The issue is that Data Science itself is a very broad, interdisciplinary field; meaning that there's no set guidelines in place in regards to questions answered. Google does it, so that means everyone must right? A data scientist MUST be a good programmer. Well when I work for you, I'm not allowed to use a computer with internet? Great HR skills there. Sure go for it! This link was posted on Dec 30, 2018 in blind Curated List of Top 100 LeetCode Questions. [–]bdubbs09 2 points3 points4 points 2 years ago (2 children). A lot of coding questions fall into the irrelevant category. How does does numerical optimization relate to data engineering (maker)? LeetCode is the platform where people practice their coding skills and prepare for software engineering interviews. Check your inbox for a message from us. You can create a free account there and play with their public dataset. Categories are If you can solve them quickly, you would have a high … Upon completing this Professional Certificate program, you will be armed with the skills and experience you need to start your career in data science … All questions come from real companies and are from real interviews. This morning I realized I'm barely a business analyst, and it's depressing. Level up your coding skills and quickly land a job. StrataScratch sources many of their questions from tech companies and continously releases interview questions currently in-rotation at those various companies. (Operations research problems for a pretty unique business. While the previous alternative is best suited for beginners, StrataScratch is one of the best platforms for those who have some basic knowledge and for those who are looking to test and grow their data science knowledge and skills. Or at minimum show you can think of the best solution even if the syntax isnt perfect. But companies waste these by: Not tailoring them to what they are looking for, Providing little instruction or guidance and no scoring rubric, Not leveraging the result in consequent interviews, Doing them for the sake of saying they do them, What does it mean to tailor something? It’s not a bad thing to look for that in a candidate and just because you can google an answer to a question doesn’t mean it’s a bad one. Again, if you think it’s to stroke their ego you’re wrong. Thank you! I do not do any challenge that looks like it will take many hours or provides little instruction and no scoring criteria. [–]ztnq 0 points1 point2 points 2 years ago (0 children). There is generally a primary focus of each resource. Why is HackerRank / Leetcode a Thing for Data Science? That said, our test is based on real problems we've encountered. But their Python tutorial lacks this kind of exercises and do not focus on growing your skills. Mode Analytics might be a great LeetCode alternative for you that offers tutorials and courses for learning SQL and Python. Also, to narrow certain syntax issues or problems, you can organize the challenges according to the subject areas and subdomains. So if you prefer to learn in a brief and clearly expressed manner, you will like their learning method. Most employers have noticed that hiring without testing results in complete idiots writing crap code that can't be maintained or even looked at without breaking. Create Account . Open in app. And the system on HackerRank tracks your progress. I know there are specialized packages that are only available in R though. Rendered by PID 9785 on r2-app-067690931eb403c3f at 2021-02-13 19:18:17.654040+00:00 running b9e2eac country code: US. But then most hiring processes are actually quite terrible. Customer Placing the Largest Number of Orders. In fairness, to be an effective data scientist, you should be able to produce reasonably clear code, reasonably efficiently. Most data engineers I know would be pretty terrible data scientists (and vice-versa). Good bye then. The Data Science Skills Competency Model In 2018, IBM built the first Data Science Apprenticeship program in the United States. These are supposed to be random questions that just test how you approach a problem, not necessarily looking for a right answer. And if a company goes to the bottom of your list because they asked you a challenging question that you can’t google on the spot (a brain teaser) then good luck to you man. This includes tools like databases, python libraries (numpy, spark, pandas), and terminal work. Reasons that are not really good reasons are: If a company really believes this, they should test it. Concepts and topics like natural language processing and computer vision are covered here. Your model is only as efficient as the code that generates it. It's sort of a social network for programming. [–]beginner_ 35 points36 points37 points 2 years ago (11 children). Although users enjoy learning through the problems on HackerRank, some find them too much like puzzles and enough like the real work. But Overall, DataCamp