How I Got My First Data Science Internship (And How You Can Land One)

Key Takeaways

what's up everyone ken here back with another data science video for you today internships are kind of the rage this time of year and I'd love to talk about how I got my first data science internship there are a couple things that I think I did really well and there's also a couple things that I think we could have really done better and improved my chances a lot so let's get to it as usual if you enjoy this video please hit that like button and please subscribe if you'd like to see more content like this so for me I thought it was really important to focus on my resume first one thing I've learned now that I didn't know back then is that it's really important that you just have the necessary technical skills for a specific position so if a position requires Python and some sequel knowledge I would put that right up front at the top that your that your technical skills include Python sequel and anything related to that you don't have to be expert level in any of those things most internships really require a pretty basic level but letting them know that you pass their baseline requirements for technical skills up front is a way for them to mark those boxes and move on to the rest of your resume I think that you should also put a ton of your personal projects on your resume as well that is a huge key especially when I'm looking through resumes when I'm on the other side of the desk now for personal projects I recommend doing a couple different types of projects so the first one I do is a regression project I recommend doing a classification and a clustering project and then either a deep learning project or one where you use a gradient boosted random forests it's also cool if you use some PCA or factor analysis that shows that you have a breadth of the different kind of core algorithms that are relevant in data science today when I was applying I did those four types of projects that I mentioned and I made sure I did them on topics that I was really interested in that a lot of the jobs that I was applying for were related to so I did some analysis of some train data that I found when I applied to a local locomotive shop I looked at a lot of sports data when I was applying to sports teams I looked at food data because I applied also to a couple of food-related technology groups so if you can have something that is in the industry that you're applying a project you've done it lets you talk intelligently about that industry and it also really looks good it shows that you are genuinely interested in that field that you're applying so this first internship that I applied to I was in grad school I had just started my master's in computer science I had some data science skills but not a whole lot really and I probably sent out between 15 and 20 resumes and applied for that many jobs and some people you know it's like oh that's really easy I just send out 15 and I'm done I custom wrote every cover letter and I also made sure I catered my resumes to the specific positions that I was applying for for me I think you go you get a lot more out of customizing those things for each role if you put a lot of time in and you really focus on the value you can provide and understanding the industry of each role you're more likely to get something that you really appreciate and you really enjoy if you send out bulk resumes I just don't think that that's even as efficient as really putting the time in on a couple and doing it right after I sent out all these resumes and cover letters it turned out I got around five or six interviews they all start with a phone interview where you either talk with a recruiter or the manager of the team in these phone interviews I focused on really telling my story my background my educational background my project background and also what brings me to this company specifically I really like to brush up on the news really to accompany and I'll usually ask about something related to that to the interviewer that shows that you're engaged that shows that you know what's going on and you've been paying attention I can't tell you how valuable it is for a applicant to actually do their homework about a company outside of the basic stuff really going one level deeper is very impressive again especially when you're interning make sure you have very clear why you are applying to a place you know for example let's say I was applying to Boeing in an aviation company you know I might as a child have loved to go to the airport with my dad and I would watch the planes for hours and hours and I always wondered how that worked how we could make them safer how the technology was advancing in that field and you know working at that company would be incredible to me because I get to see the inner workings of that and I get to contribute to that so you know that's an example of in my opinion what a good Y would be it's tying your personal story your personal interest to the domain that you're applying to next some of the interviews would have a light coating or kind of data analysis portion those generally for the coding stuff it's not live I don't really like live coding interviews I don't think that that's generally a good practice but some people do them and I understand that I had to do a couple to work with a couple different data sets which was actually pretty fun you know you can explore I think feature engineering is cool when you're working with those that's something that might separate you from from the pack is thinking about okay not just running this through a bunch of different algorithms and parameter tuning and using a grid search everyone does that let's think about how we can change what we're putting in to get a better outcome so if you use some again principle component analysis or if you do some clustering to change up what features are there or if you do some sort of in Samba model that's creative I think that that really separates you from the pack these aren't necessarily used to say hey like this guy is you know incredible he's doing this differently than everyone else it's usually just assessing a baseline ability to do analysis so I wouldn't go too crazy about these things I'd make sure you do a good job if you want to impress them you can do some of the stuff that I just talked about but just make sure it is solid it looks clean your code is well commented etc because that'll get you to the next round so the next phase of the interview which I ended up getting to I think was three in-person ones I went in and I was able to basically talk about my project experience it's it's fun to be able to tell your story about why you chose a specific project and the outcomes that came out of it and that's something I really want to focus on you always want to talk about what the results of your work are so it's cool talk about the analysis it's cool to get into the details of of the algorithm and like any of the you know the parameters you've met you've turned etc but from a business perspective you're always thinking about the value you can create so one of the projects I worked on was a algorithm to determine how fair UFC outcomes were so when if I went to a decision how often could we predict correctly that who would win the fight and it turns out my you know my model wasn't very good it wasn't very good at predicting that but that could also be a symptom on the other side that maybe the referees are not good and they're not an objective judge of performance so that's something you could take that organization and say hey you know maybe you're huh fries aren't very good maybe they're not judging the matches accurately let's look into that further so even if your analysis doesn't have that good how come you can still have some interesting findings and really interesting questions associated with it I think that again taking that next level of thought and communicating how you know your analysis evolved over time is something that will really impress interviewers and I'd like to think that that some of my stories there were what got me that position occasionally there's some technical questions mixed in but for the most part they're pretty basic as long as you have an okay understanding of Statistics you should be able to get through them a lot of the time they'll ask what like multicollinearity is or they'll ask you what the vanishing gradient problem is these are things that if you just look through some basic interview material some like the interview cheat sheets you should be able to find pretty easily also if you don't know I think it's perfectly okay to be honest and say you know I don't know what that is I can look into it or I think it is something related to this and please you know please tell me if I'm wrong I'd love to learn more about this that mindset that you're continually learning and you're looking for opportunities to learn is something that will really you know rack up some good brownie points for you to get my first data science and your trip oh it wasn't an easy road you know I I sent out a lot of resumes that I custom tailored I had to really think about my background my experience and how that related to a lot of different companies I also had to really focus on telling my story and I think that that's one thing that I really like everyone to take away from this video is that make everything a narrative explain the whys you know why you want to be a data scientist why you want to work at a specific company and why you would be a good fit for this role I think wise are one of the main reasons that I got an internship in the first place and hopefully they can also help you get an internship either in this cycle or the next cycle thank you so much for watching and good luck on your data science journey