Top 20 Data Science Interview Questions in 2022
Data Science is considered to be one of the best jobs of 2022. The Data Science Jobs Report presented by Analytics India Magazine (AIM) provides a comprehensive study of the data science job landscape, including profiles and roles requiring analytical competencies and skills. The research offers a holistic view of the talent market affected by the Covid-19 pandemic and its recovery since the lifting of lockdown restrictions. The report highlights the rise of data science/analytics jobs in the post-pandemic world and the factors that have led to it.
If you already know the ropes, it’s time to move on to data science interview questions to land your dream role. After some general data science interview questions, here is a list of beginner and technical data interview questions and answers. Use them as a preparation aid.
As with any technical interview, ensure you have the basics and can implement ideas in code. Of course, you should also submit a good resume and be prepared to summarize the past experience.
The list below contains all the top 20 data scientist interview questions and some statistics interview questions.
1. List three types of bias that can occur during sampling
There are three types of preferences in the sampling process which are:
2. Why is TensorFlow considered essential in Data Science?
TensorFlow is highly prioritized in teaching Data Science because it supports languages ??like C++ and Python. Some data science processes benefit from faster compilation and completion compared to the Keras and Torch libraries. TensorFlow also supports CPU and GPU for more rapid data entry, editing, and analysis.
3. What is an Dropout?
Dropout is a tax in data science used to randomly drop hidden and visible units of a network. They prevent data overflow by dropping up to 20% of nodes to accommodate the required space for the iterations needed to converge the network.
4. What is a p-value?
The p-value is an estimation of the statistical essence of observation. It is the probability that shows the importance of the output for the data. We calculate the p-value to know the test statistic of the model. It usually helps us decide whether we can accept or reject the null hypothesis.
5. What is the difference between error and residual error?
The error occurs in the values, while the prediction gives us the difference between the observed values ??and the valid values ??of the data set. The residual error is the difference between observed and predicted values. We use the residual error to estimate the algorithm’s performance because the valid values ??are never known. So we use the observed values ??to measure the error using the residuals. It helps us get a proper estimate of the error.
6. Why use the summary function in R?
The summary function gives us the statistics of the implemented algorithm on a particular data set. It contains various objects, variables, data attributes, etc. When entering the function, it provides summary statistics for individual objects. We use the summary function when we want information about the values ??present in a dataset. It provides summary statistics in the following form: The minimum and maximum values ??from a particular dataset column. It also provides the median, mean, 1st quartile, and 3rd quartile values ??to help us understand them ??better.
7. Explain univariate, bivariate, and multivariate analyses.
We often encounter univariate, bivariate, and multivariate concepts when analyzing data.
Univariate Analysis: Univariate analysis involves analyzing data with only one variable or, in other words, one column or vector of data. This analysis permits us to apprehend the data and extract patterns and trends. Example: Investigating the weight of a group of people.
Bivariate Analysis: Bivariate analysis involves analyzing data with exactly two variables; in other words, the data can be placed in a two-column table. This analysis allows us to find out the relationship between the variables. Example: Analysis of data containing temperature and altitude.
Multivariate Analysis: Multivariate analysis involves analyzing data with more than two variables. The number of columns of data can be more than two. This kind of analysis allows us to determine the effect of all other variables (input variables) on one variable (output variable).
Example: Analyzing house price data that contains information about houses such as location, crime rate, square footage, number of floors, etc.
8. What is Ensemble learning?
Ensemble learning is the method of converging a diverse set of learners who are individual models. It helps to improve the stability and predictive ability of the model.
9. What are the different types of Ensemble learning?
The different kinds of ensemble learning are:
Bagging: Implements simple students on a small population and takes the mean for estimation purposes
Boosting: Adjusts the weight of the observations and classifies the population into different sets before predicting the outcome.
10. What is the purpose of conducting A/B testing?
AB testing is used to perform random experiments with two variables, A and B. This testing method aims to detect changes in a web page to maximize or increase the outcome of the strategy.
11. What are the assumptions necessary for linear regression?
Several assumptions are required for linear regression. They are as follows:
The data, a sample from the population, used to train the model should represent the population.
The connection between the independent variables and the mean of the dependent variables is linear.
The variance of the residual will be the same for any value of the independent variable. It is also represented as X.
Each observation is independent of all other words.
The independent variable is usually distributed for any value of the independent variable.
12. What happens when some of the assumptions necessary for linear regression are violated?
These assumptions may be lightly violated (i.e., some minor violations) or strongly (i.e., most of the data has violations). Both of these violations will have different effects on the linear regression model.
Substantial violations of these assumptions render the results completely redundant. Slight violations of these assumptions cause the effects with more significant bias or variance.
13. Explain recommendation systems?
It is a subclass of data filtering methods. It helps you predict the preferences or ratings that users are likely to give to a product.
14. List three disadvantages of using a linear model
Three disadvantages of the linear model are:
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Assumption of linearity of errors.
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You cannot use this model for binary or numeric results
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There are a lot of overblown problems that it can’t solve
15. Why do you need to resample?
Resampling is done in the following cases:
Estimating the precision of a sample statistic by randomly drawing with replacement from a set of data points or using a subset of the available data
Replacing labels on data points when performing necessary tests
Validating models using random subsets
16. Discuss the normal distribution
A normal distribution is uniformly distributed. As such, the mean, median, and mode are the same.
17. How can one select base variables when working on a data set?
One can use these variable selection methods:
Remove correlated variables before selecting important variables
Use linear regression to choose variables that depend on these p-values.
Use backward selection, forward selection, and progressive selection
Use Xgboost and Random Forest and create a variable importance graph.
Measure the information obtained for the given features and select the top n features accordingly.
18. Can correlation be captured between continuous and categorical variables?
One can utilize the analysis of covariance technique to capture the association between continuous and categorical variables.
19. What are skewed distribution and uniform distribution?
A skewed distribution occurs when the data is spread out on either side of the graph, while a uniform distribution is identified when the spread of information is equal in the range.
20. When does a static model become underequipped?
An underfit occurs when a statistical model or machine learning algorithm fails to capture the underlying trend in the data.
Conclusion
In this article, we have seen some top data science interview questions. This would help you in a job interview.
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