Data Science Job Interview – Full Mock Interview



This full-length interview will show you what a data science interview is like. This is a great video for anyone currently in the job-market for a data-focused role. It is also a solid video for anyone who wants a better understanding of the machine learning process. They cover topics that include building a dataset for training/testing purposes, feature vectorization, and model implementation details. Consider pausing after the question and thinking about how you would answer them.

Interviewer: @KeithGalli
Interviewee: @KylieYYing

⭐️ Contents ⭐️
⌨️ (0:00:00) Video overview & format
⌨️ (0:02:13) Introductory Behavioral questions
⌨️ (0:07:46) Social media platform bot issue task overview
⌨️ (0:15:26) What are some features we should investigate regarding the bot issue?
⌨️ (0:25:02) Classification model implementation details (using feature vectors)
⌨️ (0:41:38) What would a dataset to train models to detect bots look like? How would you approach collecting this data?
⌨️ (0:51:38) Technical implementation details (python libraries, cloud services, etc)
⌨️ (0:56:01) Any questions for me?
⌨️ (1:03:42) Post-interview breakdown & analysis

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This Post Has 35 Comments

  1. Keith Galli

    Thanks for sharing this interview freeCodeCamp!!

    I hope this video is helpful for you all. If you have any specific questions, feel free to reply to this comment and I'll do my best to respond 🤠

    The job interview process can definitely be stressful. Try your best to not get too caught up with rejections, but instead focus on what you can control. Consistency in interview prep goes a long way. Get a little better each day. You got this!!

  2. system2

    MIT…yeah because that's how we all start 😂

  3. Gel D

    that was pretty intense at the beginning when he started asking about how would a email filter model look like or how would data set look like to feed the model… She went into implementation immediately which is a normal resort because how else can you defin the data set if your not gonna think of the function that might be doing the filtering. Overall was handled nicely from both sides. Thanks for sharing.

  4. youtuber

    Anyone noticed about mr elong tusk🤔

  5. Dexter

    Can we have DevOps/ Cloud Engineer mock interviews too?

  6. richDon

    Can we have Game programmer mock interview please!

  7. Dom McKean

    I don’t think this approach is quite right. You’re diving into models before any data analysis. Can we clearly define a bot? We need to look at sample bot accounts. How many different types of bots can we identify? What are the similarities between bot groups? Can all the different bot group feature values ranges fit inside a single data envelope or should we concentrate on identifying a single bot group at a time?

    The amount of time the account has been active is prob a strong feature.

    Where are bots coming from historically?
    Are account names and profile pic very similar to an existing account?

    There could also be an anti-dataset, where accounts that were classified as spam complained and got reinstated? This could help mitigate misclassification.

    Ethically, could some demographics’ accounts be more susceptible to being wrongly classified as spam?

  8. Dom McKean

    If you ever read the Twitter Privacy Policy, you’d know a lot more features Twitter can use to infer a user’s identity and authenticity, and therefore classify as spam.

  9. theawesomeharris

    I realize this might be something smart about catching spam bot that twitter is currently doing.

    There is actually delayed pushed notifications to new posts. Whenever I receive a push notification of Elon's new posts I immediately clicked in and I can see from the time posted it is actually some time back, maybe minutes or tens of minutes back and whoever replying immediately to this post is a very good chance of being a spambot and this may be how the platform is catching spam bots nowadays by delaying push notifications to users. Brilliant!

  10. Anton Fedun

    Personally, I didn't like the interview. Probably it's something about the answers and personality of the interviewee. The answers seemed shallow and they were not discussed properly in detail, there were almost none real interesting and on-point follow-up questions from the interviewer

  11. Folkus On Me

    cried meself to sleep when she said she has a masters
    edit: the way she speaks about things omg, wow, I wanna be like her one day

  12. Ken

    Do these Interviews really take 1 hr and more? I’m just curious.

  13. Randy4i i

    So mr tusk is hiring again, so soon? Seriously though, A software developer interview would be great.

  14. Mohammed Shehada

    Cool interview,

    Wanted to add, the account's age would make a very good feature I guess

  15. iorekby

    "You had me at 'Bachelors and Masters from MIT' 😁"

  16. xrailgun

    this is the most reasonable interview I've ever seen, which ironically makes it somewhat unrealistic and useless.

  17. Andreas P

    This is a great interview, thanks for doing this! If only all data science interviews would be like this in reality (w/ some exceptions), the world would be a better place.

  18. Great video, but next time can you please post a how would successful job interview look like?

  19. Larry Priest

    don't look at screen, look at camera… focus on other person. be better prepared so U can ask and answer the Q's without looking to the sides. Also tech Q's should be as general as possible, for obvious reasons, getting into a technical discussion is taking my or the co. expertise, & PO and spreading it around.

  20. Daniel ndukwe

    I kinda surprisingly enjoyed this. I didn't even know when the interview started. Feels like two people having a convo about data science

  21. bravo1oh1

    "our CEO has been complaining…". I would fire this guy

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