Data Science Interview Questions and Answers for Fresher’s and Experienced Candidates

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Data Science Interview Questions and Answers

Train Yourself with the Best Data Science Interview Questions and Answers. The list on Data Science Question and Answers consist of all type of interview questions and answers, which are specially designed for all the Data Science Fresher’s and for all the experienced candidates. This question and answer set can be very much beneficial for Data Science new bees. Below is the given list of commonly asked Data Science interview questions and answers which are specially designed for each one at 3RI Technologies. 

We trust and anticipate that these set of Question and Answers related to Data Science will be highly beneficial for you and your career and help you reach new heights in the IT industry. These sets of questions and answers are specially designed by our Data Science Professional experts for freshers as well as for experienced candidates. These are one of the frequently asked questions in the top MNC’s of the IT industry. Apart from this, if you wish to pursue a Data Science course in Pune’s most reliable Data Science Training Institute, you can always drop by at 3RI Technologies. 

Now let us dive into the article below and check out the set of Data Science questions & Answers.

Q1. What do you mean by precision and recall in Data Science?

Ans: In Data science, precision is nothing but the percentage of all the correct predictions made by you. Whereas, recall is the exact percentage of all the predictions which are in point of fact true. 

Q2. What is the meaning of the word Data Science? 

Ans: From large volumes of data which is unstructured or structured, Data Science is nothing but the knowledge of extraction for it. In sort Data Science is the continuance of the data mining field and the predictive analysis. In other and simple words, it is commonly known as knowledge discovery and data mining.

Q3. What does the value of P of the statistics in data Science mean?

Ans: The P value is commonly used for determining the value of the end result, following a hypothesis test in statistics. The P value helps a reader to wrap up which is normally between 0 and 1.

  • P- Value> 0.05 indicates frail confirmation against a zero hypothesis, which strongly means that the null hypothesis can’t be rejected.
  • P-value <= 0.05 indicates sturdy substantiation alongside the zero hypotheses, that means that the null hypothesis can be rejected.
  • P-value = 0.05 is a boundary and a limited value which indicates that it is likely probable to go in both the directions.

Q4. Can one provide any kind of statistical method which can turn out to be very useful for all the data analysts?

Ans: The statistical methods which are commonly used by all the data analysts are listed below:

  • Mark’s process
  • Sort statistics, percentages, detection of outliers.
  • Bayes method
  • Imputation
  • Spatial and grape processes.
  • Symbolic algorithm
  • Mathematical optimization.

Q5. What do you mean by “Clustering”? List down all the properties of the Clustering algorithms in the answer below.

Ans: Clustering is nothing but a simple procedure where the all the data can be classified into one or more groups. Here are the following clustering algorithm properties which are listed below:

  • Iteratively
  • Hard and soft
  • Disjunctive
  • Hierarchical or straight.

Q6. List down some of the statistical methods which can be useful for all the data analysts?

Ans: Some of the simple and effective statistical methods which can be useful for all the data scientists are:

  • Sort statistics, percentiles, find out
  • Bayes method
  • Mathematical optimization.
  • Symbolic algorithm
  • Spatial and grape processes.
  • Mark’s process
  • Imputation techniques, etc.

Q7. Which are few of the common shortcomings of the linear model in Data Science?

Ans: Some of the vital disadvantages of using the linear model are:

  • The hypothesis of the linearity can consist of lot of errors in it.
  • It cannot be used for calculating the binary results or normal results.
  • There are too many huge number problems which cannot be effortlessly solved.

Q8. Name some of the common problems which are encountered by all the data analysts today?

Ans: Some of the most common problems which are encountered by all the data analysts in today’s world are:

  • Extremely bad pronunciation.
  • Replication of entries
  • Values that are missed
  • Values that are illegal 
  • Values that are differently presented.
  • Identification of overlapping data

Q9. Which are some of the common data verification methods that are used by all the data analysts?

Ans: Normally, some of the common methods that used by the data analysts to validate the data are:

  • Data verification
  • data verification

Q10.  Mention below all the various and the different steps in an analysis project.

Ans: The various steps used  in an analysis project include the following,

  • Definition of the problem
  • Data preparation
  • Data exploration
  • Data validation.
  • Modeling
  • Implementation and Monitoring

Q11. List of some best tools that can be useful for data-analysis?

Ans:

  • OpenRefine
  • Tableau
  • KNIME
  • Solver
  • Wolfram Alpha’s
  • NodeXL
  • Google Fusion Tables
  • Google Search Operators
  • RapidMiner
  • Io

Q12. Mention below the 7 common ways which are used in statistics by data scientists?

