Top 30 Data Science Interview Questions & Answers.

September 12, 2022


Data Science is one of the fastest-growing jobs in today’s world. Data science is an interdisciplinary field that combines the fields like statistics, mathematics, computer science, domain knowledge, artificial intelligence, machine learning, etc. the uses of particular techniques and analytical methods to extract information from data used in strategic planning, decision-making, etc. is known as data science. Data science is the practice of analyzing data to get meaningful insights.

In this article, we are briefly going to cover the top 30 Data science interview questions & answers.

  1. What are the differences between supervised and unsupervised learning?

Supervised learning - It uses labeled or known data. It is a feedback mechanism to train instances. Most used supervised machine learning algorithms are logistic regression, decision trees, and support vector machine

Unsupervised learning - Uses unlabeled data as input. It has no feedback mechanism. The most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm

  1. Explain the steps in making a decision tree.

  • Take the entire data set as input
  • Calculate the entropy of the target variable, as well as the predictor attributes
  • Calculate your information gain of all attributes (we gain information on sorting different objects from each other)
  • Choose the attribute with the highest information gain as the root node 
  • Repeat the same procedure on every branch until the decision node of each branch is finalized.


  1. How can you select k for k-means? 

We use the elbow method to select k for k-means clustering. The idea of the elbow method is to run k-means clustering on the data set where 'k' is the number of clusters.

Within the sum of squares (WSS), it is defined as the sum of the squared distance between each member of the cluster and its centroid. 


  1. What is the significance of the p-value?

p-value typically ≤ 0.05- This indicates strong evidence against the null hypothesis; so you reject the null hypothesis.

p-value typically > 0.05- This indicates weak evidence against the null hypothesis, so you accept the null hypothesis. 

p-value at cutoff 0.05 - This is considered to be marginal, meaning it could go either way.


  1. How can time-series data be declared as stationery?

Time series is considered stationary when the Mean and Variance of the data are constant with the time. In the real-world scenario, we generally  do not find stationary time-series data


  1. How can you calculate accuracy using a confusion matrix?

the formula for accuracy from the confusion matrix is:

Accuracy = (True Positive + True Negative) / Total Observations

= (262 + 347) / 650

= 609 / 650

= 0.93

As a result, we get an accuracy of 93 percent.


  1.  'People who bought this also bought…' recommendations seen on Amazon are a result of which algorithm?

The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc. 

The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.


  1. Which of the following machine learning algorithms can be used for inputting missing values of both categorical and continuous variables?
  • K-means clustering
  • Linear regression 
  • K-NN (k-nearest neighbor)
  • Decision trees 

The K nearest neighbor algorithm can be used because it can compute the nearest neighbor and if it doesn't have a value, it just computes the nearest neighbor based on all the other features. 

  1. What is the ROC curve?

The graph between the True Positive Rate on the y-axis and the False Positive Rate on the x-axis is called the ROC curve and is used in binary classification.

The False Positive Rate (FPR) is calculated by taking the ratio between False Positives and the total number of negative samples, and the True Positive Rate (TPR) is calculated by taking the ratio between True Positives and the total number of positive samples.

  1.  What is a Confusion Matrix?

The Confusion Matrix is the summary of prediction results of a particular problem. It is a table that is used to describe the performance of the model. The Confusion Matrix is an n*n matrix that evaluates the performance of the classification model.

  1.  How can one assess the Normal Distribution?

There are several methods to check the Normality of a Distribution. Some of the methods are:

  • Histogram
  • Kernel Density Estimation (KDE)
  • Q_Q (quantile-quantile) Plot
  • Skewness
  • Kurtosis


  1.  What do you understand about Random Forest?

Random Forest is one of the widely accepted machine learning algorithms classified under the supervised learning technique. The forest is built by combining multiple classifiers to bring solutions to complicated problems, thus, helping to improve the performance of a model. With a large number of forests, the risk of overfitting is avoided and it also leads to increased accuracy. 

  1.  Explain the term overfitting.

Overfitting, in simple terms, occurs when a statistical model is overfed with data. When such an event happens, the model starts training itself from the noise and inaccurate data entries. It is similar to trying to fit in an oversized cloth. 


  1.  How to avoid overfitting?

There are many ways to avoid the overfitting of statistical models. The most common ways are:

  • Cross-validation
  • Train with more data to help the model detect the right signals.
  • By removing irrelevant features from the model.
  • Overfitting can also be avoided by preventing it at an early stage. In this, one needs to measure each iteration at all levels.
  • Through regularization, overfitting can be avoided. In this solution, techniques to artificially force the model to be made simpler are used. 
  • Ensembling is another way to avoid overfitting data. 


