Posted on : 19 Mar, 2021, 04:55:38 PM

Top 50 Data Science Interview Questions And Answers

Top 50 Data Science Interview Questions And Answers

Data Scientist is one of the most popular and prominent careers globally. There is no surprise that the new era is all about Artificial Intelligence, Machine learning, and Data Science. Due to the great demand and low availability of data scientists, multinational companies are ready to pay the highest perks to data science experts and professionals.


If you are moving towards the path of becoming a Data Scientist, you must be qualified and fully prepared to impress the interviewer or prospective employers with your knowledge. In this guide, Wissenhive includes a list of the Data Science Interview questions that are most frequently asked in job interviews so candidates can frame their answers.

1. What is Data Science?

Data Science refers to an interdisciplinary area that utilizes a combination of scientific methods, algorithms, tools, processes, systems, and machine learning techniques to extract knowledge to find the hidden pattern from given raw data that contains structured, semi-structured, and unstructured data. 

Data Science uses the theories and techniques from various fields such as Statistics, Information Science, Mathematics, Computer Science, and Domain knowledge to lead the information and data.

2. What is the main difference between supervised and unsupervised?

Supervised and unsupervised learning are the two different types of machine learning techniques, which allow building from basic to advanced models and solve different kinds of problems.

Supervised Learning Unsupervised Learning
Works with data that includes both expected output and inputs such as the labeled data Work with data that includes no mapping from outputs to inputs, such as unlabeled data.
Used in creating models that can be employed to classify and predict things Used in extracting meaningful information from the large volumes of data
Used commonly supervised learning algorithms such as decision tree, linear regression, etc Used commonly Unsupervised learning algorithms such as Apriori algorithm,  K-means clustering, etc


3. What do you mean by a Decision Tree?

A Decision Tree refers to the decision support tool that uses the tree-like model’s decisions and possible consequences, including resource cost, changing event outcomes, and utility. It is a flow chart to visualize the process of decision-making by planning out various courses of action and their potential outcomes.

Decision trees mostly used in decision analytics, operations research, identifying the strategies to reach goals, and machine learning.

4. Why is it necessary to make a Decision Tree?

  • Decision trees are flexible.
  • Effectively communicates complex processes.
  • Clarify choices, objectives, risks, and gains
  • Focused on probability and data, no biases
  • Enable to flesh out ideas before sinking valuable resources and time

5. What are the steps involved in creating a Decision Tree?

  • Take the entire set of data as input.
  • Calculating entropy of the predictor attributes and the target variable
  • It gains information from all the attributes while sorting various objects from each other.
  • Choosing the attribute as the root node with the highest information gained 
  • Repetition of all the procedures on every branch until every decision note branch is finalized.

6. What is pruning in a decision tree?

Pruning in Decision Tree algorithm refers to a data compression technique in search algorithms and machine learning that diminishes the size volume of a decision tree by removing certain sections of the tree that are redundant and non-critical to classify instances. It helps in reducing the complexity and improving predictive accuracy by reduction of overfitting.

7. What is entropy in a decision tree?

In the Decision Tree, entropy refers to a measure of randomness and impurity. It controls how the Decision tree algorithms decide to split the data. The entropy of the provided dataset tells us how impure or pure the value of the dataset is. It affects how the Decision tree draws its boundaries. In simple terms, it describes the variance in the dataset.

8. What do you mean by Linear Regression?

Linear regression helps in building the understanding of the linear relationship between the independent variables and the dependent. It is a supervised learning algorithm that assists in finding a linear relationship between the independent and the dependent variables. 

In linear regression, understanding the behavior of how independent variable changes w.r.t the dependent variable. Single linear regression is an independent variable, but if they are more than one in numbers, it becomes multiple linear regression.

9. What are the advantages and disadvantages of Linear Regression?


  • Simple implementation
  • Performance-based on linearly separable datasets
  • Regularization can reduce overfitting.


  • Prone to underfitting
  • Sensitive to outliers
  • Assumes that the data is independent

10. What would be the assumptions required for Linear Regression?

There are various required assumptions for linear regression. Let’s cover all the main assumptions one by one.

  • The regression model can be expressed in a linear way.
  • The expected mean error of the regression model is zero.
  • The variance of errors is constant (homoscedastic)
  • The observation and error are independent (no autocorrelation). Not relevant for cross-sectional data.
  • The independent variable is usually distributed.
  • The errors need to be approximately normally disturbed.
  • The relationship between dependent variables and independent variables is linear.

11. What do you mean by logistic regression?

The Logistic regression or logit model measures is an appropriate analysis regression to manage when the dependent variable is binary (dichotomous). It refers to predictive analysis, which is used to collect and describe data with the explanation of the relationship between one or more ordinal, nominal interval and one dependent binary variable or ratio-level independent variables.

