# What I learnt after 50+ data science interviews

Learn about the companyKnow well about the company you have applied to.

Apart from the basic information such as revenue, different functions, the company motto and beliefs, find out more about the company’s clients or products.

Learn about the projects they are working on and ask questions around it.

Also, it is good to talk about how you can add value to the company in general.

4.

Know SQL and ExcelInterviewers assume everyone knows Excel — and hence you won’t be tested on it.

Along with Excel, SQL is a must-know for every data scientist.

All the data querying will require SQL and you will definitely be tested on writing SQL queries — and in all of my interviews, it was a live coding exercise.

So you have to be quick on your feet as you may not be allowed to use any online help.

5.

R/PythonMost of the R/Python interviews were given as a take-home test.

The take-home test is very similar to a problem you would find in Kaggle.

It is well defined and the data is fairly clean.

However, you would be expected to be thorough in your analysis- starting from EDA to building multiple models and tuning your model, and finally providing insights and recommendations.

The choice of language is yours — R or Python.

Also, using Jupyter notebook for the test is recommended for better readability.

6.

Basic StatisticsGo through the elementary statistics you would have learnt in college — Probability, Statistical Inference, Hypothesis Testing, Central Limit Theorem, Law of Large Numbers etc.

A good understanding of p-value, confidence intervals and hypothesis testing is required.

Also, practice probability questions on Conditional probability, Bayes theorem.

I was asked to explain these concepts in the simplest way possible — so understanding the concepts with an example in mind would be useful.

7.

Machine LearningIt’s necessary to have a thorough understanding of some of the basic ML algorithms such as Linear Regression, Logistic Regression, kNN and k-means clustering.

Questions on the basic algorithms can be rigorous such as assumptions of linear regression, how do you perform backward elimination, what are the different parameters you check to build a regression model etc.

And, it is good to have a general idea of the other algorithms like Lasso/Ridge regression, SVM’s, Neural Network etc.

Also, it is definitely okay to say you don’t know about a specific algorithm as long as you don’t have it in your resume.

Surprisingly, I did not encounter many questions on Deep Learning — so it should be good to check the job description to anticipate these questions.

8.

Open-ended data science problemsIn a lot of interview rounds, I was asked an open-ended data science problem and this is to check your thought process for solving a problem.

My tip is to never jump to a solution directly, take your time, think out loud, and think of the various factors that affect the problem’s objective.

As an example, I was asked how I will approach a dynamic pricing problem for a flight.

So, it’s good to start with the drivers of flight fares— duration of the flight, time of booking, source, destination, fuel charges, demand etc.

And then maybe you can talk about what algorithm/methodology you can use to combine the various factors.

For example, multiple regression can be used to predict the optimum pricing of the flight.

The interviewer won’t expect you to reach an optimal answer or any sort of conclusion- however, this is just to check your ideas and approach to solving a problem.

9.

Data InfrastructureThis was rarely asked in my interviews, and it was mostly asked in start-ups and smaller sized companies where there is a significant overlap between the roles of data scientists and data engineers.

In such cases, it’s good to know Hadoop, Spark, cloud services such as AWS, Azure, and other technologies which they would have mentioned in their job role.

10.

Project ManagementThere were a few questions regarding my project management abilities.

Specific questions involved my previous roles in projects, how I worked with cross-functional teams, the challenges I faced, my experience leading a team etc.

Some questions revolved around specific instances during the project where I have walked the extra mile to ensure the success of the project.

These are the top areas around which most of the questions were asked.