  1.  Define what is bias?

In statistics, bias can be defined as a wrong estimation of a parameter. In such a case, the results of the expected value differ from what is estimated. In bias, results can be either underestimated or overestimated. 


  1.  List the types of biases that can happen during the sampling process

Some of the biases that occur during the sampling process are:

  • Selection Bias
  • Self-Selection Bias
  • Observer Bias
  • Survivorship Bias
  • Pre-Screening or Advertising Bias
  • Undercoverage Bias


  1. What do you mean by prior probability and likelihood?

Prior probability can be defined as a probability of an event that is calculated before the collection of new data. In prior probability, the probability is computed before taking evidence into account, expressing one’s belief. 

The likelihood, on the other hand, is the probability of attaining results for data given a particular parameter. 


  1.  What is backpropagation?

backpropagation is a short form for backward propagation of errors and is also known as backdrop or BP. Backpropagation is an algorithm that works to tune the weights of a neural net using the technique of delta rule or gradient descent. By reducing the error rates, backpropagation helps to increase the generalization of the model. 


  1.  Explain Deep learning in your own words

Deep Learning comes under the rubric of Machine Learning. It is a system that is used to create a model that will predict and solve problems using a handful of lines of coding. It is a neural network that is based on the functioning and structuring of a brain. Using its unique aspect of efficiency and accuracy, the systems of Deep Learning can even surpass the cognitive powers of the human brain. 


  1.  Define collaborative filtering

Collaborative Filtering is a technique that is used to filter out items using the interactions and collection of data from other users. 


  1.  What do you mean by recommender systems?

Recommender Systems can be defined as systems that are used to predict and recommend things that a user might be interested in based on various factors. These systems can anticipate the product a user most likely be interested in or might purchase based on their burning history. Some companies that use recommender systems are Netflix and Amazon. 

  1.  What do you understand about the true-positive rate and false-positive rate?

TRUE-POSITIVE RATE - The true-positive rate gives the proportion of correct predictions of the positive class. It is also used to measure the percentage of actual positives that are accurately verified.

FALSE-POSITIVE RATE -  The false-positive rate gives the proportion of incorrect predictions of the positive class. A false positive determine something is true when that is initially false.


  1.  How is Data Science different from traditional application programming?

The primary and vital difference between Data Science and traditional application programming is that in traditional programming, one has to create rules to translate the input to output. In Data Science, the rules are automatically produced from the data.


  1.  Why is Python used for Data Cleaning in DS?

Data Scientists and technical analysts must convert a huge amount of data into effective ones. Data Cleaning includes removing malware records, outliners, inconsistent values, redundant formatting, etc. Matplotlib, Pandas, etc are the most used Python Data Cleaners.


  1.  What is variance in Data Science?

Variance is the value that depicts the individual figures in a set of data that distributes themselves about the mean and describes the difference of each value from the mean value. Data Scientists use variance to understand the distribution of a data set.

  1.  What is pruning in a decision tree algorithm?

In Data Science and Machine Learning, Pruning is a technique that is related to decision trees. Pruning simplifies the decision tree by reducing the rules. Pruning helps to avoid complexity and improves accuracy. Reduced error Pruning, cost complexity pruning, etc. are the different types of Pruning.


  1. What is entropy in a decision tree algorithm?

Entropy is the measure of randomness or disorder in the group of observations. It also determines how a decision tree switches to split data. Entropy is also used to check the homogeneity of the given data. If the entropy is zero, then the sample of data is entirely homogeneous, and if the entropy is one, then it indicates that the sample is equally divided.

  1. What information is gained in a decision tree algorithm?

Information gain is the expected reduction in entropy. Information gain decides the building of the tree. Information Gain makes the decision tree smarter. Information gain includes parent node R and a set E of K training examples. It calculates the difference between entropy before and after the split.

  1.  What is k-fold cross-validation in machine learning?

The k-fold cross-validation is a procedure used to estimate the model's skill in new data. In k-fold cross-validation, every observation from the original dataset may appear in the training and testing set. K-fold cross-validation estimates the accuracy but does not help you to improve the accuracy.

  1.  What is an RNN (recurrent neural network)?

RNN is an algorithm that uses sequential data. RNN is used in language translation, voice recognition, image capture, etc. There are different types of RNN networks such as one-to-one, one-to-many, many-to-one, and many-to-many. RNN is used in Google’s Voice search and Apple’s Siri.




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Cloud platforms offer fast access to high performance compute and deep learning algorithms, which makes it simpler to start using machine learning models and get early insights into your data. 