12. What are the advantages of logistic regression?

  • Easy to interpret and implement
  • No assumptions about distributions
  • Natural probabilistic view of class predictions
  • Easily extend to multiple classes
  • Provides measures with appropriate predictor and direction
  • Can easily classifying unknown records
  • Interpret model coefficients as indicators 
  • Provides accuracy of various simple data sets

13. What are the disadvantages of logistic regression?

  • Lesser number of observation
  • Constructs linear boundaries
  • It only used to predict discrete functions.
  • Tough to obtain complex relationships
  • Assumption of linearity between the independent and the dependent variables 
  • Requires no or average multicollinearity between independent variables
  • Dependent and independent variables are related linearly.

14. What is bias in Data Science?

In the Data Science model, Bias refers to the type of error that occurs due to weak algorithms that are unable to capture new trends that exist and underlying patterns in the data. In other words, Bias occurs when algorithms are unable to understand complicated data and end up building a model based on simple assumptions, which becomes the reason for lower accuracy underfitting. Some algorithms that can lead to high bias are logistic regression, linear regression, etc.

15. What are the types of biases that occur during sampling?

There are six different types of biases that might occur during the sampling process, and those are 

  • Self-selection
  • Non-response
  • Undercoverage
  • Survivorship
  • Pre-screening or advertising
  • Healthy user

16. What do you mean by Random Decision Forest?

Random decision forests or random forests are built up of several decision trees. It is an ensemble learning method for many tasks such as classification regression that builds many decision trees at training time and outputs the class on average prediction (regression) or the mode of the classes (classification) of the individual trees.

If the data split into different packages to make a decision tree in each of the data groups, the random forest brings all the trees together.

17. Explain the steps to build a Random Forest Model?

There are the five basic steps to build a random forest model are 

  • Select any random K feature from a total M features where M >> K
  • From the K features, use split point by calculating the node D
  • Best Split divide nodes into daughter nodes 
  • Repeat two and three steps until leaf nodes are finalized 
  • Repeat steps one to four to build a Random Forest for 'n' times to create 'n' number of trees

18. What do you understand by dimensionality reduction?

Dimension reduction and Dimensionality reduction is a process of data transformation from low-dimensional space into high dimensional space. It is a common field that deals with a huge number of observations and variables such as speech recognition, bioinformatics, signal processing, and neuroinformatics.

19. What is variance in Data Science?

In the Data Science model, Variance refers to an error type that makes the model end up being too complex and noisy in data. Variance error occurs if algorithms used to build the model have high complexity, even through trends that are discovered and underlying patterns in the data.

It makes model sensitivity that performs poorly on the testing database and provides inaccuracy in testing and finishing in overfitting.

20. What is Power Analysis?

The power analysis is one of the integral components of the experimental designs, which helps in determining the raw sample size for a research study that requires finding out the effect of a given size from a cause with a particular level of assurance. Power analysis allows a specific probability in a constraint sample size.

21. What is Univariate Analysis?

Univariate analysis is a primary form of statistical data analysis methods. Data that contains only one variable and does not deal with the effect relationship or a cause then Univariate analysis method is used. The key purpose of Univariate analysis is to describe data within data by finding patterns. This technique is done by looking into

  • Mean
  • Median
  • Mode
  • Variance
  • Dispersion
  • Standard deviation 
  • Range

22. What Are The Ways To Conduct Univariate Analysis?

There are several ways to conduct univariate analysis, which mostly descriptive in nature are

  • Frequency Distribution Tables
  • Frequency Polygons
  • Histograms
  • Bar Charts
  • Pie Charts

23. What is Bivariate Analysis?

Bivariate analysis is a better analytical technique than Univariate analysis. The simplest form of statistical (quantitative) analysis involves two variables analysis mostly represented as X and Y to manage the empirical relationship. It is helpful in testing hypotheses of association. This technique is done by looking into

  • Numerical & Numerical
  • Categorical & Categorical
  • Numerical & Categorical

Numerical & Numerical


Categorical & Categorical

Numerical & Categorical

24. What are the ways to conduct Bivariate Analysis?

There are multiple ways to conduct Bivariate analysis, which mostly descriptive in nature are

  • Correlation coefficients
  • Regression analysis
    • Linear regression
    • Logistic regression
    • Simple regression
    • Binary regression
    • Polynomial regression
    • Binomial regression
    • General linear model
    • Discrete choice

25. What is Multivariate Analysis?

Multivariate analysis refers to a complex statistical analysis technique, which involves research and observation to use when there are more than two statistical outcome variables in the dataset at a time. It addresses the situation where various measurements are involved in every experimental unit, their structure information, and the relation between all the measurements.

