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Table of contents
1.
Introduction
2.
Easy-level Interview Questions
3.
Medium-Level Interview Questions
4.
Hard-Level Interview Questions
5.
Conclusion
Last Updated: Jun 13, 2024
Easy

Data Science Interview Questions

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Introduction

Hello Ninjas, are you looking for Data Science interview question? If yes, then you have made it to the right platform. We have gathered a list of the most asked and popular interview questions for Data Science. Data Science is a potpourri of statistics, artificial intelligence, maths, machine learning, and algorithms. Therefore, If one wants to get their dream job, having a list of selected and popular interview questions makes preparation much smoother.

Data Science Interview Questions

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Easy-level Interview Questions

1. What do you mean by Data Science?

Answer:  Data Science is an area of study that deals with significant data volume using modern-day technology such as statistics, Artificial Intelligence, maths, Machine Learning, and algorithms. Using these, we identify relevant patterns in our data for making strategic decisions. We use it to create data models to get an optimal solution for our problem.

2. How Data Science and Data Analytics are different from each other?

Answer: Data analytics analyzes the data to see valuable patterns and solve predefined problems. It uses Data Mining, modeling, analysis, and database management tools. Whereas, Data Science uses artificial intelligence, machine learning, algorithms, and asking relevant questions. The relevant information is extracted from unstructured or unorganized data.


3. What is Sampling?

Answer: Usually, a large volume of data is available for analysis, but performing data analysis on such massive data is not possible. In such scenarios, sampling plays an important role. A small portion of data samples are selected, and suitable analysis is performed. The choice should be made in such a way that it correctly represents the rest of the data.


4. What is selection bias?

Answer: Selection bias occurs while sampling. Data is sampled so that an indiscriminate piece is not achieved. Selection bias can also be referred to as non-random sampling. Therefore, in this, the sample doesn't truly represent the dataset.


5. What do you mean by linear regression?

Answer: There are generally two types of variables, dependent and independent. Linear regression helps understand the relationship between these dependent and independent variables. It tells us how the dependent variable changes with respect to the independent variable. Simple linear regression is the case in which only one independent variable is present. But, when there is more than one independent variable, then it is called multiple linear regression.

 
6. What do you mean by logistic regression?

Answer: Logistic regression is a logistic model. It allows us to understand the relationship between binary dependent and independent variables. This kind of regression is usually used for prediction or classification. The outcome of logistic regression is definite or a discrete value.


7. How is Data Science different from traditional application programming?

Answer: In Traditional programming, a program is written in assembly or high-level compiler languages such as C, C++, Python, etc. In such programming, we judge the input based on The output. Generally, we write many essential steps to solve a problem. In comparison,  Data science uses artificial intelligence and machine learning and works on patterns observed in the data. Data science algorithms use mathematical analysis to give out the rules to match the inputs to outputs.


8. What do you understand by the term tensors?

Answer: Tensors usually portray various applications, including videos or images. This mathematical object consists of linear algebra, through which selection vectors (vectors being a mathematical object) are mapped to numerical values.


9. Explain Boltzmann Machine's concept.

Answer: Boltzmann Machine discovers unique features which portray complex regularities. This type of machine consists of repeating neural networks, and decisions are made by binary nodes using a simple learning algorithm. It uses the algorithm to optimize the quantity and weight of particular complications.
 

10. What do you mean by Power Analysis?

Answer: We use power analysis while calculating the smallest sample size during an experiment. This analysis is done before data collection, which aids the researcher in determining the minimal sample size, given some significant level, effect size, and statistical power.

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Medium-Level Interview Questions

1. How is Deep Learning used in Data Science?

Answer: Deep learning, a subset of machine learning, is a neural network based on convolutional neural networks that stimulate the human brain's behavior. It profoundly connects to various algorithms, which is encouraged by the human brain's structure and function. These networks enable us to "learn" from loads of data.


2. What are some Deep Learning Frameworks used in Data Science?

Answer: Some of the popular deep-learning frameworks used in Data Science are as follows.


3. How is Deep Learning different from Machine Learning?

Answer: Deep and Machine Learning are both AI, but Deep Learning uses artificial neural networks to stimulate the human brain's behavior. Also, it is a subdivision of machine learning. Whereas, Machine learning is more adaptable, with minor human interruption. Therefore, being a superset of Deep understanding, it involves algorithms usually used for smaller datasets.


4. What do you mean by batch normalization?

Answer: Deep neural networks are trained using batch normalization, which plays a significant role in settling the learning process and improving the performance and stability of neural networks. To achieve such performance, inputs can be normalized, contributing to each layer; this results in mean output activation staying at 0 given a standard deviation of 1.


5. How is cluster sampling different from systematic sampling?

Answer: There are many types of sampling plans used in statistical analysis, two of them being systematic and cluster sampling. In cluster sampling, we usually segregate the population into clusters. From these clusters, we randomly select some of them in the form of a sample. Also, one must remember that clusters represent the population as a whole.


6. What do you mean by clustering algorithm?

Answer: In a clustering algorithm, data points are grouped into clusters of similar data points, i.e., stuff aligned to similarities is grouped in one. The clustering algorithm is an unsupervised or autonomous learning method. Also, each set has a cluster ID. These IDs are used in the simplification and processing of data.


