Top 22 Data Science Interview Questions in 2022

July 4, 2022

There is nothing new in saying that data is gold. We all know this, every organization of every scale depends heavily on mining the data for generating growth and profit. And it is necessary for organizations today to keep a data science team in hand to handle complex data-related problems. 

Even on the job aspirants' side, opportunities in the field of data science are very lucrative and attract widespread attention. To help such candidates, this blog highlights the top 22 Data Science Interview questions that can come in handy while appearing for a data science job interview. 

Ques 1. What, in your opinion, accounts for Deep Learning's current surge in popularity?

Ans 1. Even though Deep Learning has been around for a while, the biggest advances in the field have only recently been made. There are two basic causes for this:  the expansion of data produced from a variety of sources and the increasing amount of hardware needed to run these models, particularly Cloud GPU services. 

Since GPUs are so much quicker than CPUs, we can now create deeper and larger deep learning models much more quickly than we could in the past.

Ques 2. Why Is Tensorflow the Most Popular Deep Learning Library?

Ans 2. Tensorflow has a faster compilation time than other Deep Learning libraries like Keras and Torch and has both C++ and Python APIs, making it simpler to work with. Tensorflow is compatible with both CPU and GPU-based computer systems.

Ques 3. What is sampling's main benefit? What procedures are employed in sampling? 

Ans 3. Sampling is the process of choosing the group from which you will actually collect data for your study. For instance, you could interview a sample of 100 students if you were examining the viewpoints of students at your university. This saves both time and resources while conducting research. 

There exist two categories of sampling techniques:

  1. Probability sampling: Random selection is a key component of probability sampling, which enables you to draw robust statistical conclusions about the entire group. Clustered sampling, Simple random sampling, and Stratified sampling are a few examples of this category. 
  2. Non-probability sampling: This technique entails non-random selection based on practicality or other factors, making it simple to gather data. For example: Quota sampling, Convenience sampling, snowball sampling, etc.

Ques 4. What does variance in data science mean?

Ans 4. Variance is a sort of inaccuracy that develops in a Data Science model as it becomes overly complicated and learns features from data, along with any noise that may be present. Even though the data and underlying patterns and trends are relatively clear to spot, this type of inaccuracy can happen if the technique used to train the model is highly sophisticated. As a result, the model is extremely sensitive and does well on the training dataset but poorly on the testing dataset and on any other type of data. Variance typically results in overfitting and poor testing accuracy.

Ques 5.  What makes supervised and unsupervised learning different?

Ans 5. The use of labeled datasets distinguishes the machine learning strategy known as supervised learning. These datasets are intended to "supervise" or "train" algorithms to correctly classify data or forecast outcomes. Labeled inputs and outputs allow the model to monitor its precision and improve over time. When using data mining, supervised learning may be divided into two categories of issues: classification and regression. 

While unsupervised learning analyses and groups unlabeled data sets using machine learning techniques. These algorithms are referred to as "unsupervised" since they identify hidden patterns in data without the assistance of a person. Clustering, association, and dimensionality reduction are the three basic tasks that unsupervised learning models are utilized for.

Ques 6. What does bias in data science mean?

Ans 6. When an algorithm is used in a data science model, bias can happen because it is unable to fully capture the underlying patterns or trends in the data. In other words, this error happens when the algorithm creates a model based on simplistic assumptions since the input is too complex for it to comprehend. As a result, underfitting results in reduced accuracy. Regression algorithms like logistic and linear can result in substantial bias.

Ques 7. What exactly is dimensionality reduction?

Ans 7. The method of reducing the number of dimensions (fields) in a dataset involves making data and its field less redundant. Dimensionality reduction involves the removal of a few fields or columns from the dataset. But this is not carried out carelessly. In this method, the dimensions or fields are only removed after confirming that the remaining data will still be sufficient to briefly describe the related problems or can represent the whole data.

Ques 8. What are the popular libraries used in Data Science?

Ans 8.  Below are the most popular libraries used in Data Science-

-TensorFlow: Provides flawless library management for parallel computing.


-SciPy: Mostly used for manipulating data, solving multidimensional programming problems, and visualizing data using graphs and charts. 