is well-organized, intuitive, and subject area focused platform where you can test and improve your skills through various problems and projects. If you only ask candidates questions about things you are good at instead of what the candidate is good at. No doubt about that. I mean if it is a top tier tech company, I wouldnt be surprised. DataCamp is meant to be a streamlined way to learn skills like SQL, R or Python, Statistics and more through video tutorials & coding challenges. It wasn't designed to help prepare data scientists for their data science interviews or improve their analytical skills in SQL and python -- two tools that are required to become a successful data scientist. I'd suggest really doing your homework on each company and if possible, getting a sense of the day to day work. Then we are completely away from reality and not really doing anything about my problem solving skill just assessing with how much BS I will out up with, which is the real reason for these questions. This is probably something that can be standardized, and made more fair. Has anyone tried to work remotely abroad and keep their job? These are valuable tools that HackerRank offers if you want to test your skills and prepare for the interview or prepare for the problems you might see on the job. I like to mix both data science and programing so I usually split my time and ask 1-2 easier programing questions and some ML as well. Similar to LeetCode, you have the ability to filter by topic and skill level. If you make them do a data challenge and ask them a brain teaser in the consequent interview, you have wasted it, and by extension their time and effort. It is the primary educational platform meant for the advanced-beginner to an intermediate engineer looking to brush up on their technical concepts. the problem I have with the questions is that either the answer is trivial (google, brute-force) or requires specific domain in this case about prime numbers which very few will know, is impossible to come up with in an interview and could be easily googled. The congitive test was about five mini-games involving both numbers and logic. [–]pankswork 2 points3 points4 points 2 years ago (0 children), [–]GoodHeight 0 points1 point2 points 2 years ago (0 children), Haha definitely - thanks for the feedback, [–]schroedinger11 0 points1 point2 points 2 years ago (0 children). Basically companies choosing to use these platforms are selecting for engineers who have (a) practiced those problems a bunch (b) are good at random algorithmic insights without taking the time to do research. Do you want to have one of the high-paying DS jobs? Judging by your post here I wouldn't hire you, and I'm an entry level analyst. Spend 5-10 hours going through "Cracking the Coding Interview" and it may elucidate that while the process is annoying, there is some kind of method there. [–]lovelyvanquyen 5 points6 points7 points 2 years ago (2 children). In this course you will be introduced to Data science and how it’s defining the future of data. It really made me think about what sort of job I wanted. I got an OA 'IBM NA Data Science (EPH 2020-2021)' which I got a referral for '326081BR Entry Level Data Scientist: 2021' ...are these questions belong to the same position? My point was that ingenuity was very much needed for the types of problems my boss worked on, therefore it’s a good question to ask. To test that you can program. But what do I know lol, [–][deleted] 2 points3 points4 points 2 years ago (1 child), How much are HackerRank/Leetcode questions actually asked for data science positions? I think, a rule of thumb, don't waste peoples' time and effort, that is the decent thing to do as a human being. Describe the Bias vs Variance tradeoff in ML, 2) Describe how it relates to underfitting/overfitting, and 3) If I have a model that works great on training data but poorly on validation data, what would you do to improve the algorithm if you had access to any resources you may need? I’m not the most active on here (slowly transitioning from lurker to more active) but I’ll try to answer any q’s you have, [–]dn_red_usr -1 points0 points1 point 2 years ago (0 children), [–]datareinidearaus 0 points1 point2 points 2 years ago (0 children), Seems like brogrammers are just wanting to adopt it from statistics there, [–]vexkov 21 points22 points23 points 2 years ago (4 children). That being said, just two years ago I went 1/20 applying to jobs and similarly hated the interview process. [–]toshi_g 8 points9 points10 points 2 years ago (1 child). The one thing they are useful for is testing if someone can code at all, and a lot of people who are trying to make the transition to being a data scientist can't. Additionally, 'data scientist' can mean very different things depending on the company and role they are looking for. [–]GoodHeight 90 points91 points92 points 2 years ago (17 children). Specifically built for the