7. What do you mean by GAN?

Answer: GAN stands for generative adversarial network. This generative model comprises two networks that can produce new content. It is a recent innovation in machine learning, which creates data instances resembling our training data. They are a popular ML model for online retail sales.
 

8. What do you mean by true-positive rate and false-positive rate?

Answer: The false-positive rate is given as, FP/FP+TN, where FP states the number of false positives and TN is the number of true positives. It is a probability that a positive result is generated when the actual value is negative.

The true-Positive rate is given as, TP/FP+TN, where FP states the number of false positives and TN is the number of true positives. It is a probability that a positive result is generated when the actual value is positive.


9. How is Batch different from Stochastic Gradient Descent?

Answer: These descent models are used to train linear regression models. The Gradient Descent model consists of iterative optimization algorithm. The Batch Gradient Descent uses complete data set to compute the gradient, while, Stochastic uses only a single sample.

10. How is long-format data different from wide-format data?

Answer: Datasets can be depicted in two formats, i.e., long and wide. In wide-format data, the values are not repeated in the first column. On the other hand, in the long format, the values are repeated in the first column. Data is stored more densely in a long-form compared to a wide design.

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Hard-Level Interview Questions

1. What are the different layers of CNN?

Answer: CNN consists of four different layers:

  • Convolutional layer: It consists of filters whose size is smaller than the actual image.
     
  • ReLU Layer: In ReLU Layer, negative values are removed from the filtered image and replaced by zero.
     
  • Pooling Later: In Pooling Layer, sharp and smooth attributes are pulled out by adding Pooling Layer after the Convolutional layer.
     
  • Fully Connected Layer: This is a neural network layer. Each neuron uses a linear transformation to the input vector through a weights matrix.


2. What do you mean by exploding gradients?

Answer: Exploding gradient results when several significant gradient errors are grouped. An exploding gradient is the inverse of a vanishing gradient. This unstable model makes it incapable of learning and training data. Exploding gradients can be resolved by changing the error derivative before propagating it back through the network. Hence, if the derivatives are massive, then gradients increase exponentially.

3. What do you mean by RNN?

Answer: RNN stands for a recurrent neural network that processes data sequences. The result of the previous step is an input of the current stage. This type of network is generally used for time series, prediction, voice recognition, language processing, etc. RNN being an artificial neural network recognizes sequential data characteristics and uses distinct patterns for prediction.

4. What do you mean by Ensemble Learning?

Answer: Ensemble learning consists of a meta approach to machine learning. It combines a wide variety of sets of learners, which are sole models. This kind of learning enhances the stability and predictive power of the model and strategically generates and combines classifiers or experts to solve a specific complex problem.


5. State different types of Ensemble Learning?

Answer: Different kinds of Ensemble Learning are:

  • Bagging: In Bagging, simple learning is implemented on a small population. It is a method of reducing prediction variance. Bagging produces additional data for training from datasets.
     
  • Boosting: Boosting classifies the population into various sets. It is an iterative process for adjusting an observation’s weight based on the previous classification.


6. What is  Polling in CNN?

Answer: There are times when we need to reduce the spatial dimension of a CNN. To achieve this, we use the Polling method, which consists of sliding a 2D filter over every particular channel of the feature map. The features are summarised in the region covered by the filter. It aids in sliding the filter matrix over the input matrix.
 

7. Can a validation set be compared with the test set?

Answer: A validation set is helpful for parameter selection. It is an essential part of data analysis. This data set finds and optimizes the best model to clarify a particular complication. They are also known as dev sets. While in the Test set, Initially, a data set is trained. After this process, a machine learning program is tested using a test set, i.e., it evaluates or tests the execution of instructed machine learning.


8. What do you mean by Vanishing gradients?

Answer: Vanishing gradients are detected using kernel weight distribution. Due to the massive number of layers of networks, the value of the derivative decreases, but at some point, the partial result of the loss function reaches a value close to zero, and the partial product disappears. Thus, this is a vanishing gradient problem. They can be resolved by residual neural networks , or ResNets.
 

9. What do you mean by A/B Testing?

Answer: It is a statistical hypothesis testing for an indiscriminate experiment using two variables, A and B. This kind of testing is also known as split testing. The main advantage of A/B testing is that it's beneficial for understanding user engagement and satisfaction with various online features. This testing improves user experiences by collecting data, constructing hypotheses, and understanding which optimization affects user experience. The A/B testing process includes steps such as.

  • Collecting data
     
  • Identifying goals
     
  • Generating test hypotheses
     
  • Creating different variations
     
  • Running experiments
     
  • Waiting for the results
     
  • Analyzing results 
     

10. What do you mean by the Activation function?

Answer: Activation functions are used in Neural Networks. This function plays a crucial role in deciding if a neuron is to be activated or not by doing the necessary computation. The activation function contains three layers: 

  • Input layer holds the input data, and no calculations are performed.
     
  • Hidden layer located between the input and output of the algorithm, allow us to model complex data using neurons.
     
  • Output layer is a layer in a neural network model that produces the result for a given input.

Conclusion

In this article, we have discussed easy, medium, and hard data science interview questions. To learn more, you can check out our other articles:
 

Check out related article on interview questions:

 

We hope this article helped you understand some standard interview questions for data science. You can also consider our online coding courses such as the Data Science Course to give your career an edge over others.

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