-Matplotlib: Since it is free and open-source, it can be used in place of MATLAB, producing better results and using less memory. 

-Pytorch: Best for projects using Deep Neural Networks and Machine Learning methods. 

-Pandas: ETL (Extracting, Transforming, and Loading the datasets) capabilities are implemented in commercial applications using pandas.

Ques 9. Describe the operation of a recommender system?

Ans 9. Many consumer-facing, content-driven, online platforms use a recommender system to produce user-specific recommendations from a library of available information. Based on the users' behaviors on the site, these systems produce recommendations based on what they know about their preferences.

Imagine, for instance, that we have a movie streaming service that is comparable to Netflix or Amazon Prime. A user is said to enjoy watching action and horror films if they have already watched and enjoyed films in these genres. In that situation, it would be preferable to suggest these movies to this specific user. These suggestions can also be produced using the content that individuals with similar tastes enjoy watching.

Ques 10. Describe Normal Distribution?

Ans 10. A visualization tool called data distribution can be used to examine how data is dispersed or disseminated. Different methods can be used to distribute data. For instance, there can be a bias to the left or right, or everything might be mixed up. 

Data, such as the mean, median, etc., can also be dispersed about a central value. This type of distribution, which resembles a bell-shaped curve, is unbiased in either the left or the right directions. The mean of this distribution is also the same as the median. A normal distribution is a name given to this type of distribution.

Ques 11.  What does a decision tree mean in Data Science?

Ans 11. A supervised learning approach called a decision tree is utilized for both classification and regression. As a result, the dependent variable in this situation can have either a numerical value or a categorical value. Each node in a decision tree represents a test on an attribute, each edge represents the result of that test, and each leaf node contains the class label.

Ques 12. A Boltzmann machine: What is it?

Ans 12. Boltzmann machines have a straightforward learning process that enables them to find intriguing features in the training data that represent intricate regularities. The main purpose of the Boltzmann machine is to optimize the weights and quantities for the given problem. In networks with numerous layers of feature detectors, the learning algorithm is quite sluggish. The "Restricted Boltzmann Machines" approach is quicker than the others because it only uses one layer of feature detectors.

Ques 13. What is an auto-encoder?

Ans 13. Simple learning networks called auto-encoders work to convert inputs into outputs as accurately as possible. This indicates that our goal is to have the output be as close to the input as feasible. Between the input and the output, we add a few layers, and the sizes of these layers are less than the input layer. The auto-encoder receives unlabelled input which is then encoded to reconstruct the input.

Ques 14. What is the role of the Activation Function?

Ans 14. The neural network is given non-linearity through the activation function, which enables it to learn more complex functions. Without it, the neural network could only learn linear functions, which are combinations of the data it receives in a linear fashion. An artificial neuron's activation function produces an output in response to inputs.

Ques 15. Describe what backpropagation is and how it works?

Ans 15. A multilayer neural network's training algorithm is backpropagation. By moving the error from one end of the network to all of its weights, this method enables the computation of the gradient to be done quickly. 

In Backpropagation, first training data is propagated forward then output and target are used to compute derivatives. To compute the derivative of an error with respect to output activation, backpropagation is done. Then in the end, using derivatives that have already been calculated as output weights are refreshed. 

Ques 16. How should outlier values be handled? 

Ans 16. Any graphical analysis technique, even univariate, can be used to detect outlier values. If there are only a few outlier values, each one can be evaluated separately, but if there are several, the values can be replaced with either the 99th or the 1st percentile values. 

The most typical method for handling outlier values is  to adjust the value so that it is inside a certain range or  just remove the value

Ques 17. What is Exploding and Vanishing Gradient?

Ans 17. Exploding Gradients are erroneous gradients that expand exponentially during the training of an RNN, aggregate, and lead to very large modifications to the weights of the neural network model. At their most extreme, weight values have the potential to overflow and produce NaN values. Because of exploding gradient, your model will suffer as a result, making it unable to benefit from your training set of data.

While in Vanishing Gradient your slope may get too narrow, which makes training challenging. Long training sessions, subpar performance, and low precision are the results of a vanishing gradient. 

Ques 18. What Sets Epoch, Batch, and Iteration Apart in Deep Learning?

Ans 18. 

