Preparing for an Interview: Acing the commonly asked Data Science Questions

Preparing for a data science interview can be a bit of a task, as there is no specific set of questions to be asked in the interview. An interviewer can throw you off with an unexpected set of the question at any point of time, irrespective of your experience in data science or certificates you possess.

An interviewer will try to test your statistics, programming, and data modelling skills through a variety of questions. These questions are specially designed to check a person’s performance under pressure.

While pursuing a degree in data science, one should start preparing for the interview process. Preparation is the key to success, and starting preparations early will help you ace the interview.

In this article, we will discuss the common questions asked in a data science interview and how you can ace them. But before that, let us have a look at other key points to be considered while going for a data science interview:

Be thorough with your data science resume
Study up on your data science projects
Practice solving puzzles
Prepare to face case studies for data science roles
Research the job profile and the organization
Review confusing data science terms

Acing the commonly asked Data Science questions:

We have categorised the data science interview questions into six different categories:

Statistics	Programming (General, Big Data, Python, R, SQL)
Modelling	Behavioural
Culture Fit	Problem-Solving

Statistics Interview Questions:

The process through which data scientists take raw data and create predictions and models is called statistical computing. It is challenging to succeed as a data scientist without advanced knowledge of statistics. An interviewer will ask statistics-oriented data science questions and try to explore your understanding of the subject. Prepare yourself well to answer fundamental statistics questions during a data science interview. Some examples of questions asked are:

What are the assumptions required for linear regression?
What is linear regression?
What do the terms p-value, coefficient, and r-squared value mean?
What is sampling? How many sampling methods do you know?
What is the Central Limit Theorem, and why is it important?
What is the difference between type I vs type II error?

Programming:

To test your programming skills, the interviewers will ask you to solve programming problems in theory without writing the code. They might also offer whiteboarding exercises to code on the spot for them. Some examples of questions asked are:

Explain how MapReduce works as thoroughly as possible.
In Python, how is memory managed?
What are the different types of sorting algorithms available in the R language?
What are the two main components of the Hadoop framework?
What is the purpose of the group functions in SQL? Give some examples of group functions.
What modules/libraries are you most familiar with? What do you like or dislike about them?
With which programming languages and environments are you most comfortable working?

Data Modelling:

Using data modelling, data science provides value to a company by turning the raw data into predictive and actionable information. The interviewers will ask questions based on data modelling to unveil the information and knowledge you possess regarding the same. Unable to explain the theories and techniques you used in your respective models will leave a wrong impression on the employers. Some examples of questions asked are:

How is k-NN different from k-means clustering?
How would you create a logistic regression model?
How would you effectively represent data with five dimensions?
Tell me about how you designed a model for a past employer or client.
What are your favourite data visualization techniques?

Past Behaviour:

Behavioural questions reveal an individual’s character and performance, which is why they are favourite to an employer. Employers try to read between the lines and know how a person can affect the rest of the team. These questions help the interviewers understand how individuals react to a situation and what they have learned so far from their experiences.

The behavioural questions can be categorised further as:

Some examples of questions asked are:

Tell me about this job. Why did you choose to do it, and what do you like most about it?
Tell me about a time when you had to overcome a dilemma.
Tell me about a time when you resolved a conflict.
Tell me about a time when you took the initiative.
Tell me about a time you failed and what you have learned from it.

Culture Fit:

The employers generally ask these questions to understand the background of a person and evaluate how they can be an excellent fit for the company. They are basically trying to figure out what made you incline towards data science and the company you are interviewing for. Some examples of questions asked are:

Give a few examples of "best practices" in data science.
How did you become interested in data science?
What do you think makes a good data scientist?
What is the latest data mining conference, webinar, class, workshop, or training you attended?
What is the latest data science book or article you read?

Problem-Solving:

To check your problem-solving skills at some point in the interview, an interviewer will throw a data science problem your way. Some examples of questions asked are:

How would you come up with a solution to identify plagiarism?
How many "useful" votes will a Yelp review receive?
How do you detect individual paid accounts shared by multiple users?
You are about to send a million emails. How do you optimize delivery? How do you optimize the response?

Ace data science with SRM and LEARNXT:

There is no defined best way to ace any interview, especially a data science interview. Hopefully, the questions mentioned above would help you to grab your favourite data science job role in the desired organisation.

If you are willing to pursue data science, you can enroll yourself in the various degree and certificate data science courses offered by the SRM and LEARNXT collaboration. 1800+ hours of learning, Capstone Project, experiential learning through concepts and real-life case studies, and the latest applied data science curriculum aligned with industry requirements will prepare you into a skilled data scientist from the very beginning.

Stay tuned to LEARNXT to know more about commonly asked data science questions.

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Preparing for an Interview: Acing the commonly asked Data Science Questions