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Data Science Interview Questions and Answers
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Data Science Interview Questions and Answers

Published On: May 6, 2021

Data Science Interview Questions and Answers

Data Science is the hottest field in 2021 and it gives tremendous job opportunities worldwide for freshers and experienced candidates. Top companies are employing skilled professionmanage various tasks through high profile with high salary pay. There are innumerable interviews lines up for freshers and recruiters are looking after certified and talented candidates.

By keeping them in mind, we prepare the most frequent interview questions and answers for the benefit of the learners that help them ace the interview easily in a single attempt. Join us to learn the in-demand Data Science Training in Chennai at Softlogic’s with an industry-accredited certificate and job guidance.

1.What is Data Science?

Data Science is the collection of tools, algorithms, scientific methods, and principles used to explore the hidden patterns from structured and unstructured raw data. These insights help the business people to make better decisions according to the trending business growth.

2.What is the difference between supervised and unsupervised learning in data science?

In Supervised learning, the input data is labeled and uses a training data set. Supervised learning is used for prediction and allows classification and regression. While in unsupelearning, the input data is unlabelled and utilizes the input data set for performing the analysis. It enables classification, density estimation, and dimension reduction.

3.What is Selection Bias?

Selection Bias is the type of error that occurred by individuals, groups, or analyzed data when it is done without achieving proper randomization. It is also referred to as the seleffect
that the population is not analyzed to ensure the perfect result. It occurs when people volunteer to study analytics.

4.Define Survivorship bias.

Survivorship bias is the logical error that focuses on aspects that supports a surviving process by overlooking as it has a lack of prominence. It leads to wrong conclusions in vways.

Stay tuned for our regular updates on this Data Science Interview Questions and Answers as it is done as per the trending requirements of top companies. Our Data Science Course in Cis useful for gaining more insights and in-demand knowledge and skills to perform in the companies.

5.What are the types of Selection Bias?

The four types of selection bias are Sampling Bias, Time Interval, Data, and Attrition. Sampling Bias is a systematic error caused by non-random samples, Time Interval is a trial thbe terminated at an extreme value, Data is the conclusion, and Attrition is the loss of participants.

6.What are the steps of decision tree making?

Step 1: Take up the entire data set as input

Step 2: Calculate the entropy of predictor attributes and target variable

Step 3: Calculate the gained information of all attributes

Step 4: Choose the attribute that has the highest value gain as root node

Step 5: Repeat the process until the decision node of all branches finalized.

7.What is a random forest model?

A random forest is developed on several decision trees and if we split the data into various packages and make a decision tree in all groups of data, then the random forest puts atrees together for fetching useful insights.

8.What are the steps involved in developing a random forest model?

Step 1: Randomly select ‘k’ features from the total number of ‘m’ features when k << m

Step 2: Calculate the D node among ‘k’ features using the best split point

Step 3: Split the nodes into sub-nodes using the best split

Step 4: Repeat the steps until leaf nodes are formed

Step 5: Develop forest by repeating the above steps one to ‘n’ times to generate ‘n’ number of trees.

9.How to avoid the overfitting of the model?

Overfitting is the model that sets every small amount of data and avoids the bigger picture. The following methods are used to avoid overfitting:

Keep the sample model that takes fewer variables into account and removes the noise of training data

Utilize cross-validation techniques such as k-folds cross-validation

Apply regularization techniques such as LASSO that penalize the models that have the possibilities of overfitting

8.Define Univariate

Univariate is the data that contains only one variable and the purpose of this analysis is to define the data to extract the pattern within them. This pattern will be studconcluding median, mean, mode, range or dispersion, minimum, and maximum. Ex: Height report of students

9.Describe Bivariate

Bivariate data includes two different variables. This kind of data analysis deals with causes and relationships and it is used to determine the relationship of two given variablerelationship is visible for users to make better decisions. Ex: the analysis of temperature and the sales of ice cream.

10.What is Multivariate?

Multivariate data includes three or more variables and it is categorized as per the total number of dependent variables. It will be studied by fetching the conclusions through mean,median, minimum, maximum, and dispersion or range. Ex: Analysis of price attributes of a house.

11.What are the feature selection methods applied for selecting the right variables?

