Data Science Interview Questions
Image Source - Pixabay

Steering your career path towards Data Science is the pathway to unlock the best paying job roles in the near future! In today’s world, data is everything! This is the reason why there is an emerging demand for Data Scientists in the job market. So, it’s the right time for you to build your career that you will be proud of.  To help you with your upcoming Data Science Interview, here we have come up with the collection of top most important and frequently asked Data Science Interview Questions (recently updated 2019).

Preparing for an interview is not an easy task as you think! No matter how much experience you have or what certificate you have, an interviewer can throw you off with a set of questions that you didn’t expect. So we have curated this list of Data Science Interview Questions that are asked to the candidates frequently. Usually, the questions asked in the Data Science Interview falls into three categories:

  • Programming Data Science Interview Questions Based On Data Science Programming Languages Like Python, R, etc.
  • Technical Data Science Interview Questions Based On Statistics, Probability, Math, Machine Learning, etc.
  • Practical Data Science Interview Questions Based on the Projects You’ve Done, Your Work Experience, etc. has got you covered all these categories that will help you prepare for your next data science interview.

Data Science Interview Questions

Question: State the Difference between Artificial Intelligence, Machine Learning and Data Science
Norms Data Science Machine Learning Artificial Intelligence
Definition Subset of machine learning but it uses machine learning techniques to analyze and make future predictions. A subset of AI that focuses on narrow range of activities. A term that focuses on applications ranging from Robotics to Text Analysis.
Role Business role. Technical role. Both business and technical aspects.
Scope Data Science is a broad term for diverse disciplines and is not merely about developing and training models. Machine learning fits within the data science spectrum. AI is a sub-field of computer science.
AI Loosely integrated Machine learning is a sub field of AI and is tightly integrated. Integrated on various task like planning, moving around in the world, recognizing objects and sounds,
Question: Name the Technique That’s Used to Predict Categorical Responses?

“Classification technique” has been widely used in data mining fields to predict categorical responses.

Question: Define Logistical Regression?

Logistical Regression is a technique to predict the binary outcome from a linear combination of predictor variables.

Question: Explain SVM Machine Learning Algorithm in Detail

SVM, the support vector machine is a supervised machine learning algorithm which can be used for both Regression and Classification. If you have n features in your training dataset, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate. SVM uses hyper planes to separate out different classes based on the provided kernel function.

Question: Describe Decision Tree Algorithm in Detail

Decision Tree, the supervised machine learning algorithm is mainly used for the regression and classification. This decision tree algorithm breaks down a data set into smaller sub sets while at the same time an associated decision tree is incrementally developed. Decision tree can handle bot categorical and numerical data.

Question: Define Pruning in Decision Tree

The process of removing data nodes or sub nodes of a decision node is called pruning. or else, it is also called process of splitting.

Question: Define Interpolation and Extrapolation?

The process of estimating a value from 2 known values from a list of values is Interpolation. Extrapolation is approximating a value by extending a known set of values or facts.

Question: State Collaborative Filtering

Collaborative Filtering is the process used by most of the systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.

Question: Differentiate Cluster and Systematic Sampling

Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection, or cluster of elements.

Systematic sampling is a statistical technique where elements are selected from an ordered sampling frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the list; it is progressed from the top again. The best example for systematic sampling is equal probability method.

Question: What Does P-Value Indicates About the Statistical Data?

The P-Value is used to decide the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1. The statistical value of P is determined as follows:

  • If the P- Value > 0.05, it denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.
  • And if P-value <= 0.05, it indicates the strong evidence against the null hypothesis which means the null hypothesis can be rejected.
  • Finally, if the P-value=0.05, the marginal value, it indicates the possible to go either way.
Question: Define Random Forest and How Does It Work?

Random forest, the machine learning method is capable of performing both regression and classification tasks. It is also used for dimensionality reduction, treats missing values, outlier values. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

Question: Define the Term Normal Distribution

Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell shaped curve. And this type of arrangement of data is often referred as Normal Distribution

Question: Brief RNNs?

RNN stands for Recurrent Neural Networks! These types of neural networks are designed to recognize pattern from the sequence of data such as Time Series, Government organizations, Stock Market and so on!

Question: Define Reinforcement Learning

The learning of what to do and how to map situations into actions is often called as reinforcement learning. Reinforcement learning is inspired by the learning of human beings, it is based on the reward/penalty mechanism. The learner is not told which action to take, but instead must discover which action will yield the maximum

Question: Explain What Regularization

In order to prevent overfitting problems, regularization has been used. Regularization is the process of adding tuning parameter to a model to induce smoothness. This constant is often the L1 (Lasso) or L2(ridge). The model predictions should then minimize the loss function calculated on the regularized training set.

Question: Explain the Step-By-Step Process to Make a Decision Tree
  • Take the entire data set as input.
  • Look for a split that maximizes the separation of the classes. A split is any test that divides the data into two sets.
  • Apply the split to the input data (divide step).
  • Re-apply steps 1 to 2 to the divided data.
  • Stop when you meet some stopping criteria.
  • Finally, clean up the tree if you went too far doing splits. And this step is called pruning.
Question: Define Root Cause Analysis

Root Cause Analysis has been designed to analyze industrial unforeseen situations. But now, it is being widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from reoccurring.  

Question: Mention the Drawbacks of Linear Model
  • The assumption of linearity of the errors.
  • It can’t be used for count outcomes or binary outcomes
  • There are overfitting problems that it can’t solve
Question: Explain Star Scheme in Data Science

Star Scheme is a traditional database with a central data set. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields; these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve several layers of summarization to recover information faster.

Question: List Out the Types of Biases That Can Occur During Sampling
  • Selection bias
  • Under coverage bias
  • Survivorship bias
Question: Explain Cross Validation

The goal of cross-validation is to assess a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting and gain insight on how the model will generalize to an independent data set.

Common Interview Questions That Every Candidate Should Be Ready to Answer

Question: What Makes You Interested to Apply this Position?

Make sure to visit their website and learn what they’re doing before going to the interview. Fascinated by their innovation? Or are you just a big fan of their services? Let the recruiter know—it helps build rapport and shows you’re serious about considering a job with the company.

Question: What is Your Salary Expectation?

Don’t ever undervalue yourselves! Be frank! It’s important to be transparent about how you’re paid currently and what incentives you’d need to see in order to consider making a move.

Question: What Do You Think Makes a Good Leader?

Whether you’re vying for a leadership position or not, this question reveals a lot about how you work and what’s important to you in a collaborative work environment. So, answer accordingly!

Bonus Tips to Crack Your Data Scientist Interview!

Be sure to prepare yourself for the rigors of interviewing and stay sharp with the nuts-and-bolts of data science. Hope this Data Science Interview Questions tutorial guide will help you to crack the interview! The important tip, to nail a data science interview is to be confident with the answers without bluffing.

If you have any words of wisdom for data science students to ace a data science interview, share with us in comments below! Stay tuned with to get more recent updates on interview questions, job alerts and careers!


Please enter your comment!
Please enter your name here