Data Science Interview Questions and Answers
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Best Data Science Interview Questions and Answers
Here you will all the frequently asked Data Science Interview Questions and Answers. This will make your interview preparation simpler and will mold you confidently to face technical interviews at ease. These top 50 data science interview questions and answers will cover all the major and essential topics in data science which will assist aspirants to crack the interviews of top multinational companies.
What’s your dream? Are you intended to become an expert data scientist? Then you must prepare yourself in such a way that your knowledge in data science strongly impresses the employers. Apart from letting them know why you picked this stream, you should be able to answer all the technical data science questions that rise in the interview. So, it is essential for all the participants who wish to excel in the field of data science to go through our top 50 data science interview questions and answers.
We have given you a list of popular and famous data science interview questions that are frequently asked in the majority of the data science face to face interviews. We wish you all success in your further data science interviews. Make use of our top 50 data science interviews questions and become a successful data scientist.
Data Science is nothing but a combination of several algorithms, tools and machine learning techniques. Using Data Science, we can easily detect the similar hidden patterns from the provided raw data.
Supervised Learning | Unsupervised Learning |
Labelled input data | Unlabelled input data |
Training data set is used | Input data set is used |
It is used highly for prediction | It is highly used for analysis |
Classification and Regression is enabled | Classification, Density Estimation and Dimension Reduction is enabled |
Listed below are some of the commonly used algorithms by the data scientists:
- Linear regression
- Logistic regression
- Random Forest
- KNN
Logistic regression in Data Science is used to analyze a set of data that consists of dependent variables and little independent variables from which the binary variables can be predicted. Logistic regression has only two possible outcomes.
A statistical programming in which the variable score A is determined from the secondary variable score B where A is the criterion variable and B is the predictor variable.
The error caused in your model due to oversimplifying the machine learning algorithm which causes underfitting is known as bias.
When the machine learning algorithm or a statistical model fails to capture the underlying trend of the data then it is certain that underfitting will occur.
The bayes theorem is otherwise known as naïve bayes algorithm which defines an event probability. The knowledge of the conditions in line with the particular event is known as naïve in the naïve bayes algorithm.
The three main types of biases that occur during sampling are as follows:
- Under coverage bias
- Selection bias
- Survivorship bias
The process when you pick individual or a group of data for analysis and when no specific randomization is occurred, then it is known as the selection bias. It refers to the sample which does not appropriately represent a population that has to be analyzed.
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Cluster sampling technique is implemented when simple random sampling cannot be utilized to study the target population that is spread over.
The purpose of performing resampling process is discussed below:
- To estimate the sample statistics accuracy by randomly drawing from a set of data with replacement or using it as an accessible subset of data
- To substitute labels on data points while performing several tests
- By validating models with the help of random subsets
To analyze the right pattern by collaborating several agents, view points and multiple data sources, collaborative filtering can be used.
One of the famous supervised machine learning algorithms is known as the decision tree algorithm. This type of decision tree algorithm is used for classification and regression. It is used to handle several numerical and categorical data and it is also used to divide huge data sets into several smaller subsets.
The proportion of dependent variable in the data set is known as prior probability while likelihood is known to be the probability of categorizing a given set of observant in the occurrence of some other specific variables.
A subclass of information filtering technique which is used to predict the ratings and preferences of the customers for a particular product or service is known as recommender systems.
The three main cons that occur while using the linear model are as follows:
- Linear model does not support count or binary outcomes
- It cannot solve several overfitting problems that occur
- There can be various linearity errors with the model
An integral part of the experimental design that is used to analyze the sample size required to determine the effect of a given size with a particular assurance level is known as power analysis which also allows user to deploy a specific probability in accordance to the constraint of a sample size.
Listed below are some of the libraries in Python which can be effectively used for scientific computations and data analysis:
- SciPy, NumPy and SciKit
- Pandas
- Matplotlib
- Seaborn
A data scientist actually slices a data and extracts the valuable insights which can be used in the real-time business scenarios by the data analyst. And, moreover a data scientist will have abundant technical stuff than a data analyst. Also, they will require business information or knowledge for data visualization.
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While discussing a probability distribution the term mean value can be used and the context of a random variable is referred by the term expected value.
