# Top 30 Data Science Interview Questions & Answers.

September 12, 2022

Introduction

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
• 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.

References:

Latest Blogs
October 18, 2022

### A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

• Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

• MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
• CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
• CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
• Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

• Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

October 18, 2022

### Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

## The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

• Highly calibrated cameras that take a photograph of the image from various angles.
• Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

• Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

• Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

• Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

• Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

• Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

• 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
• It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
• They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
• It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
• It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

October 18, 2022

### A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

October 13, 2022

### GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

• The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
• Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
• GAUDI also removes GANs pathologies like mode collapse and improved GAN.
• GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

• Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
• This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
• The scenes are thus synthesized by interpolation within the hidden space.
• The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
• A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.