Data Science Job Interview - Full Mock Interview
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- Опубликовано: 17 май 2024
- 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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Key takeaways:
1. Talk a lot and explain why you decided to go with the specific solution
2. Don't be afraid to take a pause and rethink the problem
3. Don't get fixated on one aspect of the problem too much, always try to approach it from the bird's view
4. Focus first on the easier parts of the problem and then approach the harder ones
5. Remember it's a conversation with the interviewer, not the solo showoff
6. Ask them questions about the work they've done so far, what they learnt in the company, look for the signs of being valued as a person in a team, how would the first 6 months on the job look like
Talk with a purpose. I hate people that talk to fill space lol
No you don't want it to be a conversation with the interviewer. Point 5 is wrong, if you are the 'solo showoff' and you talk through a detailed correct solution to a problem for 30 minutes and you hit many of the faucets then the interviewer will be impressed. You want as little tips or hints for the answer as possible, questions to clarify the problem are great if they aren't dumb questions.
I learned that you should probably do some research on the company you want to work at so that you spend more time practising relevant topics
Thanks for featuring!!
You did an amazing job, keep up the good work
Very Good Job . superb way to learn from you
I admire you.
You are really confident while communicating with interviewer. It's almost like 2 colleagues discussing about a problem. Great work
You deserve that
I appreciate that this interview is more like a conversation, with a focus on problem-solving. In the past, I've had interviews where the tone was passive-aggressive instead of constructive, so it's refreshing to have a more productive experience. It's great when interviews can be a valuable use of time, rather than a frustrating waste.
Even not knowing much about data science, this interview was very helpful in learning how it might be applied to real, known problems, and the mutual feedback at the end was very helpful to learn about the dynamics of interviews, too! Thank you guys for doing this.
I love the part they pull open a Google doc to clarify their points and have it noted down.
Excellent problem-solving approach.
Focusing on the problem, its scope, its limitation, whats expected behaviour, what isnt etc ...was always something that I was rushing ahead with.
Jumping to the solution right away is something that I had to unlearn.
Also great on Kylie for being super composed. Another super power and not easy to do in live situations.
Great video!
Thank you for sharing this video. As someone who is transitioning into the Data Science field( Machine Learning/ AI), I was very surprised that I was able to keep during the interview. I was it lost at all. I don’t have a technical background, but I’ve been studying ,Azure,python,GitHub,R,SQL etc… pretty hard for the past few months, and doing some labs I’m feeling pretty confident I can make the move.
As someone who just finished their masters and is looking for a job in data science, this interview really boosted my confidence cuz I was able to respond to every question
Exactly my thoughts…. Glad to hear someone else with similar thought
enjoy your underpaid intern in the capitalistic world. Everyone is replaceble, you will make zero difference.
@@yugiohfanatic1964 that's a very sad way to view the world. Hope you feel better soon
@@duckcluck123 hello simp
@@yugiohfanatic1964 are you 12
Thank you for this interview. For the spam issue, you can tag on the traditional features that Kylie Ying mentioned, and then use a multi-lingual embedding model to create vectors out of the post content. Use these features + embeddings to train your model.
Hello, I dont usually comment on RUclips but this time I just wanna say thank you for the people involved in this video. This really helped go through my technical interview and I ended up getting the job I wanted. The introduction and close of the interview was almost a copy of what happen in my interview. Obviously, the technical part was different (in my case they just asked about the technical challenge I had to solve prior the interview) but the way I approached the questions was very similar. Just THANKS!!
Wow, I didn't expect such a video. Very interesting. Thank you for sharing!
Great video, I watched it from beginning to end. I liked how she answered the questions. I was surprised at how conversational the interview was. If possible, could you post a Data Analytics interview as well please. Thank you.
This was really a useful video. If interviews are like this. It's love 🎉
One thing we can add in this feature are the links. Certain spam posts consists of similar links to the same post/ profile.
Not only bots do it, but even people constantly spam their account in the comments. So Link Frequency, Link Context and so on.
That feature used with Bayes Classifier can be useful to make the model more robust. Bag of Words from NLP can also be used in order to make this Link tasks easier.
Just an opinion of mine. 😊
This was so helpful, thank you! It was a delight to watch Kylie's problem solving approach :)
As someone who is currently in college and is actively preparing for interviews, this video helped me because i answered every question almost easily.
Grrr i couldn’t answer them all , but I’m gonna catch up to you soon GIDEON
I kinda surprisingly enjoyed this. I didn't even know when the interview started. Feels like two people having a convo about data science
more mock interviews please
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.
