Data scientist Interview Questions You Must Know!

Data scientist Interview Questions You Must Know!

It is no surprise that in the era of Machine Learning and Big Data, Data Science professionals are in heavy demand. Nowadays, companies need to leverage the data if they want to get ahead of their competitor and improve the way build products, serve their customers, and run their operations.

If you want to become a data scientist you must impress prospective employers with your skills and knowledge. During your interviews, you'll need to show your technical proficiency with Big Data concepts, frameworks, and applications.

So, here we present you a list of some most popular data science interview questions (along with answers) that you will face during interviews.

Data Science Interview Questions

1) Differentiate between supervised and unsupervised learning?

In supervised learning, we feed labeled and known data as input to the algorithms and it has a feedback mechanism as well. 

The most used supervised learning algorithms are logistic regression and decision trees.

Whereas in unsupervised learning we feed unlabeled data as the input and it has no feedback mechanism. Most used unsupervised learning algorithms are hierarchical and k-means clustering and apriori algorithm.

2) How to avoid overfitting a model?

Overfitting means when a model is set for only a small amount of data and it ignores the bigger picture. There are three methods to avoid overfitting:

1. Keep the model as simple as you can and remove the noise in the training data
2. Use cross-validation techniques, i.e, k folds cross-validation 
3. Use regularization techniques, i.e, LASSO to penalize particular model parameters if they cause overfitting.

3) Suppose you are given a data set consisting of variables with more than 30 % missing values. How will you deal with it?

If the data set is comprehensive, we can simply remove the rows having missing data values and we can use the rest of the data to predict the remaining values. It is the quickest way.

In the case of smaller data sets, we can replace missing values with the mean of the rest of the data with the help of pandas. 

4) How should you maintain a deployed model?

Following are the steps to maintain a deployed model:

Step 1 - Monitor 

To determine the performance accuracy, constant monitoring of all models is required. Whenever you make some changes, you will have to figure out how your changes will affect things. So, you need to keep an eye to ensure that it's doing what it's supposed to do.

Step 2- Evaluate

Evaluating the metrics of the current model is to be calculated to know if there is any need for a new algorithm. 

Step 3- Compare

The new models are compared to each other to discover which model is performing the best. 

Step 4- Rebuild

Hence, the best-performing model is re-built on the current state of data.

5) 'People who bought this also bought…' recommendations seen on E-commerce platforms are a result of which algorithm?

This happens because of the KNN algorithm which makes use of recommendation engine, which is accomplished with collaborative filtering. Collaborative filtering describes the behavior of users on the platform and scans through their purchase history. 

Then the engine makes predictions of what a person might like on the basis of the preferences of other users. 

 For instance, Amazon's algorithm observes that 90% of users who buy a new phone also buy tempered glass in the same cart. So, the next time, whenever a person will buy a phone, he will see the recommendation to buy tempered glass as well.

6) What are the steps in making a decision tree?

Following are the steps to make a decision tree-

1. Take the entire data set as input.
2. Look for a split that maximizes the division of the classes. 
3. Apply the split to the input data.
4. Re-apply steps 1 and 2 to the split data.
5. Stop when you meet the criteria.
6. Finally, clean up the tree if you went too far doing splits.

7) Explain cross-validation.

Cross-validation is a technique that is used to evaluate Machine Learning models by training various ML models on different subsets of the available input data. It is mainly used when the objective is to make a prediction or to estimate the accuracy of a model. 

The main goal of cross-validation is to test the model in the training phase (i.e. validation data set) to curb problems like overfitting and gain information about how a particular model will generalize to an independent data set.

Final words

Being a data scientist isn't easy, but this career is very rewarding and there are loads of available positions out there in the market. We hope these data science interview questions will prove helpful for you to get one step closer to your dream job. So, prepare yourself for the hardships of interviewing and most importantly stay sharp with the latest trends and changes in data science.