Standard Chartered data scientist interview questions

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Standard Chartered data scientist interview questions

 

A typical In every interview process for data science is same and question ask into the topics. Also Same asked in Standard Chartered data scientist interview questions.

There are different in rounds, some companies position includes multiple rounds. Often, one of such rounds covers theoretical concepts, where the goal is to determine if the candidate knows the fundamentals of machine learning.

In this post, I’d like to summarize all my interviewing experience  —  from both interviewing and being interviewed  —  and came up with a list of theoretical data science questions.

Standard Chartered data scientist interview questions

Topics include in the following topics:

Linear regression Standard Chartered data scientist interview questions

Validation

Classification and logistic regression

Regularization

Decision trees

Random forest

Gradient boosting trees

Neural networks

Text classification

Clustering

Time series

That’s, of course, subjective, and it’s based only on my personal opinion.

Let’s start!

What is supervised machine learning ?

Linear regression Standard Chartered data scientist interview questions

What is regression? Which models can you use to solve a regression problem?

What is linear regression? When do we use it ?

What’s the normal distribution? Why do we care about it ?

How do we check if a variable follows the normal distribution ? ‍

What if we want to build a model for predicting prices? Are prices distributed normally? Do we need to do any pre-processing for prices ? ‍

What are the methods for solving linear regression do you know? ‍

What is gradient descent? How does it work? ‍

What is the normal equation ?

What is SGD  —  stochastic gradient descent? What’s the difference with the usual gradient descent? ‍

Which metrics for evaluating regression models do you know?

What are MSE and RMSE ?

Validation Standard Chartered data scientist interview questions

What is overfitting?

How to validate your models?

Why do we need to split our data into three parts: train, validation, and test?

Can you explain how cross-validation works?

What is K-fold cross-validation?

How do we choose K in K-fold cross-validation? What’s your favourite K?

Classification Standard Chartered data scientist interview questions

What is the classification? Which models would you use to solve a classification problem?

What is logistic regression? When do we need to use it?

Is logistic regression a linear model? Why ?

What is sigmoid? What does it do ?

How do we evaluate classification models?

What is the accuracy ?

Is accuracy always a good metric?

What is the confusion table ?

What are the cells in this table ?

What is precision, recall, and F1-score ?

Precision-recall trade-off ‍ Standard Chartered data scientist interview questions

What is the ROC curve? When to use it ? ‍

What is AUC (AU ROC)? When to use it ?

How to interpret the AU ROC score? ‍

What is the PR (precision-recall) curve? ‍

What is the area under the PR curve? Is it a useful metric? ‍

In which cases AU PR is better than AU ROC? ‍

What do we do with categorical variables? ‍

Why do we need one-hot encoding? ‍

Regularization Standard Chartered data scientist interview questions

What happens to our linear regression model if we have three columns in our data: x, y, z  —  and z is a sum of x and y? ‍

What happens to our linear regression model if the column z in the data is a sum of columns x and y and some random noise? ‍

What is regularization? Why do we need it?

Which regularization techniques do you know? ‍

What kind of regularization techniques are applicable to linear models? ‍

How does L2 regularization look like in a linear model? ‍

How do we select the right regularization parameters?

What’s the effect of L2 regularization on the weights of a linear model? ‍

How L1 regularization looks like in a linear model? ‍

What’s the difference between L2 and L1 regularization? ‍

Can we have both L1 and L2 regularization components in a linear model? ‍

What’s the interpretation of the bias term in linear models? ‍

How do we interpret weights in linear models? ‍

If a weight for one variable is higher than for another  —  can we say that this variable is more important? ‍

When do we need to perform feature normalization for linear models? When it’s okay not to do it? ‍

Feature selection  Standard Chartered data scientist interview questions

What is feature selection? Why do we need it?

Is feature selection important for linear models? ‍

Which feature selection techniques do you know? ‍

Can we use L1 regularization for feature selection? ‍

Can we use L2 regularization for feature selection? ‍

Decision trees Standard Chartered data scientist interview questions

What are the decision trees?

How do we train decision trees? ‍

What are the main parameters of the decision tree model?

How do we handle categorical variables in decision trees? ‍

What are the benefits of a single decision tree compared to more complex models? ‍

How can we know which features are more important for the decision tree model? ‍

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