As the amount of data grows rapidly, data science
is developing into a more robust and popular field in the modern world. It is
extensively used across various business sectors, including banking, marketing, insurance, education, healthcare, finance, and other
fields. Therefore, most businesses are now employing experts in data science
due to the industry's growing demand. Data Scientists are among the top-paid IT
experts in this field.
For your subsequent data science job interviews,
we will provide you with frequently asked interview questions in this blog.
Data Science
Interview Questions and Answers 2022
- In data science, which libraries are most
widely used?
Given below is a list of the top 10 widely used Python libraries for data science:
● Keras
● Matplotlib
● NumPy
● Pandas
● PyTorch
● SciPy
● SciKit-Learn
● TensorFlow
- Explain the differences between
univariate, bivariate, and multivariate analysis.
Univariate analysis: It is the most straightforward statistical data analysis technique, where only one variable is used to analyze data. Since there is only one variable, it does not deal with causes or effects relationships, and it is mainly used to describe the data and identify any patterns.
Bivariate analysis: Compared to univariate analysis, bivariate analysis is a little more analytical. In this analysis, two variables (X and Y) are compared to examine their relationships, which may be dependent or independent of one another.
Multivariate analysis: It is a more complicated statistical analysis technique using more than two variables in a data set. It is used to identify patterns in data, draw precise comparisons, discard irrelevant data, and more.
- What is the difference between
supervised and unsupervised learning?
Supervised and
unsupervised learning are the two methods used in machine learning.
A supervised learning algorithm uses labeled data as input. It is used to predict the output and is also useful for sentiment analysis, spam detection, weather forecasting, etc.
An unsupervised learning algorithm uses unlabeled data as input. It is used to identify hidden patterns in the data and is also useful for anomaly detection, recommendation engines, and medical imaging.
- What methods of feature selection
are employed to choose the correct variables?
The two basic feature selection methods that are used to select the appropriate variables are:
- Filter methods
- Wrapper methods
- In data science, what is a variance?
Variance is a statistical assessment of the difference between individual numbers in a data set and describes how far apart each number in the set is from the mean value.
- What are the methods for
dimensionality reduction?
Methods for dimensionality reduction include:
●
Principal Component Analysis (PCA)
●
Linear Discriminant Analysis (LDA)
● Generalized Discriminant Analysis (GDA)
- In a linear regression model, how
are MSE and RMSE determined?
MSE stands for Mean Square Error
RMSE stands for Root Mean Square Error
- What is the significance of the p-value?
●
p-value ≤ 0.05
It shows strong evidence against the null hypothesis; therefore, we reject the null hypothesis and adopt the alternative hypothesis.
●
p-value > 0.05
It shows weak evidence against the null hypothesis; therefore, we retain the null hypothesis and reject the alternative hypothesis.
●
p-value at cutoff
0.05
It is assumed to be marginal, so that it might go either way.
- What are the sampling techniques?
● Simple random
sampling
● Stratified
sampling
● Cluster sampling
● Systematic sampling
- What are the types of selection bias?
The following are six types of selection bias:
● Survivorship bias
● Attrition bias
● Sampling bias
● Exclusion bias
● Recall bias
● Volunteer or
self-selection bias
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