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Cloud GPUs: Cloud GPUs are remote data centers where you can rent unused GPU resources. This allows you to run your models on a massive scale, without having to install and manage a local machine learning cluster.

Lower TCO: Cloud GPUs require no upfront investment, making them ideal for companies that are looking to reduce their overall capital expenses. Furthermore, the cost of maintenance and upgrades is also low since it takes place in the cloud rather than on-premises.

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Security & Compliance : Since cloud GPUs reside in a remote datacenter separate from your business' core systems, you are ensured peace of mind when it comes to security and compliance matters (eigenvector scanning / firewalls / SELinux etc...) 

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Impact of the Strong Dollar: Cloud Costs Increasing, Be Indian Buy Indian

Indian SMEs and startups are feeling the effects of the high dollar. These businesses use hyperscalers(MNC Cloud) who cannot modify their rates to account for the changing exchange rate. For certain companies, even a little shift in the currency rate may have a significant effect on their bottom line. Did you know, when the INR-USD exchange rate moved from 60 to 70 in December 2015, it had an impact of around 20% on Digital Innovation?

As the rupee is inching closer to 82 per dollar, the strong dollar has directly impacted the costs of cloud services for Indian businesses. The high cost of storage and computing power, along with bandwidth charges from overseas vendors, has led to a huge increase in the effective rate of these services. This is especially true for startups and SMEs that rely on cloud computing to store and process user data. With the strong dollar continuing to impact the cost of cloud services, it is essential for Indian companies to evaluate their options and adopt local alternatives wherever possible. This blog post will discuss how the strong dollar impacts cloud costs, as well as potential Indian alternatives you can explore in response to this global economic trend. 

What is a Strong Dollar?

A strong US dollar($) is a term used to describe a situation where a US’s currency has appreciated in value compared to other major currencies. This can be due to a variety of factors, including interest rate changes, a country’s current account deficit, and investor sentiment. When a currency appreciates, it means that it is worth more. A strong dollar makes imports more expensive, while making exports cheaper. Strong dollars have been a growing trend in the past couple of years. As the US Federal Reserve continues to hike interest rates, the dollar strengthens further. The rising value of the dollar means that the cost of cloud services, especially from hyperscalers based in the US, will rise as well. 

Increase in Cloud Costs Due to Strong Dollar

Cloud services are essential for modern businesses, as they provide easy access to software, storage, and computing resources. Cloud services are delivered over the internet and are typically charged on a per-use basis. This makes them incredibly convenient for businesses, as they can pay for only the resources they actually use. Cloud computing allows businesses to scale their resources up or down, depending on their current business needs. This makes it suitable for startups, where demand is uncertain, or large enterprises with global operations. Cloud computing is also inherently scalable and allows businesses to quickly react to changing business needs. Cloud computing is a very competitive industry and providers offer attractive prices to attract customers. However, these prices have been impacted by the strong dollar. The dollar has strengthened by 15-20% against the Indian rupee in the last few years. As a result, the costs of services such as storage and bandwidth have increased for Indian companies. Vendors charge their Indian customers in Indian rupees, taking into account the exchange rate. This has resulted in a significant rise in the costs of these services for Indian companies.

Why are Cloud Services Becoming More Expensive?

Cloud services are priced in US dollars. When the dollar is strong, the effective price of services will be higher in Indian rupees, as the cost is not re-adjusted. There are a couple of reasons for this price discrepancy. First, Indian customers will have to pay the same prices as American customers, despite a weaker Indian rupee. Second, vendors have to ensure that they make a profit.

Possible Indian Alternatives to Cloud Services

If you're looking for a cost-effective substitute for services provided by the U.S.-based suppliers, consider E2E Cloud, an Indian cloud service provider. When it comes to cloud services, E2E Cloud provides everything that startups and SMEs could possibly need.

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According to the data in the table above, Indian E2E Cloud Services are much cheaper than their American equivalents. The difference in price between some of these options is substantial. When compared to the prices charged by suppliers in the United States, E2E Cloud's bandwidth costs are surprisingly low. Although not all E2E Cloud services will be noticeably less expensive. Using Indian services, however, has an additional, crucial perk: data sovereignty.


The price of cloud services will rise as the US Dollar appreciates. Indian businesses will need to find ways to counteract the strong dollar's impact on their bottom lines. To do this, one must use E2E Cloud. The availability of E2E Cloud services in INR currency is a bonus on top of the already substantial cost savings. An effective protection against the negative effects of a strong dollar.

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Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.


In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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