advanced-beginner to intermediate data scientist to help grow your knowledge and advance your career. [–][deleted] 0 points1 point2 points 2 years ago (0 children). The simple and straightforward approach to teaching programming languages like SQL, Python, and more makes this platform popular and one of the best alternatives to LeetCode. Software engineering is part of the solution whether you like it or not. Also, we look at some uses cases for data science. [–]breck 1 point2 points3 points 2 years ago (0 children), easy to eliminate people from huge pool of candidates whereas data science questions will often require more in-depth discussion. It's just reuse and rehashed shit from other companies. From basic to most advanced, you can pick up a lesson you want depending on your skill level and grow your knowledge of various concepts. REDDIT and the ALIEN Logo are registered trademarks of reddit inc. π Rendered by PID 9785 on r2-app-067690931eb403c3f at 2021-02-13 19:18:17.654040+00:00 running b9e2eac country code: US. He was looking for people that are good at pure math, and it was very much needed for the types of problems we worked on. why that model? There are so many considerations to account for and tradeoffs to make from the modeling to production transition, it is impossible for someone without a detailed understanding of the algorithm to do it. During my application and interviewing process, I was also frustrated with having to practice implementing Dijkstra's algorithm and coming up with Dynamic Programming solutions for Hackerrank tests and the like. Definitely brain teasers, so stop asking those. And no, don't come up with "you are not allowed to do that". Those are stupidly arbitrary sometimes too. but I'd probably a brainteaser (like the one I was asked. How many do you think u have to solve to be a good candidate? But sure, you do you bro! A lot of people seem to think that data scientists develop models and hand them off to another team to implement. [–]nieuweyork 1 point2 points3 points 2 years ago (0 children). Paulo Vasconcellos in Towards Data Science. Hi all, Multiple openings at IBM IN data science Request for a referral 4+ year exp Masters in data science at UNT Thanks 0 1 facebook twitter reddit hacker news link If you need to ask these questions just ask the simple most prevalent ones. [–]jaco6y 2 points3 points4 points 2 years ago (7 children). Unfortunately, in practice this does not happen at the rate it needs to happen. I applied for a data science/machine learning fall internship opportunity at … Wow, you have proven i can use a computer with internet. For example, a large bank I interviewed with started the interview process by having me log in and solve 2 problems in two hours. No need to reinvent the wheel. There are people that get so flustered when asked that question that they can’t even come up with a solution of “well I would write a program that just brute forces of up to the square root”. “Leetcode Algorithm” is published by Jen-Li Chen in JEN-LI CHEN IN DATA SCIENCE. Coding optimization algorithms from scratch would be "Maker" 's forte. Some roles want you to build out their infrastructure, build scheduled tasks, this looks more like a data engineering role, but again call it a data scientist role. They can be quite hard but most of them (except one) are manageable. Start Exploring. It's a great way to rank candidates. There's also deep learning concepts including neural networks to help you truly understand ML theory. Let's find out why you should know the best alternatives to LeetCode. I didn't get out of college last year and stressing about getting a job. There's a hot trend for data scientists in 2021 to be full-stack -- meaning they must know how to build pipelines and deploy their models on some cloud provider. It helps you to pick up and learn a new topic in a short period of time. That means the algorithms have to be in production at scale. But to do so, it must have clear scoring/success criteria such that any way that does lead to objective success counts as success. (Do you want job security?) Offers many ways to test your knowledge and skills through practicing. Just to get everyone in a good level of programming. This is because no one really knows what to do for data science interviews. Well, I know, you know, that I know that you are/will/might grind out hundreds of questions on Leetcode, what's the point? They provide separate training courses for each topic. Which in my experience makes working for the company, meh, Also, those questions are not as random as you would think. I'm not going to continue with this as it is obviously a waste of time. The 4 biggest mistakes of data science job applicants. To help improve your data science skills, StrataScratch provides 500+ SQL and Python questions to practice. Mode.com is one of the valuable alternatives to LeetCode if you're looking for a basic and straightforward method to