My Knowledge increase a lot by watching this. Please Upload more mock interviews like this. I also some technical details in model implementation.
Great video on interview processes! ❤
I really liked that Keith decided to use a Google doc, because in these what I would First Round 'team fit' and knowledge-gauging interviews the assumption is you'll just talk over zoom and not do whiteboarding or use another tool. This was a good reminder to expect the unexpected - you could be asked to do anything - maybe even code :)
This mock interview really impressed me.
Thanks for boosting my confidence, that I might get a job if i stay focused on learning. Good luck
this is very helpful. I wish people do more of this type of content
Incredible video! Nice job.
Keith is one fantastic teacher. He took my analytics skills from 0 to 5 very quickly. Great content.
Ghanta
@@yuti65 why bro?
So, I think I want to go into Data Science beside Web Development, and this was pretty handful. Even tho, I miss some points, I answered some questions in a pretty good way. Thank you!!
Interesting to see a data science interview, it's the same format as other tech interviews
Very fruitful interviews you guys put together, thanks for sharing!
Proven. Reliable. Keith Galli!! I love you man!! 😂😂
Thanks for this great insight!
Meaningful insights, thanks for sharing it!
Can you pls provide Data Engineer interview Process as well? It will be greatly appreciated.
Thank you for changing lives for better around the world, including mine! 😁✌️
Thank you for this. Wow!
Nice video. I think in the beginning when asked if she had any first thoughts on the issue of spam bots, one thing that could've been added was 'what are the positives of bots' Too much was said about the negative aspects of bots, and the first impression I had was, if all bots are so negative, just ban bots. But bots do have a role, and many bots are used to automate functionality. So the real important point is, identify bots that are being malicious in some way. Then dive into how to develop metrics to identify the concept of 'malicious'.
Thanks for sharing this.
my approach is slightly different :
Basic data collection :
1. You have basic details capture when you create a YT account, name, email, DOB, Image, etc
2. Assume that everytime you log into RUclips your activity is recorded as follows : comment made (if any), time, post, ip_address, email, like, dislikes, report, type of report, followers, activity spent (scrolling, browsing etc) .
Selection on queries :
1. Filter on accounts where comments are made and activity time in one session is extremely long (say more than 12hrs) or very frequent activities in a time interval ( e.g. log in/out 5 times in 1 minute)
On feature engg side :
Extract features -> in 1day how many comments are made, number of links posted in comment, number of reports per time interval, number of words, number of words which are spam, difference between account followers vs those followed , activity time in minutes/seconds (scrolling 100 videos/minutes indicates bot action).
Target variables:
1. Set threshold based on existing patterns visible eg. more than 50 comments are day, more than 100 reports in 1hr, clicking 100 video's in 1min, if spam words > 10 ( should satisfy any of these conditions ) -> set to 1 else set to 0.
Classification model side : Sklearn (fast and quick to test your ideas and features), Model- XGB, Logistic/SVM (baseline)
Deploy, see and rework
This is not perfect but this what I was coming up while seeing this video.
This is awesome thanks!
Please upload all mock interviews for web development, app development etc.
Interview seems very easy, as a product owner I speak to our Data scientists and many of them are building business projection models etc. Is the technical implementation the key skill here? Because working in tech now, the first hour of this interview I think most of our stakeholders would even be able to answer during requirements gathering due to subject matter expertise. Like what to train the model on is usually subject matter expertise. I think its so well understood among tech stakeholders that something happens in the system, can you show me a 1 if it, or a combination of things happened, or a 0 if not and push that to some visualization report.
Thank you so much for sharing.
This channel is amazing
great video! more data science mock interviews, pls
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.
tell us more about the interviews you had plz
@@alirezouali3119 can you tag me , if he replies
Real life interviews are simillar. Usualy there would be a separate interview to asses your coding skills.
Very exciting!
Very Nice , Post more content on Data Science!
Can we have Data Analyst mock interviews too?
Yes please
Yeah, that'd be great!
No, unfortunately not 😔
Please 🙏
Yes please
Would love one for Data engineering too
This actually got me hyped for a job interview!
are data science interviews even remotely similar to this mock interview? If so i would be hyped too lol
@@nb7070 personal experience from my machine learning interview - 90% interview is a conversation between you and the hiring person about ur projects and your ideas and approach.