-Epoch: One iteration of the complete dataset is represented by an epoch (everything put into the training model). 

-Batch: When we divide a dataset into multiple batches because we can't feed the complete dataset at once to the neural network, this is referred to as batching. 

-Iteration: An epoch should run 50 iterations if the input data contains 10,000 photos and the batch size is 200. (10,000 divided by 50).

Ques 19. Define Cost function?

Ans 19. The cost function, which is often known as "loss" or "error," is a metric for gauging how well your model performs. It is employed to calculate the output layer's error during backpropagation. In order to use it for the various training functions, we push that error backward through the neural network.

Ques 20. What is the repercussion of setting a wrong learning rate?

Ans 20. The model's training will advance very slowly if your learning rate is too low because we are just making minor weight updates. Before reaching the minimum point, there will be a need for several revisions. On the other hand, the loss function exhibits unwanted divergent behavior if the learning rate is set too high because weights are drastically updated. It could diverge or fail to converge respectively. 

Ques 21. How Often Does an Algorithm Need to Be Updated?

Ans 21. An algorithm has to be updated when: 

  • In order for the model to change as more data flows via the infrastructure 
  • The source of the underlying data is evolving. 
  • There is a non-stationarity instance. 
  • The algorithm is inefficient and produces inaccurate results.

Ques 22. How do you handle missing values in a dataset?

Ans 22. After finding the variables with missing values, the extent of the missing values is determined. If any patterns emerge, the analyst must focus on them since they may yield fascinating and valuable business insights. 

If no patterns are found, it is possible to ignore the missing numbers entirely or to impute them using mean or median values. a default value is being assigned, which can be the mean, minimum, or maximum value. Entering the data is crucial. 

If the variable is categorical, the default value is chosen. A default value is given to the missing variable. Give the mean value for a normal distribution if you have a distribution of data. 

Else you can also respond that you would drop the variable in place of processing the missing values if 80% of the values for a variable are missing.


Data Science is a very broad discipline that covers a variety of subjects, including Machine Learning, Deep Learning, Data Mining, Data Analysis, and Data Visualization. But most significantly, it is built on the foundation of mathematical ideas like Linear Algebra and Statistical analysis. 

So if you have an interview scheduled soon, don’t forget to brush up on your mathematical concepts with the coding and other practices.

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October 6, 2022

Comparison between Cloud-Based and On Premises GPUs

Cloud GPUs vs On Premises GPUs

Cloud GPUs are typically more powerful than on-premises GPU instances. The cost of renting a cloud GPU is generally lower than the cost of purchasing an on-premise GPU. 

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. 

Cloud GPUs are better for machine learning because they have lower latency, which is important because the time it takes a neural network to learn from data affects its accuracy. Furthermore, cloud GPUs allow users to take advantage of large-scale training datasets without having to build and maintain their own infrastructure.

On Premises GPUs are better for machine learning if you need high performance or require access to cutting-edge technologies not available in the public cloud. For example, on-premises hardware can be used for deep learning applications that require high memory bandwidth and low latency.

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.

Scalability & Flexibility: With cloud-based GPU resources, businesses can scale up or down as needed without any penalty. This ensures that they have the resources they need when demand spikes but also saves them money when there is little or no demand for those resources at all times.

Enhanced Capacity Planning Capabilities: Cloud GPU platforms allow businesses to better plan for future demands by providing estimates of how much processing power will be required in the next 12 months and beyond based on past data points such as workloads run and successes achieved with similar models/algorithms etc... 

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...) 

Reduced Total Cost Of Ownership (TCO) over time due to pay-as-you-go pricing model which allows you only spend what you actually use vs traditional software licensing models where significant upfront investments are made.

Cloud GPUs: Cloud GPUs offer significant performance benefits over on-premises GPUs. They are accessible from anywhere, and you don't need to own or manage the hardware. This makes them a great choice for data scientists who work with multiple data sets across different platforms.

Numerous Platforms Available for Use: The wide variety of available platforms (Windows, Linux) means that you can run your models using the most popular machine learning libraries and frameworks across different platforms without having to worry about compatibility issues between them.

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October 4, 2022

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.

The table below lists some of these services and compares their cost against their US equivalents. 

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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September 28, 2022

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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