Data Science Interview Questions and Answers

The following are some of the important skills to possess which will come handy when performing data analysis using Python.

• You should have good understanding of the built-in data types especially lists, dictionaries, tuples, and sets.
• Should have knowledge in N-dimensional NumPy Arrays.
• Should have knowledge in Pandas dataframes.
• Ability to perform element-wise vector and matrix operations on NumPy arrays.
• You should use the Anaconda distribution and the conda package manager.
• Familiar with Scikit-learn.
• Ability to write efficient list comprehensions instead of traditional for loops.
• Ability to write small, clean functions (important for any developer), preferably pure functions that don’t alter objects.
• Should know how to profile the performance of a Python script and how to optimize bottlenecks.

It’s Python because it has Pandas library that makes our life easy to use data structures and high performance data analysis tools.

Classification technique is used widely in mining for classifying data sets.

Supervised Machine learning:

Supervised machine learning requires training labelled data.

Unsupervised Machine learning:

Unsupervised machine learning doesn’t required labelled data.

“Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the larger data set being examined.” 

“A type I error occurs when the null hypothesis is true, but is rejected. A type II error occurs when the null hypothesis is false, but erroneously fails to be rejected.” 

• A linear regression is a good tool for quick predictive analysis: for example, the price of a house depends on a myriad of factors, such as its size or its location.

• In order to see the relationship between these variables, we need to build a linear regression, which predicts the line of best fit between them and can help conclude whether or not these two factors have a positive or negative relationship. 

“Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training.” At an extreme, the values of weights can become so large as to overflow and result in NaN values.

This has the effect of your model being unstable and unable to learn from your training data.

Gradient:

Gradient is the direction and magnitude calculated during training of a neural network that is used to update the network weights in the right direction and by the right amount.

Selection bias occurs when sample obtained is not representative of the population intended to be analyzed.

There are four types of kernels in SVM.

1. Linear Kernel
2. Polynomial Kernel
3. Radial basis Kernel
4. Sigmoid Kernel

When we remove sub-nodes of a decision node, this process is called pruning or opposite process of splitting.

Ensemble is the art of combining diverse set of learners(Individual models) together to improvise on the stability and predictive power of the model.

• Random forest is a versatile machine learning method capable of performing both regression and classification tasks.

• It is also used for dimension reduction, treats missing values, outlier values. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

• In Random Forest, we grow multiple trees as opposed to a single tree. To classify a new object based on attributes, each tree gives a classification. The forest chooses the classification having the most votes(Over all the trees in the forest) and in case of regression, it takes the average of outputs by different trees.

Data Modeling: It can be considered as the first step towards the design of a database. Data modeling creates a conceptual model based on the relationship between various data models. The process involves moving from the conceptual stage to the logical model to the physical schema. It involves the systematic method of applying data modeling techniques.

Database Design: This is the process of designing the database. The database design creates an output which is a detailed data model of the database. It includes the detailed logical model of a database but it can also include physical design choices and storage parameters.

• Logistic Regression often referred as logic model is a technique to predict the binary outcome from a linear combination of predictor variables.

• For example, if you want to predict whether a particular team win the IPL match or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the number of runs scored and the number of high wickets achieved by the team.

• A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product.

• Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

• It is a statistical hypothesis testing for a randomized experiment with two variables A and B.

• The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest.

• A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads.

• An example of this could be identifying the click-through rate for a FB ad.

• Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.).

• Sensitivity is nothing but “Predicted True events/ Total events”. True events here are the events which were true and model also predicted them as true.

• Calculation of seasonality is pretty straightforward.

• Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable)

• There is a linear relationship between the dependent variables and the regressors, meaning the model you are creating actually fits the data

• The errors or residuals of the data are normally distributed and independent from each other.

• There is minimal multicollinearity between explanatory variables.

• Homoscedasticity – This means the variance around the regression line is the same for all values of the predictor variable.

Basically, an interaction is when the effect of one factor (input variable) on the dependent variable (output variable) differs among levels of another factor.

Instead of using k-fold cross-validation, you should be aware of the fact that a time series is not randomly distributed data – It is inherently ordered by chronological order.

In case of time series data, you should use techniques like forward chaining – Where you will prepare model based on past data and then look at forward-facing data.

• fold 1: training[1], test[2]

• fold 1: training[1 2], test[3]

• fold 1: training[1 2 3], test[4]

• fold 1: training[1 2 3 4], test[5]

• Deep learning is subfield of machine learning inspired by structure and function of brain called artificial neural network.