Actually, I think so. I joined a fresher DS Interview a year ago, and they asked me to whiteboard coding, IQ problem solving, mathematics, machine learning, etc. Even though I did not do too worse except for the whiteboard part, I did not land that job. Lmao
excellent interview it inspired me to go ahead and update myself into Data Scientist again.
wow is that how it works? I'm gonna update myself to billionaire.
Impressive! Well detailed! @KeithGalli, that siren was pretty loud. Perhaps you have trained a model that picks and feeds only @KylieYYing's voice to you and removes any other unwanted voices or sounds 😁😜
The interview was awesome
Greate video. Create a Data Analyst Mock, please
Cool interview,
Wanted to add, the account's age would make a very good feature I guess
Amazing interview
Wow, thank you so much
Great video, but next time can you please post a how would successful job interview look like?
Great Interview, thank you you guys! CEO of quitter Elon Tusk, got me😂
Would love to see the technical programming interview as well! Thank you for sharing this.
she can't code LOL!
I have noticed that there wasn't much conversation in regards to what ML model to use or what hyperparameters or architectures. Is this normal for an ML/Data Science interview?
I think a good approach for labeling things as spam is writing a program called checkspam that references the posting frequency or if the post are the same within a certain time frame. It could label it as spam if it falls within such parameters. You could make it so the program checkspam would only run if it was reported by another account in order to combat the potential of false flagging from people with negative intentions.
I simply think any implementation that checks the number of reports is easily abusable
Right? There could have been more out-of-the-box thinking@@MrTheanimekiller
If time is considered as attribute... A model that predicts Human entered predicted time based on Tweet length can be made.. now after getting the predicted time we can have Bayesian Network with a particular account, time took to predict whether its a SPAM/Bot attack.
please do more mock interviews many thanks
One for Full stack development please.
Excellent video! What about a video on an entry-level no experience ciber security interview? 😊
Hi very useful video. Please do the same video for a Machine Learning Engineer position as well that would be very useful. Thank you.
Amazing content
Is there a mock interview for super junior entry level role in data science?
Great❤ work
She came in saying she wanted to build the Iron Man suit 😂
OMG!! I literally said the same thing.... I got interested in AI almost when the ironman first movie released and I kinda moved slowly from Electrical Engineering to DS. I wanted to have my own JARVIS and get into robotics later in the future and since then have been in AI. Have said the same thing in all my interviews! cant believe she had the same motivation :) assuming the motivation is real regardless of it being a mock interview :)
one way that will help in labeling for a given account , is how many of the account's posts are reported as spam .
If an account shares a post that is for some reason , is reported spam 1000 of times but the other posts have 0 spam report , then how confident should we be to label it as a bot
Feature idea : alongwith the number of followers . Each follower should get a weight , i.e, if an account is followed by genuine people (celebrities) then the weight of that incoming link should be high . Note that the problem with the idea that whether an account is followed by other bots or not , was circular in nature .
It would be great and crazy if after this video she does the implementation of the model like step by step
Thanks a lot for sharing, would this be like an interview for a senior data scientist position ? I once had an interview for a big tech company ( not FAANG though) and it took like 20 min, I was interviewing as a medior so I’m surprise that an interview could really take this long.. 😅
Most interviews I've done for internships, and entry level jobs took more than 1 hour.
I am a data scientist (with PhD level education and work experience) in the UK and applying to a job which would sponsor a H1B visa if successful. Does anyone have any specific advice for how to tailor my CV(resume) for this?
Great Video! But are these interview really that easy? So is it like if you have confidence and you're able to have a smooth conversation about what you are thinking about the topic, you get the job? Or is it like for an intern level role, hence easier questions?
This is just one type of interview that you'll encounter in a job search process. The open-ended nature of it should make it less stressful than technical coding interviews. That being said, there is a lot of opportunity here to really demonstrate your abilities. A senior data scientist candidate would be expected to go into a lot more complexity & implementation details than an intern-level candidate. A senior candidate should also be able to clearly communicate trade-offs of any decisions that they make. This type of interview is really designed to see how well someone understands the data science process and to measure how well they can communicate what they know.
In my opinion, part of the reason this interview seemed pretty easy is that Kylie is very confident in her approach and could get to key details without needing much/any prodding from me on the interviewer side.
To get the job, you'll probably need to succeed in this interview as well as a technical coding interview or two and a behavioral interview.