The process of blending several set of learners to enhance the predictive power and stability of the model is known as ensemble learning. And, there are two types of ensemble learning namely boosting and bagging.
The iterative method that is used to adjust the weight of an observation in accordance with the last classification is referred to as boosting which can reduce the bias errors and build a strong and effective predictive model.
The method that is highly preferred to implement similar learners on very small populations that ensures nearer prediction is known as bagging.
The goal of A/B testing is used to predict the changes in a web page in order to boost the strategy of the outcome and it can be used to organize random experiments with some of the two variables like A and B.
The process used to understand the linear transformations is known as eigenvectors. In order to determine the covariance matrix or correlation data scientists has to calculate eigenvectors while eigenvalues are such directions that involve compressing, stretching and flipping which can used for specific linear transformation activities.
To get better idea of customer’s expectations data scientists can make use of statistics. With statistics, data scientist can analyze customer’s interest, retention, engagement, behavior and more. Using these data, data scientists can generate a powerful data model which can be used to analyze the predictions and inferences.
A validation technique used to evaluate how the statistical analysis outcome will generalize for a dataset that is independent is known as cross validation. This technique can be highly used in a background where objective is in the form of forecast and to accurately estimate the accomplishment of a model.
An essence of neural net training used to tune the neural net weights depending on the obtained error rate predicted from the previous epoch. This can minimize error rates and builds a reliable model by enhancing generalization.
The machine learning method that is used to perform several classification and regression tasks are known as random forest. It is also used to treat outlier and missing values.
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An essential unsupervised learning method used to classify data with a help of certain set of clusters known as k clusters is termed to be k-means. It is highly used to find the data similarity.
A special set of algorithm that is used to revolutionize the machine learning is known as artificial neural network (ANN). It adapts to the input changes such that the several possible results are generated by the network without redesigning the output criteria.
A subtype of machine learning is known as deep learning that is concerned with the algorithms highly inspired by the structure.
Some of the types of deep learning frameworks are as follows:
- Pytorch
- Microsoft Cognitive Toolkit
- TensorFlow
- Caffe
- Chainer
- Keras
P value actually allows you to predict the result’s strength while performing hypothesis test during statistics. P value is also termed to be a numerical value between 0 and 1 using which the strength of the resultant can be denoted.
When the below listed situation arises you will have to update the algorithm of Data Science:
When you need your data model to come up as the data streams using the infrastructure
When the underlying data source changes and if it turns non-stationary
The social media data can be collected using Twitter, Instagram and Facebook’s API. For instance, in Twitter, we can develop a highlight for every tweet like the date of tweets, list of followers, retweets and more. Then with the help of a multivariate time series model, users can easily predict the weather condition.
Python which comprises of several libraries known as pandas will be more suitable for text analysis. Also, python has high-level data analysis tools and data structures while these features are not available with R.
A set of continuous variable that is spread across a normal curve or a curve that is in the shape of a bell is known as normal distribution. It can be considered as the continuous probability distribution used in statistics. Normal distribution is used to predict the variables and their relations while using the normal distribution curve.
Yes, with the help of the covariance technique, we can easily capture the correlation between the continuous and categorical variable.
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The learning networks used to transform inputs and outputs which results in error reduction is known as auto-encoder. Through this, you can get output that is similar to the input.
When data is distributed on any one side of the plot it is known as skewed distribution while data is spread equally in the specified range it can be denoted as uniform distribution.
A simple learning algorithm used to discover some of the features of training data that are used to represent complex regularities. Through this algorithm, we can easily optimize the quantity and the weight of the given problem.
A dirty data will lead to incorrect inside which can actually degrade the prospect of any particular organization.
The most commonly used error metric in classification mechanism is known as precision, the precision range can be 0 to 1 where 1 can be 100%
Univariate analysis is an analysis which can be applied to none attribute at a single time. The most widely used univariate model is known as the boxplot.
The ratio of the true positive rate to the actual positive rate ranging from 0 to 1 is known as recall.
The learning mechanism of how to map situations to actions is known as the reinforcement learning.
A training set that is used to select parameters which can neglect overfitting of the model thus built is known as the validation set.
A test set is highly used for evaluating or testing the performance of a trained machine learning model.
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