learn. (And is solvable with enough input / background). Hackerrank style challenges suck, but the key is to pick one programming language, and do the challenges until you know the basic datastructures and learn the algorithms. Ya that's rough. https://www.glassdoor.com/Interview/If-you-look-at-a-clock-and-the-time-is-3-15-what-is-the-angle-between-the-hour-and-the-minute-hands-QTN_204557.htm, You spend enough time to scour through these questions on the internet, most likely in an interview whenever they do the "random braaaaainteaser timeee!" It is literally not a thing at tech companies and hedge funds. These sections cover how to use these tools to build models and manipulate data. Has anybody tried the Coursera IBM Data Science Professional Certificate course? Nobodys trying to nail anybody or screw a job candidate. Most companies don't know how to interview data scientists because alot of the time they have no clue about what they'll have their data scientists do. If you need a role where they will be taking a model from inception to production, then make them build a toy pipeline where they train a model and 'deploy' it locally through Flask or something. Data science has three separate pillars. Now that I've started at a DS position - I completely understand why. Same answer. They're not very "niche" but I had to include them on this list because they're a great resource for data science practice. In order to be a good data scientist, you need to program well. Each page of concepts provides the description from syntax to important functions and keywords. If I'm in an interview and they come with some questions like the "how much would you charge to wash all the windows in X place " your company just went into the bottom of the list of potential companies to work for because that shows me as a potential employee and asset to your company that 1) you don't even have the interest/skills to come up with questions that test the ability of someone in a real world setting that applies to your company , 2) you probably googled these bunch of questions just to pad your interview process. In practice. This week, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio. Its not like they’re replacing personal questions with these... It’s icing on the cake of your interview process if you can get one of those difficult questions right like the “what’s the angle between the hour and minute hand on a clock that says 3:15?”, I’ve been asked that one also. “Leetcode SQL” is published by Jen-Li Chen in JEN-LI CHEN IN DATA SCIENCE. It is hard to measure how well some does on these objectively. You're going to have to write real code. My leetcode progress. Ah but here's the secret: those are garbage ways to test software engineers as well. For sure. Currently I am a data analyst looking to level up. But do you want to work on hard problems? After a decade of work experience and dealing very closely with HR in different countries in different continents. If you intend on doing basic data mining and develop algorithms, it is a key requirement. And then there is data engineering, which is taking these analysis and modelling activities and making everything work faster, more robustly, and on larger quantities of data. https://www.businessinsider.com/answers-to-google-interview-questions-2011-11?IR=T, Where Google even went on record to say: "Those hard questions were a waste of time", https://www.businessinsider.com/how-google-hires-2013-6?IR=T, [–]jaco6y 2 points3 points4 points 2 years ago (5 children). I'm looking to get some learning resources (and if they offer a certificate that's a plus) for data science. https://www.glassdoor.com/Interview/If-you-look-at-a-clock-and-the-time-is-3-15-what-is-the-angle-between-the-hour-and-the-minute-hands-QTN_204557.htm. I can understand these tests for a software engineering position and it seems to be the norm. Compare it to a more pragmatic process. Data science is the process of using algorithms, methods and systems to extract knowledge and insights from structured and unstructured data. Good luck to your company there bro, [–]mtl_economics 4 points5 points6 points 2 years ago (0 children), For fun I googled that 3599 question and it showed up on Tesla's glassdoor. No need to reinvent the wheel. As a beginner, you can pick any area you are comfortable at and start from scratch. Is it possible a dataset is simply not predictive? 