Ans:

  • Create models that anticipate the signal, not noise.
  • Design and interpret experiments to inform about product decisions.
  • Remember user behavior, commitment, conversion, and potential customers.
  • Convert large data to a large image
  • Estimate intelligently.
  • Give your users what they want.
  • Tell a story with data.

Q13. Which kind of bias can be occurred during stamping? 

Ans:

  • Survival of bias
  • Choice of bias
  • Low bias

Q14. Which are some of the significant and various methods for recovery of data commonly used by the data scientists?

Ans: Here are the 2 common methods that are used for verifying the data for data analysis and recovery:

  • Data Screening
  • Data verification

Q15. What do you mean by the imputation process? What are some of the common types of imputation techniques?

Ans: An imputation process is a process that involves and replaces the missing data elements with all its replacement values. There are two major kinds of imputation processes with subtypes which are listed below:

  • The role of a hot mallet.
  • Unique imputation.
  • Average allocation
  • Impact with a cold roof.
  • Stochastic regression
  • More imputation
  • Imputation regression.

Q16: What is the command for storing the R objects in a file?

Ans: Save (x, file = “x.Rdata”)

Q17. Which are some of the best ways for using Hadoop and R together for data analysis purpose?

Ans: In both the cases of Hadoop and R, they are very much complimented in provision to analyzing large amounts of data and for viewing. Altogether, there are nearly 4 different ways of using Big data Hadoop and R together. 

Q18. How can you access the element in columns 2 and 4 of the matrix with the name M?

Ans:

  • In the Indexing method, you can effectively access the elements of the matrix by using the square.
  • While in the row and column method, you can access the elements as var. 

Q 19: How can you explain logistic regression in Data Science?

In data science, logistic regression is also known as the logit model. It is a way use to find out the binary outcome from a linear that are a combination of predictor variables.

Q 20: What is meant by Back Propagation?

It is a way used to tuning the weights of a neural net that relies on the error rate acquired in the last epoch. Proper tuning is used to decline error rates and also make the model relevant by enhancing its generalization. It is the substance of neural net training. 

Q 21: What is meant by Normal Distribution?

It is a set of a continuous variables spread over a standard curve or even in shape like a bell curve. It is deemed as a continuous probability distribution that is highly useful in statistics. The Normal Distribution is needed to analyze the variable along with their relationships in the standard distribution curve.

Q 22: Explain the term a Random Forest

It is a machine learning method that assists the users to indulge in all kinds of classification and regression tasks. Random Forest is used to treating missing values along with outlier values.

Q 23: What is the decision tree algorithm?

It is a prominent supervised machine learning algorithm that is used for classification and regression. It permits the splitting of the database into smaller subsets. It is used to manage both numerical and categorical data. 

Q 24: What is the p-value?

During the process of a hypothesis test in statistics, with a p-value, it becomes possible to find out the strength of a result. P-value is a numerical number that comes between 0 and 1. It is based on the value that assists the users to denote the strength of the particular outcome or result. 

Q 25: Explain Prior probability and likelihood in data science?

The likelihood is a chance of classifying a particular observant in the appearance of some other variable. At the same time, prior probability is used to specify a proportion of the dependent variable in a given data set. 

Q 26: What do you mean by recommender systems?

A recommender system is a subclass of information that is available in the filtering technique. It is used to forecast the ratings or preferences which clients or users like to provide to a product.

Q 27: Explain the term Power Analysis

The fact, in experimental design, power analysis is considered as a significant part. It assists the users to decide the sample size that is needed to forecast the effect of a particular size from a cause along with a specific level of assurance. Moreover, it also permits the users to implement a specific probability in a sample size constraint.

Q 28: What do you mean by Collaborative filtering?

In data science, collaborative filtering is used to find out specific patterns by assembling viewpoints, different agents, and multiple data sources. 

Q 29: Explain the word bias? How it different from variance?

Bias is a term used as a deviation from expectation in the data. It is an error in the data that often go unnoticed. 

Bias is like the assumptions determined by the model to turn the target function simpler to approximate, while variance is that amount used to determine the target function.

Q 30: What do you mean by the term ‘Naive’ in a Naive Bayes algorithm?

The fact, Naive Bayes Algorithm model, is entirely dependent on the Bayes Theorem. It is used to know about the occurrence of an event. It is depended upon prior knowledge of conditions that is related to that particular event.

