The explosive demand for data-literate individuals has so completely saturated the talent pool that recruiters are now seeking technical knowledge alongside business acumen, communication, or leadership skills—behavioral tactics that are best attuned to the company vision. This makes correctly predicting data scientist interview questions a skill unto itself. Here’s a complete guide on how to master one.
Data Scientist Interview
The digital transformation of industries has seen a spike in job roles that are constantly evolving to accommodate professionals who are both tech- social-savvy. So, how do you design a data scientist interview to filter only those with a mix of hard and soft skills? Let’s look at some of the common questions asked at interviews for this role. However, do keep in mind that the questions below are just recommendations and may vary from interviewer to interviewer.
What is the Interview Process for Data Scientists?
A typical data scientist interview process comprises four tests and lasts anywhere between three to four days. Each test has a corresponding score that allows data scientists to move forward. The primary online test involves Multiple-choice Questions (MCQs) on artificial intelligence, machine learning, logical reasoning, statistics, distributed programming, graph theory, and basic aptitude. The group test is an on-the-spot coding test that is followed by multiple personal interview rounds to fetch a balanced pool of candidates.
ALSO READ: How to Become a Data Scientist
Basic and Advanced Data Scientist Interview Questions
While interviewers cover a dynamic range of topics, here are a few recommended questions and hints on how to answer them.
1. Tell Me the Difference Between a Box Plot and a Histogram
Box plots and histograms visualize numerical data. While histograms comprise special bar graphs that mostly focus on data ranges or the underlying probability distribution, box plots compare multiple data sets and provide all the descriptive values of the interquartile data range.
2. Fundamentals of Neural Networks
The core components of a neural network are layers, neurons, activation function, and weights and bias. Layers refer to a collection of nodes that operate within the neural network at a specifically defined level. Neurons are the basic units of a neural network that receive information and pass them on. The activation function determines the importance of a specific neuron input within the neural network. Weights and bias adjust the overall strength of neural networks and the meticulous adjustments within individual neurons.
3. Tell Me the Difference Between Regularization Methods Such as L1 and L2 Regularization
Lasso regularization (L1) works with absolute values that bring up the cost of outliers in a linear fashion. Ridge regression, or L2, works with the square of weights, therefore massively increasing the cost of outliers. This makes working with L1 regularization very robust.
4. What is Cross-Validation?
The technique of training a model on the subset of input data and testing the same on previously unseen complementary subsets of the data set is known as cross-validation. It helps check how models germinate independent data sets.
5. Why is Dimensionality Reduction Important?
Dimensionality reduction fetches the least-important variables within a model and removes them, thus decreasing its complexity and reducing data noise.
6. What is the Difference Between Supervised Learning and Unsupervised Learning? Give Concrete Examples
Supervised learning comprises training machines with predefined data sets such as testing a model’s expertise in identifying football accessories. The model is trained on data sets of football gear; this helps it combine the inputs and come up with the correct answer. Unsupervised learning comprises unlabelled data sets where models predict outputs with zero human intervention. An example is giving the same model a random mix of inputs on sports goods. It analyzes these to find trends and patterns, eventually coming up with football accessories without any supervision.
7. What is the Principle Component Analysis (PCA)? Tell me the Type of Problem You Would Use PCA for
The principle component analysis is a technique used for increasing the interpretability of data sets without information loss and consequently reducing their dimensionality. Maximizing variance by creating non-related variables allows PCA to use summary indices for observing trends, patterns, and outliers. The representation of a multivariate data table helps understand how observations are related to variables.
Popular Data Scientist Interview Questions and Answers at FAANG+ Companies
1. Find the Distribution of Two Random Numbers.
When the same probability space defines two variables, the distribution of their joint probability will be the same across all pairs of outputs. It will also be constant for any count of random numbers.
2. What is the ‘Fair Coin’ Problem?
A coin is called fair or unfair depending on how closely it imitates a generic coin. While a fair coin will always have different outputs (one tail and one head), an unfair coin will repeat outputs (two tails or two heads).
3. How Can You Increase Conversion on an E-commerce Website?
One can increase conversion rates by:
- Tracking website interactions
- Shortening forms
- Conducting A/B testing
- Persuasive design and easy navigation
4. What is the Problem with Choosing a Door?
The Monty Hall problem refers to a problem where there are three doors—two doors hide no prize while the other one does. The trick is to find out which door hides the prize.
5. What is the Elevator Problem?
It is a problem with scheduling three elevators across 10 floors. The task of elevator scheduling has to happen in such a way that the total time taken to reach people to their destination levels is the shortest before it returns to the ground floor.
6. What is the Coin Toss Problem?
The Coin Toss problem deals with calculating the probability of fair and unfair outcomes each time a coin is tossed.
Tips for Preparing for Your Data Science Interview
Here are the five best ways to prepare for a data scientist interview:
- Research the specific job role, the company, and how you can be the best candidate
- Research your past experiences and list the most relevant projects you worked on
- Revise the fundamentals of data science
- An ideal candidate will be ready with technical skills and knowledge of domains such as modeling, statistical analysis, and the data development lifecycle
- Focus on your upskills while negotiating your salary
- For further tips, do look into Emeritus’ comprehensive courses on data science. Go nail that interview!
By Bishwadeep Mitra