If you are looking for projects, refer to our list of 24 Ultimate Data Science Projects to boost your Knowledge and Skills.
Practice Solving Puzzles – A Key Data Science Skill Puzzles are a fairly popular way of evaluating a candidate’s quick thinking and analytical acumen.
You need to be logical, creative and good with numbers to solve puzzles.
Many organizations use puzzles for testing their candidates on their problem-solving skills.
They want to know about your thought process and how you approach a problem.
I cannot give you a complete guide to solving each puzzle, but I do have a few tips for you to proceed towards puzzle-solving: Approach the problem slowly and understand all the details.
Ask for any assumptions if they are not explicitly mentioned These are meant to showcase your thought process.
So make sure to walk your interviewer through your solution while you think Do not stick with an approach for too long.
Take cues from your interviewer and modify your approach accordingly Realize that it is okay if you were not able to completely solve the puzzle.
Different puzzles have different levels of difficulty and not all of them are meant to be solved in one sitting Try solving the puzzles in our list of 20 Hard Data Science Interview Puzzles that every analyst should solve at least once.
Prepare to Face Case Studies Organizations use case studies as a means of evaluating candidates on how they approach real-life problems.
Case studies are the closest thing to the problems that you would be encountering in your role later on.
I have seen freshers struggle the most with this part of the data science interview process.
The tricky aspect of a case study is that it might not be directly related to data science.
For example, I got a case study around how to predict the number of black cars in Delhi NCR right now.
It’s a tricky one – but if you have a structured mindset – you’ll knock it out of the park!.Approaching a case study can appear hard since there is no fixed formula to solve them.
But you can use the below points to guide yourself through them: Ask a lot of questions.
Whatever questions pop in your head, ask away!.It will help you uncover a lot of details that you will require for the solution Structure the problem.
This could be organizing all available data into a table.
Structuring might unveil some hidden patterns in the data Practice!.Try case studies from different domains like retail, healthcare, business, etc.
The more you practice, the easier a new problem will feel Remember what is important is good brainstorming and a great discussion.
The goal is not to reach a fixed or pre-defined solution, but rather to find a path to it and show your thought process Have a look at some of the case studies on Analytics Vidhya (practice each of them and you’ll be interview-ready in a jiffy): Call Center optimization Dawn of Taxi Aggregators Optimize the Products Price for an Online Vendor Research the Job Profile and the Organization Researching the job profile has obvious benefits.
You would be able to streamline your preparation based on what is required from the role.
Sometimes, employers may even ask candidates a question or use a keyword to make sure they read the job description carefully: “What technologies do we work with?” “What are you expecting from this role?” “Can you tell us the latest project our data science team open-sourced?” These questions will be dreadful if you didn’t read up on the company and the role.
I highly recommend spending some time reading about the company’s mission, vision and core values.
Find out about their key achievements.
Try and find the data science set up that they have and what kind of projects they work on.
If possible, find out about the hierarchy of the organization and how the data science team fits into it.
Studying the organization and its structure will help you frame better questions for your interviewers.
This shows your enthusiasm and curiosity towards the organization and leaves your interviewers impressed.
Review confusing terms Are there any data science terms that have bamboozled you before?.I’m sure there are a few – this is true for even experienced data scientists.
A few confusing terms or concepts that I encourage you to read up on a day before your interview: Type I and Type II errors Precision and Recall False Positive Rate and True Negative Rate Business metrics v STatistical metric Model deployment I frequently have to look up the difference between these terms and I am sure most of you do as well.
These can stump you if asked in an interview.
You know the answer, but the slight differences just aren’t coming to you.
Source: AB Tasty Make sure to revise such terms a day before the interview.
Refer to our glossary of common machine learning and data science terms for a quick idea around these concepts.
End Notes These are just some last-minute tips.
The entire data science interview preparation is a long process.
You need to start months in advance and build your profile.
There are also multiple rounds in a data science hiring process, including: Telephonic Screening Assignments On-site interview, which has several rounds like technical, case studies, puzzles, guesstimate, and more.
The ‘Ace Data Science Interviews‘ course covers all of these rounds in detail.
The course also has a rich collection of Interview Questions along with many helpful tips and tricks.
This could significantly increase your chances of acing your next Data Science Interview.
So make sure to check it out!.You can also read this article on Analytics Vidhyas Android APP Share this:Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Reddit (Opens in new window) Related Articles (adsbygoogle = window.
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