What are the Most Common Mistakes Data Science enthusiasts make in an Interview?

  • Preparing only theoretical topics without applying them

Let’s say that you are in the middle of a data science interview and the interviewer asks you – What is random forest and how does it work? Being a simple and standard question you answer the question smoothly. Then the follow-up zinger comes – How would you improve the performance of the model in the context of the business?
Now, unless you have solved a data science problem previously using random forest and tuned its hyperparameters, you won’t be able to give a proper answer which can lead to doubt in the mind of the interviewer.

  • Assuming what you see in ML Competitions is what Real-Life Jobs are like

There’s no better to prepare for a data science role than participating in machine learning competitions. This is undeniable. The problem is it doesn’t make you an industry-ready professional. Usually, the interviews include case studies that test your problem-solving skills and domain knowledge and these are usually gained with experience.

  • Using too many Data Science Terms

Your resume is a profile of what you have accomplished and how you did it – not a list of things to simply jot down. When a recruiter looks at your resume, he/she wants to understand your background and what all you have accomplished in a neat and summarized manner. If half the page is filled with vague data science terms like linear regression, XGBoost, LightGBM, without any explanation, your resume might not clear the screening round.

  • Not working on Communication Skills

Communication skills are one of the most underrated and least talked about aspects a data scientist absolutely MUST possess. You can learn all the latest techniques, master multiple tools, and make the best graphs, but if you cannot explain your analysis to your client, you will fail as a data scientist. This is what the interviewer will be testing in the interview process.