2 min read. There are also high-paying jobs in finance, where I'm sure you solve interesting problems. Similar to W3Schools, their no-frill approach to teaching is simple that attempts to explain all the relevant concept in a straight-forward manner. Do you want to develop sophisticated algorithms? No clue. You come across like a stuck up dick with anger issues, [–]daguito81 0 points1 point2 points 2 years ago (0 children). I must be really smart! [–]nieuweyork 0 points1 point2 points 2 years ago (2 children). Why are we making people waste their time doing this? [–]mhwalker 13 points14 points15 points 2 years ago (3 children). There are over 1050 questions to test for different technical concepts, each with multiple solutions. That means you have to write good code. 1 Show 1 reply That doesn't mean that people have malice or negative intent towards you. There might even be some goodish reasons why these … I I just think they are pointless. That said, 100% of places I interviewed at required either a take home modeling exercise and/or whiteboard coding (usually something dumb like SQL). Once you understand the underlying theory, there's another section called core machine learning which covers the standard algorithms and techniques used to implement the models. Yes, the answer can be googled but the fact that you can solve it on the spot shows ingenuity. I know the 3:15 question is recycled. Just now encountering that many companies want a software engineer AND a person who knows data science / distributed technologies. [–]RoboOWL 0 points1 point2 points 2 years ago* (0 children). If you need a role that does lots of causal analysis, then format the challenge to test this. [–]RaggedBulleitPhD | Computational Neuroscience 4 points5 points6 points 2 years ago (1 child). Finally worth remembering that most companies think of hiring as "it's okay to miss on good candidates, but its not okay to hire bad candidates". Whatever the reason, lots of people are searching for LeetCode alternatives. For me its be mid level to advanced at a data access language/framework, and entry to mid level at coding with a desire to learn. After landing a job, I had a chance to interview some candidates. While data science and AI are relatively new in the market, the concept of extracting value from data has been around for a while. Most of the stereotypically interesting problems are in the tech industry, and those jobs are generally high-paying. Their teaching method is simple and basic. After they explained the concept, you will generally find an immediate example of how it looks with proper syntax. etc. There is also a section on data projects which will give you a few exercises on how to build machine learning models. It is trivial. Sometimes the role deals more with causal inference, but no production models. Yes, the answer can be googled but the fact that you can solve it on the spot shows ingenuity. Aw shit. The most upvoted answer is: /s. For example sometimes the role is heavy on dashboarding and ad-hoc analysis, this requires a lot of SQL, but little modeling and no productionizing of code. Just for fun ofc. It is hard to measure how well some does on these objectively. They are designed to teach you analytical skills related to SQL and Python. When you have to iterate over large data set, yeah the algorithm should be efficient. This ensures that the material you're learning is practical. ), [–]Bayes_the_Lord 1 point2 points3 points 2 years ago (0 children). Oh, don't get me wrong.. On the other side of the table as part of my jobs I've interviewed 100+ people and maybe 1 in 5 get hired. 167. Have been quite surprised at answers so far (I thought this was a somewhat basic question that would lead to a good discussion): [–]Skyaa194 5 points6 points7 points 2 years ago (0 children). Those currently on the data scientist team cannot know who was hired in what process, track the success of candidates. Of course data science people will not use those leetcode type questions in real life and will most likely rely on importing … And it’s at least an answer in the interview but it doesn’t actually answer the question asked. But it's definitely a possibility if you care enough. - Eventually some SWE has to read your code, and maybe make it live somewhere with down-stream dependancies. save yourself some cash, and ask yourself this before you buy an online course. Their lessons are sorted from basics to more advanced concepts. [–]neo4reo 0 points1 point2 points 2 years ago* (2 children). That way you can tailor applications to companies that better fit your skills. You bring up a valid point, you can have a good question like this, but only if the criteria is clear. It’s to see how you approach a tough question, they often don’t care if you got it right. Why? LeetCode is to help software engineers to get jobs. I understand it to a degree, like someone mentioned you should be able to answer easy ones on Leetcode. Furthermore I don't think creating a good interview process is easy. HackerRank is another valuable alternative to LeetCode. Here's a quick summary on what you can learn under each topic. There is data modelling, using the data that we have to estimate the data that we wish we had. Leetcode problems are their own skill...you grind the problems, do your interviews and then forget about them until the next time you're looking for a job. It goes from basics till complex problems. And you’re missing the point if