
  2. Practice SQL and technical skills
  3. Review data science concepts and machine learning pipelines
  4. Prepare a strong data scientist resume
  5. Develop a personal project or contribute to open-source projects
💡 Preparing for data science interviews requires a combination of technical skills, practice, and strategic preparation. Focus on building a strong foundation in data science concepts, machine learning, and SQL, and practice whiteboarding and problem-solving exercises.

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