Q 31: Explain the term Linear Regression

It is a statistical programming method that is used to predict the score of variable ‘B’ via the rating of a variable ‘A.’ In that case, A is considered as the criterion variable, and B is considered as the predictor variable. 

Q 32: What is the fundamental difference between the mean value and the expected value?

The fact, there is no apparent difference between both terms. These are used in different contexts. 

Mean value is used when we discuss a probability distribution while the expected value is used in the context of a random variable. 

Q 33: Why we conduct A/B Testing?

When there is a need to conduct random experiments with two variables, say variable A and variable B, then AB testing considers. The intention of using this testing or method is to hike up the amount of the outcome of a particular strategy. 

Q 34: Explain the term Ensemble Learning

Ensemble learning is a strategy where multiple models like classifiers are strategically generated and assembled to sort out a specific computational intelligence problem. Moreover, this method is needed to enhance prediction, classification, etc. 

Ensemble learning methods have two types:

  • Bagging: This method assists a user to deploy the same learners on small sample populations. It also supports users to make nearer predictions. 
  • Boosting: It is an iterative way where we permit the weight of observation to rely upon the last classification. This method declines the bias error and also assists us in setting up strong predictive models.

Q 35: What is meant by cross-validation?

It is a technique for analyzing how the results of statistical analysis will specify for an Independent dataset. This way is used in backgrounds where the goal is defined or forecast, and one requires to know how much accuracy a model will achieve.

Q 36: What do you mean by the K-means clustering method?

K-means clustering is a significant unsupervised learning method. It is a method of classifying data by using a particular set of clusters that is known as K clusters. It is implanted into the group to the similarity in the data.

Q 37: What is deep learning?

It is a subtype of machine learning that is related to algorithms. It is inspired by the structure that is known as ANN (artificial neural networks).

Q 38: Tell me some kinds of deep learning frameworks

  • TensorFlow
  • Chainer
  • Caffe
  • Keras
  • Pytorch
  • Microsoft Cognitive Toolkit

Q 39: Does it feasible to represent the correlation between the term categorical and continuous variable?

Yup! The users can use an analysis covariance method to capture the assembling or association between categorical and continuous variables. 

Q 40: Can you know the difference between a Test Set and a validation set?

Validation is a part of a training set. It is used for parameter selection. It is used to avoid overfitting of the model that is building. The test set is used to evaluate or test the performance of a trained machine learning model. 

Q 41: Do you know about normal Distribution?

Yes! When there is an equal distribution of mean, mode, and median, it is known as normal Distribution.

Q 42: Explain the term reinforcement learning

It is a learning mechanism that assists in mapping situations to actions. The outcome derived from assists to enhance the binary reward signal. In this way, a learner doesn’t explain which work needs to take, and it discovers the work that has a maximum reward. 

Q 43: Which one language is the best for text analytics? Python or R?

Python is a language that is best for text analytics. It is more suitable as having a rich library that is known as pandas. It permits the users to use high-level data analysis tools along with data structure. However, R doesn’t have these sorts of features.

Q 44: How can you explain the term auto-Encoder?

Auto-encoder is learning networks that assist users in transforming inputs into outputs with only a few errors. It means that production will be mostly closed to input.

Q 45: Explain Boltzmann Machine

It is a simple learning algorithm that is used to find out those features that have complex regularities in the training data. We also use this algorithm to improve the weights and quantity of a particular problem.

Q 46: What do you mean by the terms uniform distribution and skewed Distribution?

When the data is spread equally in the range, it is known as uniform Distribution, while when the data is covered on any single side of the plot, it is known as skewed Distribution. 

Q 47: What do you know about a recall?

It is a ratio of the exact positive rate in opposite the actual positive rate. It has ranges starts from 0 to 1.

Q 48: In which situation, underfitting occurs in a static model

It needs to happen when the machine learning algorithm and statistical model are not able to take consideration of the underlying trend of the data. 

Q 49: What is meant by a univariate analysis?

When an analysis is implemented to none attribute at the same time, it is called as univariate analysis. 

Q 50: How can it possible to choose an essential variable in a data set?

  • Before choosing a vital variable, eliminate the correlated variables.
  • Use linear regression and choose variables that rely on that p values.
  • Use forward selection, backward, and stepwise selection.
  • Use random forest, xgboost, and plot variable important chart.
  • Now measure information gain for the particular set of attributes and chose top n attributes accordingly.

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