How do I crack 5 Data Science Interviews in a month — Tips for ML and Data Science Aspirants

Say Yes to New Adventures

I had a total of 2.5 years of experience in the IT industry. I love to read books and coding, especially python language. I had learned so many blogs on medium for end to end solutions and approaches for each one shared differently that gives me good intuition behind the architecture of product design and solutions. When I think about it, why don’t we think of building a new startup if we had all these skills instead of working for a company. But yes!,experience does matter in the IT industry. LOL

How to get calls or emails for data science and machine learning?

Simple answer for this please update your naukri.com. Update more keywords and tags to represent your roles and responsibilities. Add detailed approaches that you follow in each project and update and reuse daily thrice (morning,afternoon,evening in a day) (free time). I will guarantee you will get calls if you do this. Let’s start our favourite discuss on Interview experiences. I had totally 5 offers in hand, I had chosen the best out of it and that you may already know often :) When it comes to interview rounds — They are 4–5 rounds mostly. First round is all about the “Fit bit” round to check your ability to code and basic foundational Skills & Some theoretical machine learning questions.

Examples:

What is bias & variance trade off? How does it reflect decision tree modelling?

Write a Python Program to Check Common Letters in Two Input Strings?

How is an empty class created in python?

How will you identify and deal with missing values in a dataframe?

Why is log loss useful when you have other KPIs?

What are the advantages of leaky ReLu and how is it useful?

Similarly what, How, Why questions on simple foundational skills on machine learning and deep yearning and NLP. 2nd Round-

He asked me about my Industry projects as well as personal projects which were done in course case studies. My Industry projects are in various domains like TMT,RLT,Finance etc.

He used to ask me for some domain knowledge in it and basic understanding of business impact after completion of these projects.

Scenario based questions — What if you have a large set of categorical values in one column, how to preprocess it and what are methods you follow for it?

Try to implement logistic regression from scratch. (execute code)

How to train without labelling data using Self-Supervised Learning by Relational Reasoning.

Explain about deployment and scalability for your model.

What are tools used for visualisation and some functionality reading it like power BI, Tableau.

Some Managerial kind of questions

How do you handle when you are alone in a project?

What if you don’t have the concept or information regarding domain specific. What do you do?

Explain backprop mechanism and how it is useful in the real world?

Concepts regarding NLP tool kit and Data Cleaning especially for text classification.

Regression models how they are different from each other.

3rd round -

This simple and straightforward round since they were already checked your technical and coding skills in 1 st and 2 nd rounds of interview,

She was asked about how sagemaker uses for machine learning and functionality & services in aws.

What happens to overfitting of models? How do you overcome it?

When do you use mean,mode,median Imputation techniques for missing values?

What are several deep learning weights initialization techniques we have ?

Simple general questions — where are you from?

Are you willing to relocate? etc

4th round-

Obviously Hr round to discuss about compensation and company benefits etc.

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Conclusion-

Believe in yourself, Learn and revise all the concepts that you learn. Practise more problems. Confidence about yourself when you are going for interviews. If you don’t know the concept say that you will have the ability to learn and adapt but don’t say I don’t know. Don’t repeat negative remarks in interviews. All the best

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