• We have a lot numbers of algorithms under machine learning like Linear regression, SVM, Neural network etc and deep learning is just an extention of Neural networks.

• In neural nets we consider small number of hidden layers but when it comes to deep learning algorithms we consider a huge number of hidden layers to better understand the input/output relationship.

Machine learning:

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorised in following three categories.

1. Supervised Machine Learning
2. Unsupervised Machine Learning
3. Reinforcement Learning

Deep learning:

Deep Learning is a sub field of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

• These are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can be referred to as univariate analysis.

• If the analysis attempts to understand the difference between 2 variables at time as in a scatterplot, then it is referred to as bivariate analysis. For example, analysing the volume of sale and a spending can be considered as an example of bivariate analysis.

• Analysis that deals with the study of more than two variables to understand the effect of variables on the responses is referred to as multivariate analysis.

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

An experimental design technique for determining the effect of a given sample size.

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.

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

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

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

• Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching.

• Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.

• A Validation set can be considered as a part of the training set as it is used for parameter selection and to avoid overfitting of the model being built.

• On the other hand, a Test Set is used for testing or evaluating the performance of a trained machine learning model.

• In simple terms, the differences can be summarized as; training set is to fit the parameters i.e. weights and test set is to assess the performance of the model i.e. evaluating the predictive power and generalization.

P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1.

• P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.

• P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.

• P-value=0.05is the marginal value indicating it is possible to go either way.

No, they do not because in some cases it reaches a local minima or a local optima point. You don’t reach the global optima point. It depends on the data and starting conditions.

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.

Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks

E.g. If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”, based on showing the classifier examples of apples, oranges and bananas.

• Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

• Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models

There are various methods to assess the results of a logistic regression analysis-

• Using Classification Matrix to look at the true negatives and false positives.

• Concordance that helps identify the ability of the logistic model to differentiate between the event happening and not happening.

• Lift helps assess the logistic model by comparing it with random selection.

• Understand the business problem

• Explore the data and become familiar with it.

• Prepare the data for modelling by detecting outliers, treating missing values, transforming variables, etc.

• After data preparation, start running the model, analyse the result and tweak the approach. This is an iterative step till the best possible outcome is achieved.

• Validate the model using a new data set.

• Start implementing the model and track the result to analyse the performance of the model over the period of time.

Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for a large number of outliers, the values can be substituted with either the 99th or the 1st percentile values.

All extreme values are not outlier values. The most common ways to treat outlier values

1. To change the value and bring in within a range.
2. To just remove the value.

Any dice has six sides from 1-6. There is no way to get seven equal outcomes from a single rolling of a dice. If we roll the die twice and consider the event of two rolls, we now have 36 different outcomes.

To get our 7 equal outcomes we have to reduce this 36 to a number divisible by 7. We can thus consider only 35 outcomes and exclude the other one.

A simple scenario can be to exclude the combination (6,6), i.e., to roll the dice again if 6 appears twice.

All the remaining combinations from (1,1) till (6,5) can be divided into 7 parts of 5 each. This way all the seven sets of outcomes are equally likely.

• Artificial Neural networks are a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. 

• Neural Networks can adapt to changing input so the network generates the best possible result with no requirement to redesign the output criteria.

Artificial Neural Networks works on the same principle as a biological Neural Network. It consists of inputs which get processed with weighted sums and Bias, with the help of Activation Functions.

• A gradient measures how much the output of a function changes if you change the inputs a little bit. It simply measures the change in all weights with regard to the change in error. You can also think of a gradient as the slope of a function.

• Gradient Descent can be thought of climbing down to the bottom of a valley, instead of climbing up a hill.  This is because it is a minimization algorithm that minimizes a given function (Activation Function).

Backpropagation is a training algorithm used for multilayer neural network. In this method, we move the error from an end of the network to all weights inside the network and thus allowing efficient computation of the gradient.

It has the following steps:

• Forward Propagation of Training Data.
• Derivatives are computed using output and target.
• Back Propagate for computing derivative of error related to output activation.
• Using previously calculated derivatives for output.
• Update the Weights.

Stochastic Gradient Descent: We use only single training example for calculation of gradient and update parameters.

Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at each iteration.

Mini-batch Gradient Descent: It’s one of the most popular optimisation algorithms. It’s a variant of Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is used.

• Pytorch

• TensorFlow

• Microsoft Cognitive Toolkit

• Keras

• Caffe

• Chainer

• Auto Encoders are simple learning networks that aim to transform inputs into outputs with the minimum possible error.

• This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The autoencoder receives unlabeled input which is then encoded to reconstruct the input.