There are two main methods as Filter methods and Wrapper methods. Filter Methods include linear discrimination analysis, ANOVA, and Chi-Square. The Wrapper Methods include FSelection, Backward Selection, and Recursive Feature Elimination. The Wrapper Methods requires high-end computer systems and it is labor-intensive to perform the analysis.

12.What are the steps to maintain a deployed model?

Step 1: Monitor – Continuous monitoring of all models leads to determine the performance accuracy.

Step 2: Evaluate – Evaluation Metrics of the current model should be calculated to define if any new algorithm is required.

Step 3: Compare – Comparing the new models to each other brings the performance of the best model.

Step 4: Rebuild – The best-performing model will be re-built as per the current state of data.

13.Define Recommender Systems

The recommender systems are used to predict the user rate for a particular product according to their preferences. It happens through two different areas such as collaborative filand content-based filtering.

14.What is collaborative filtering?

The marketer recommends products as per the similar interests of users. Ex: When the user checks something in Amazon it shows “Users who bought this also bought….” witrecommendations.

15.What is content-based filtering?

The app shows the recommendation of the same properties as per the user interests. Ex: Spotify used to recommend music according to the latest listening of the users.

16.What p-value indicates?

If p-value


0.05, it indicates strong evidence against the null hypothesis and we can reject them

If p-value > 0.05, it indicates weak evidence against the null hypothesis and we can accept them

If the p-value at 0.05, it means it could be in either way as it is considered as marginal.

17.Define Feature Vectors?

A feature vector is an n-dimensional of numerical features that means an object. It is implemented in the machine learning process to represent symbolic or numeric characteristicsobject mathematically as it is easy to analyze.

18.What is logistic regression?

Logistic regression is the technique used to forecast the binary outcome from a linear combination of predictor variables.

19.Define Cross-Validation

Cross-validation is one of the model validation techniques used for evaluating how the outcomes of a statistical analysis will generalize to a single data set. It is mainly applied backgrounds to estimate the accuracy of the model to test the training phase to limit the problems such as overfitting and insight gaining.

20.What is the purpose of A/B testing?

A/B Testing is the testing of statistical hypotheses for randomized experiments with two variables such as A and B. The goal of A/B testing is to detect the changes to a web pmaximize the outcome of a particular strategy.

21.What is a linear model or linear regression?

Linear regression is the method that uses the largest square by connecting a line through plotted data points. This line is positioned to minimize the distance of all data points an distance is called “residues” or “errors”.

22.What are the limitations of linear regression or linear model?

The assumption of linearity of the errors and can’t be used for counting the outcomes and binary outcomes, and overfitting problems are the drawbacks of linear model or linear regr

23.Define the law of large numbers

Law of large number is a theorem that defines the result of performing the same experiment every often. It forms as per the frequency-style thinking and it denotes the sample mean, variance, and sample standard deviation converge to tell the estimation.

24.Define confounding variables

Confounding variables are extraneous variables in a statistical model that combines directly or indirectly with dependant and independent variables. The estimate will fail to accouthe confounding factor.

25.Describe star schema

The star schema is a traditional database schema that has a central table. Satellite table map IDs to physical descriptions and connected to central fact table using ID fields andtables are also referred to as lookup tables that are used for real-time applications as it saves memory storage. This star schema includes several layers of summarization to recover the information quicker and accurate.

26.How often an algorithm must be updated?

We have to update an algorithm when the model evolves as data streams through infrastructure, when the underlying data source is shifted, and when a case of non-stability.

28.What are Eigenvalue and Eigenvector?

Eigenvalues are the directions of a particular linear transformation serve through compressing, stretching, and flipping.

Eigenvectors are the understanding of linear transformations used to calculate for a correlation or covariance matrix.

29.List out the possible biases that can occur during sampling?

Selection Bias

Undercoverage bias

Survivorship bias

30.Define Survivorship bias

Survivorship bias is the logical error that focuses on aspects that supports a surviving process by overlooking as it has a lack of prominence. It leads to wrong conclusions in various ways.

Stay tuned for our regular updates on this Data Science Interview Questions and Answers as it is done as per the trending requirements of top companies. Our Data Science Course in Chennai is useful for gaining more insights and in-demand knowledge and skills to perform in the companies

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