Data science interviews certainly aren’t easy. I know this first hand. I’ve participated in over 50 individual interviews and phone screens while applying for competitive internships over the last calendar year. Through this exciting and somewhat (at times, very) painful process, I’ve accumulated a plethora of useful resources that helped me prepare for and eventually pass data science interviews.Long story short, I’ve decided to sort through all my bookmarks and notes in order to deliver a comprehensive list of data science resources.With this list by your side, you should have more than enough effective tools at your disposal next time you’re prepping for a big interview.It’s worth noting that many of these resources are naturally going to geared towards entry-level and intern data science positions, as that’s where my expertise lies. Keep that in mind and enjoy!Here’s some of the more general resources covering data science as a whole. Specifically, I highly recommend checking out the first two links regarding 120 Data Science Interview Questions. While the ebook itself is a couple bucks out of pocket, the answers themselves are free on Quora. These were some of my favorite full-coverage questions to practice with right before an interview.Even Data Scientists cannot escape the dreaded algorithmic coding interview. In my experience, this isn’t the case 100% of the time, but chances are you’ll be asked to work through something similar to an easy or medium question on LeetCode or HackerRank.As far as language goes, most companies will let you use whatever language you want. Personally, I did almost all of my algorithmic coding in Java even though the positions were targeted at Python and R programmers. If I had to recommend one thing, it’s to break out your wallet and invest in Cracking the Coding Interview. It absolutely lives up to the hype. I plan to continue using it for years to come.Once the interviewer knows that you can think-through problems and code effectively, chances are that you’ll move onto some more data science specific applications. Depending on the interviewer and the position, you will likely be able to choose between Python and R as your tool of choice. Since I’m partial to Python, my resources below will primarily focus on effectively using Pandas and NumPy for data analysis.A data science interview typically isn’t complete without checking your knowledge of SQL. This can be done over the phone or through a live coding question, more likely the latter. I’ve found that the difficulty level of these questions can vary a good bit, ranging from being painfully easy to requiring complex joins and obscure functions.Our good friend, statistics is still crucial for Data Scientists and it’s reflected as such in interviews. I had many interviews begin by seeing if I can explain a common statistics or probability concept in simple and concise terms. As positions get more experienced, I suspect this happens less and less as traditional statistical questions begin to take the more practical form of A/B testing scenarios, covered later in the post.You’ll notice that I’ve compiled a few more resources here than in other sections. This isn’t a mistake. Machine learning is a complex field that is a virtual guarantee in data science interviews today.The way that you’ll be tested on this is no guarantee however. It may come up as a conceptual question regarding cross validation or bias-variance tradeoff, or it may take the form of a take home assignment with a dataset attached. I’ve seen both several times, so you’ve got to be prepared for anything.Specifically, check out the Machine Learning Flashcards below, they’re only a couple bucks and were my by far my favorite way to quiz myself on any conceptual ML stuff.This won’t be covered in every single data science interview, but it’s certainly not uncommon. Most interviews will have atleast one section solely dedicated to product thinking which often lends itself to A/B testing of some sort. Make sure your familiar with the concepts and statistical background necessary in order to be prepared when it comes up. If you have time to spare, I took the free online course by Udacity and overall, I was pretty impressed.Lastly, I wanted to call out all of the posts related to data science jobs and interviewing that I read over and over again to understand, not only how to prepare, but what to expect as well. If you only check out one section here, this is the one to focus on. This is the layer that sits on top of all the technical skills and application. Don’t overlook it.I hope you find these resources useful during your next interview or job search. I know I did, truthfully I’m just glad that I saved these links somewhere. Lastly, this post is part of an ongoing initiative to ‘open-source’ my experience applying and interviewing at data science positions, so if you enjoyed this content then be sure to follow me for more stuff like this.If you’re interested in receiving my weekly rundown of interesting articles and resources focused on data science, machine learning, and artificial intelligence, then subscribe to Self Driven Data Science using the form below!If you enjoyed this post, feel free to hit the clap button and if you’re interested in posts to come, make sure to follow me on Medium at the link below — I’ll be writing and shipping every day this month as part of a 30-Day Challenge.This article was originally published on conordewey.comFrom a quick cheer to a standing ovation, clap to show how much you enjoyed this story.Data Scientist & Writer | www.conordewey.comSharing concepts, ideas, and codes.