26. What are the ways to conduct Multivariate Analysis?

There are several ways to conduct multivariate analysis, which mostly descriptive in nature are

  • Factor Analysis
  • Redundancy Analysis
  • Cluster Analysis
  • Principal Component Analysis
  • Variance Analysis
  • Multidimensional Scaling
  • Discriminant Analysis

27. What Is Collaborative Filtering?

Collaborating filtering or social filtering refers to a predictive process that happens behind recommendation engines to analyze actual and real information about the users with a similar feeling to estimate the probability that the targeted audience will enjoy in the form of video, product, or book.

It searches for the correct pattern by collaborating with multiple data sources, viewpoints, and various agents.

28. What are the two selection techniques used to select the right variables?

There are two specific techniques for the selection. Let’s cover one by one all the methods.

  • Filter Methods
    • Chi-Square
    • Linear discrimination analysis
    • ANOVA
  • Wrapper Methods
    • Recursive Feature Elimination
    • Forward Selection
    • Backward Selection

29. What are the steps to maintain a Deployed Model?

There are four main steps included in maintaining the quality and accuracy of the deployed model, and those steps include

  • Monitoring
  • Evaluating
  • Comparison
  • Rebuilding

30. What is the main difference between Data Science and Big Data?

Factors Data Science Big Data
Concept Analyzing data Handling large data
Responsibility Understand pattern within data and make decisions Process huge volume of data and generate insight
  • Sales
  • Image recognition
  • Advertisement
  • Risk analytics
  • Ecommerce
  • Security services
  • Telecommunications
  • SAS
  • R
  • Python
  • Hadoop
  • Spark
  • Flink


31. Why do you need to perform resampling?

  • Resampling is an active process of gathering information and observation with the plan of estimating a population variable.
  • It estimates the accuracy of raw and sample statistics by randomly drawing with the usage subsets of accessible data.
  • Resampling is an economic methodology that uses a data sample to enhance the quantification and accuracy of a population parameter’s uncertainty.
  • Substitute labels on data when performing necessary required tests
  • Resampling methods make use of nested resampling methods.
  • Validates models by using random subsets

32. What are the libraries in Python used for scientific computation and data analysis?

  • SciPy
  • Seaborn
  • Pandas
  • SciKit
  • Matplotlib
  • NumPy

33. What are the basic queries that record all orders with customer details and information?

Usually, it contains order and customer tables that includes the following columns, and those are

  • Order Table
  • Order ID
  • Customer Profile ID
  • Order Number
  • Total Amount 
  • Customer Table
  • Identity Document
  • First Name
  • Middle Name
  • Last Name
  • Country
  • City
  • SQL Query

34. What is a Confusion Matrix?

Confusion matrix in the area of machine learning and the problem of statistical classifications that is also known as an error matrix refers to a specific table layout that provides visualization performance of algorithms. It compares the predicted value with the actual targeted value by the machine learning model and gives a holistic representation of the performance of the classification model with finding error catcher.

35. How to measure the accuracy of a binary classification algorithm using a confusion matrix?

There are only two labels in binary classification algorithms, and those are True and False. Before measuring the accuracy, understanding a few key terms are important.

  • True positive (Nos. of observation classified as correctly True)
  • False positives (Nos. of observation classified as incorrectly True)
  • True negatives (Nos. of observation classified as correctly False)
  • False negatives (Nos. of observation classified as incorrectly False)

To calculate the accurate solution, we will divide the sum of classified observations correctly by the nos. of total observations.

36. What is the Naive Bayes algorithm?

The Naive Bayes Algorithm or naive refers to a probabilistic machine learning algorithm based on the Bayes Theorem, which describes the possibility of an event and classification tasks. Naive provides a strong assumption that is unrealistic for actual data, but it is very effective on complex problems.

37. What is the A/B Testing?

A/B testing, also known as bucket testing or split testing which used for conducting random experiments and comparing two different variables. This testing method’s main objective is to discover changes to a web page to increase or enhance the best outcome of the strategy.

38. What is Ensemble Learning?

Ensemble learning is a processor by which various models, such as experts and classifiers, generate and combine strategically to solve particular computational intelligence problems. It is used to improve the entire model’s performance by reducing the likelihood of poor unwanted selection.