you think the solution to all of these questions is one you have to study for and remember. I was not talking about code optimization. It will jog your memory and answer because it will be a small variation of copy, [–]PhysicalPresentation 0 points1 point2 points 2 years ago (1 child). For sure. I think data science problems might have more variability in the answers and less chance for a "right" answer. But literally anywhere else I'd be shocked. I do a ton of coding in Spark, Hive, SQL, Python via UDFs, shell scripts etc but this kind of stuff is incredibly tangential to leetcode problems. But just because they have 1000+ questions doesn't mean they're the best. [–]jaco6y 6 points7 points8 points 2 years ago (14 children). Their interactive lessons offer heavy use of examples. Amazon still does it, every trading company or hedge fund still does it, etc. Famous last words. In my opinion the most relevant task that can be asked of data scientists, the task that mimics what they will do as a data scientist is the data challenge. What is important is that you don't focus on something trivial like how much feature engineering they did for the deployed model and not nitpick the causal analysis role for how much the code looks like 'production level.'. If you make a candidate do a data challenge and then bring them in for an onsite and not talk about their work on it, you have wasted it, and by extension their time and effort. You have proven I know what prime numbers are, I can program and I'm not a complete idiot. 49. Under ML theory, there are lessons that cover the underlying mathematical concepts relevant to ML models such as statistics, probability, and linear algebra that are crucial for any data scientist to understand in order to build and implement models. Hahahaha you sweet summer child. Many tools such as W3Schools, Guru99, and Mode Analytics focus on straightforward teaching concepts. You just set up a loop with up to 1300 or so and see if there is a division that returns a whole number, [–]jaco6y -3 points-2 points-1 points 2 years ago (9 children), I get why people wouldn't like those questions, but from the type of guy my boss was and who he was looking for it made sense that he asks it. There is data analysis, taking raw information and turning it into knowledge that can be acted on or that can drive a decision. It's really easy for them to default to software dev questions because that process has been around for some time now. A data scientist should be a good programmer so I can understand a hackerrank. Actually it's a pretty common (shitty) practice to try and do brainteasers in interviews. It was designed for data scientists by data scientists. What are possible future career paths for data scientists who do a lot of software engineering? If you don't mind me asking, what languages do you regularly code in? [–]lovelyvanquyen 0 points1 point2 points 2 years ago (0 children). how to brute force it or to come up with some solution although it's not elegant) I think it's a pretty good gauge to see if people get frustrated that they can't solve something and how they handle it. [–]nivenkos 5 points6 points7 points 2 years ago (0 children). 200? I've been asked those questions. However, others such as StrataScratch, DataCamp, Confetti.ai, and HackerRank focus to provide resources to build your understanding through practicing real-world problems. [–]neo4reo 1 point2 points3 points 2 years ago* (4 children). There is a variety of languages including SQL and Python you can choose from. I have been through a lot of interviews (and given some). So a lot of this might just have to do with it being much easier on the interviewers. [–][deleted] 17 points18 points19 points 2 years ago (12 children). In my (limited) experience it seems to me that companies that make you do take-home challenges skip the Leetcode questions, while companies that don't have a take-home ask them. And those questions actually test general problem solving ability. This question can be answered by anyone that knows how to factor numbers. Just to see their problem solving skills..), ask some questions about statistics fundamentals or what model they would use in a given scenario and why, and then maybe a programming one tailored to the language they use the most (python, SQL, R, etc). We start by defining what is data science, what is data, and what is the role of data scientists. It could be to provide a place to prepare for an interview, build up basic skills, or test and improve your knowledge. Which at the end will be answer by someone that spends X ammount of time online looking at "Hard brainteasers you could be asked in interviews" and remember the little tricks or funny answers. I'm sure that works for some companies in some industries. You just disqualified yourself as an employer and this is not a brainteaser / puzzle just one of these pseudo-clever question often used in consulting area.