Data science is an emerging field and the career opportunities that it brings forth are immense. This data science domain is vast enough to the extent that there are numerous fields that one can explore. Out of the many professions that the magical world of data has to offer, data science developer is one of the most sought-after ones. If you are keen on becoming a data science developer and are preparing for the interview, we have got you covered. In this article, we will address the top 10 data science developer questions asked in interviews. Have a look!

What is the difference between data science and data analytics?

Now, you might be surprised as to why an interviewer would come up with a question as basic as this. Well, no matter how basic the question is, there are countless people who tend to get confused between the two. Having a clear understanding of the two and why would you like to be a part of data science is what your interviewer might be keen on knowing.

What do you understand by root cause analysis?

The main aim behind root cause analysis is to analyze industrial accidents. You need to explain why this analysis is a problem-solving technique and used for isolating the root causes of problems or faults.

How regularly should an algorithm be updated?

For this question to be answered, you need to have a fair understanding of algorithms and how they function. After this is well understood, you can very well answer how regularly should an algorithm be updated.

What are confounding variables?

Data science field has a lot to do with variables which is why this question becomes one of the most frequently asked ones. To put it simply, confounding variables are extraneous variables in a statistical model. Give a detailed explanation of the same and also explain how are they related to the independent and dependent variables.

What is Naive Bayes Classifier and how does it work?

Naive Bayes Classifier forms an integral part of data science and data science developers have to rely on it for a majority of their work. This algorithm is based on a probabilistic model, which works on the Bayes Theorem principle. Explain to the interviewer, the underlying principle and also how Naive Bayes' accuracy can be improved. That added information would be a good way to create a good impression.

How do you avoid overfitting your model?

Before answering this, you need to have a fair understanding of what is overfitting. Only then will you be able to answer how can one avoid overfitting. While understanding this concept, you will come across regularisation techniques, a way of avoiding overfitting. Having a fair understanding of these techniques is also critical.

What are the conditions for overfitting and underfitting?

Now that you have a fair understanding of overfitting, why leave behind underfitting? Go through the entire concept of it and what would follow is knowing in detail the conditions for overfitting and underfitting.

What is linear regression?

Yet another common question that interviewers usually ask us about linear regression. In simple words, linear regression is about understanding the linear relationship between dependent and independent variables. A good understanding of dependent and independent variables always helps.

What do you understand by recommender systems?

Recommender systems predict how a user would rate a product based on their preferences. Explain to the interviewer the same in detail and also how these systems are further classified.

What are the true-positive rate and the false-positive rate?

This concept is quite important in the field of data science which is why this makes into the list of top 10 data science developer questions asked in data science developer interviews.