@@KeithGalli so specially for a Data Science role do the interviews go any deep into implementation details. Like right now I'm looking for similar roles and was easily able to answer these questions myself. The only thing I'm not that confident about is if they ask how decision trees mathematically work or implement a neural network from scratch (I mean I could, but in the heat of the moment I cant). Do they ask such questions in Data Science roles or just in ML roles?
Every interview is different so it's definitely possible that a company could ask you to go into implementation details, but from my perspective knowing when to use decisions trees or neural networks is more important than being able to implement them from scratch. In the real world, we have libraries that make decision trees & neural networks very easy to use. You almost never need to implement something from scratch. As a result, it's more important to understand how they work at a high-level and when to use them and what the relevant Python libraries are. Hope this makes sense!
@@KeithGalli yup, that makes sense. Thanks for replying!
Usually there are at least 2 rounds: A round like this which is personality/ high level problem solving/culture fit, and there will usually be a technical screening as well.
Technical screenings are usually first and weed out people who don't understand the tech stack/ Data Science principles at all or very well. If you know the tech stack or most of it, it is usually no stress.
The initial tip I got from this was to not shave before my interview.
In my opinion this data scientist interview is pretty weak and useless. The interviewer didn't probe deeply enough on important details to get a real answer of whether the candidate is capable of building a successful spambot model, applying it to the production system and maintaining/improving it continuously. A large part of the interview is spent on listing a bunch of potential features that could be used by the model to classify spam, which does not require deep thinking, understanding or knowledge of the problem. Very little implementation details were actually asked or answered. This candidate only talks about the approach in a very high-level and vague way, like "I'd use Tensorflow and try a few things to get a sense of a good-enough model." This kind of general answer is not helpful at all. We've seen so many candidates who can do the high-level talking points, but fail to have a good understanding of the entire ML production workflow and lack basic implementation skills.
There are flaws with some of those solutions. Like flagging an account as spam if it is follower by spam accounts. That could lead to valid accounts being flagged as spam or even attacked by bots intentionally adding users to flag them as spam. The thing about using email from random domains is also problematic in many ways, and also using emails that use random characters (some of us use random emails for different accounts precisely to keep spam away and improve privacy). You could also not even guess bot characteristics and feed data for models to try to find common characteristics and trends.
That's what I thought of too. For example, checking if a name is common is not possible. But of course, we're watching from the sideline so it's easy to form criticism.
so i've never had any technical interviews before thats why i'd like to ask do they usually ask you to code some stuff on the go or its just some broad questions about the technicalities of the problem you're trying to solve ?
great video
Thanks!
Thanks for the video. Both of you did a great job in making it feel realistic. My only question is. Is this really the sort of difficulty of the entry data science jobs? It feels suspiciously easy or shallow to me. Can someone back this up? Thank you again!
That's what I'm saying
I have been interviewing for entry level data science jobs and this is fairly accurate! Although I have often been asked about what advanced concepts I know and how I've used it.
Do we usually have other rounds of interviews to actually do the coding like software engr usually have?
Can we have DevOps/ Cloud Engineer mock interviews too?
very helpful
30:00 - omg, and im literally hearing the sirens with my studio pairs headphones.
I definately think, this interview was good. 😁
Sir i have completed bsc physics but now I m very instrested in programing can i m eligible in msc IT Or data science
Great Video, Now can you make a video on how to get interview calls for freshers from no name universities but abundance of talent
i wonder how elon's bandaid solution of monetizing the ability to even use twitter has been working, thats just making more steps for the attackers to achieve their goal
but anyways, wonderful example of behavior questions, i wish i was asked these tailored questions instead of the more general ones (how to deal with bad coworkers, etc), the technical questions are really useful too because i was able to be excited to give my own answers while Kylie was answering
Can we have azure and AWS data engineering interviews also please
Another feature to be included could be the IP address from which the request is coming
⭐ 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
Please upload more like this videos
being reported as spam shouldnt be the the only way to mark your mail as spam, many bots/trolls that doing just because they can. World of warcraft has this useless report spam
/offensive that automatically kick and mute you from the game for a month if enough reports are summited and doesnt need that many reports in the first place. But we need to have another check to be sure isnt just fake reports, an extra check can be made from the last 10 posts, if any have offensive/repetitive languages combined how often was written.
Thank you
thank you
Clarification at 48:37 can’t we just consider the accounts that have already been reported as spam and create a dataset just based on these accounts?
I need Django interviews ❤️
this is the most reasonable interview I've ever seen, which ironically makes it somewhat unrealistic and useless.