39. What are the different types of ensemble Learning Algorithms?

  • Bagging or Bootstrap aggregating 
  • Boosting
  • AdaBoost
  • Stacked Generalization
  • Mixtures of Experts
  • Bayes optimal classifier
  • Bayesian model averaging
  • Bayesian model combination
  • Bucket of models
  • Stacking

40. What is Data Science and Artificial Intelligence?

Factors Data Science Artificial Intelligence
Scope Involves various underlying data operations Limited to the implementation of ML algorithms
Type of Data Structural and Unstructual Standardized in the form of vectors and embeddings
  • R
  • SAS
  • Python
  • SPSS
  • Keras
  • Scikit learn
  • TensorFlow
  • Scikit-learn
  • Kaffe
  • PyTorch
  • TensorFlow
  • Shogun
  • Mahout
  • Advertising
  • Marketing
  • Internet Search Engines
  • Manufacturing
  • Automation
  • Robotics
  • Transport
  • Healthcare


41. What do you mean by cross-validation?

Cross-validation is an advanced validation technique that evaluates how statistical analysis outcomes will generalize and work for an independent dataset. This technique is used in the background, where the main purpose is to forecast and estimate the model’s accuracy.

42. Explain the steps used for a data analytics project?

Some of the important steps that are used and involved in analytics projects are

  • Strong understanding of the business problems 
  • A project exploring the data and studying it in depth
  • It prepares the data for modeling by searching and finding transforming variables and missing values.
  • Start operating the model and analyze the result from big data.
  • Validating the model with the new dataset.
  • Implementing the model and tracking the result to evaluate the entire performance of the model for a particular period

43. How to select important variables while working on a data set?

To select and find important variables while working with data sets includes. 


  • It selects important variables by removing the correlated variables.
  • Usage of linear regression and selecting variables, which depend on p values
  • Use the stepwise, backward, and forward selection. 
  • Use Random Forest, Xgboost, and plot variable important charts.
  • Measure information and detail gain for the provided set of features and select top n features accordingly.

44. What do you understand about TF/IDF vectorization?

TF-IDF refers to a statistical measure that stands for Term Frequency/Inverse Document Frequency that evaluates how relevant a collection of documents is. It can be done by multiplying two metrics: word count appeared in the document and inverse document frequency of the word. TF/IDF is used often in information retrieval and text mining.

45. What is a Boltzmann Machine?

The Boltzmann machine is one of the simple learning algorithms that help in discovering those new features that represent complex regularities in the data training. Algorithms will permit you to optimize the weights fully, and the amount qualifies for the given problems.

46. What is P-Value?

The p-value refers to the possibility of obtaining test solutions at least as extreme as the observed outcomes under the assumption of a statistical null hypothesis test is correct. It is used as an alternate option to reject points to give the smallest level of importance at which the null hypothesis would be declined.

47. What do you mean by root cause analysis?

Root cause analysis is a problem-solving method used for identifying, isolating, and recognizing the root cause of problems or faults. It initially develops analyzing industrial accidents, but it’s is also used in other areas, and those industries are telecommunications, accident analysis, IT operations, industrial process control, healthcare industry, Medicine, etc.,

48. What do you mean by deep learning?

Deep learning is one of the sub-branch of machine learning. It has a unique aspect that brings efficiency and accuracy to the table while trained with vast amounts of data as the system is advanced and can match the human brain’s cognitive powers. 

Deep learning algorithms train advanced machines and devices by learning from example. It includes industries such as eCommerce, health care, advertising, and entertainment that commonly use deep learning.

49. What are the various types of deep learning frameworks?

  • TensorFlow
  • Chainer
  • PyTorch
  • ONNX
  • Keras
  • DL4J
  • Sonnet
  • Gluon
  • MXNet
  • Swift for TensorFlow

50. What Is the Difference between Data Science And Data Analytics?

Factors Data Analytics Data Science
  • BI Tools
  • Solid Programming Skills
  • Intermediate Statistics
  • Regular Expression (SQL)
  • Data Modelling
  • Predictive Analytics
  • Advanced Statistics
  • Programming/Engineering
Scope Micro Macro
  • Data Visualization Techniques
  • Designing Principles
  • Big Data - Mostly Structured
  • Search Engine Exploration
  • Machine Learning
  • Artificial Intelligence
  • Big Data - Often Unstructured
Goals Using existed information to uncover actionable data Discovering new questions to drive innovation


Here, we are done with the list of top 50 Data Science interview questions that the interviewer frequently asks. Fulfilling necessity criteria is important, but reviewing interview questions and answers in advance is the best way to calm the candidate’s nerves and boost confidence before going for an interview to give your best.


Let us know if you find difficulty understanding any question or any query that arises that is not mentioned in this article related to Data Science interview questions in the comment box.

Are you one of those who are looking for the best ways to stand apart from the crowded competition? Join Wissenhive to enhance Data Science skills with advanced data science certification training courses.


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