Data Science Interview Questions

 


Top Data Science interview question

1. How to get rid Overfitting and Underfitting?

To get rid of overfitting and underfitting, you can resample the data to estimate the model accuracy (k-fold cross-validation) and have a validation dataset to evaluate the model.

This interview questions for data science tests your ability to solve problems.

  • 2. What is the reason behind cleaning playing a vital role in analysis?

When number of data sources increases, the time it takes to clean the data increases exponentially.

When number of data sources increases, the time it takes to clean the data increases exponentially.

A large amount of data is challenging to handle and consumes a lot of time. It might take up to as much as 80% of the time to just clean data. Hence, it is a critical part of the analysis task.

This interview questions for data science tests your theoretical knowledge of the subject.

  • 3. What are the main components of the Hadoop Framework?

HDFS and YARN are basically the two primary components of the Hadoop framework. HDFS: Stands for Hadoop Distributed File System. It is the distributed database working on top of Hadoop. It is capable of storing and retrieving the bulk of datasets in no time.

Yarn: Stands for Yet Another Resource Negotiator. It allocates resources dynamically and handles the workloads.

This question has been mentioned in the data science interview questions GitHub.

  • 4. What is Collaborative Filtering?

It is the process of filtering used by most recommender systems to find patterns and information by collaborating perspectives, numerous data sources, and several agents.

  • 5. What is Survivorship Bias?

It is the logical error. It considers the aspects that support surviving some process and casually overlooking those that did not work because of their lack of prominence.


  • 6. What Is the Cost Function?

Also referred to as "loss" or "error," the cost function is a means to calculate the model's performance. It is used to evaluate the level of error of the output layer during backpropagation.

These data scientist interview questions are frequently asked in the data science interview.

  • 7. What Are Hyperparameters?

A parameter having its value is set before the learning process begins is termed as a hyperparameter. It points out how a network is trained and the network structure.

  • 8. What is the Computational Graph?

Everything in TensorFlow revolves around the creation of a computational graph. It has a network of operational nodes which represent mathematical operations.

This interview questions for data science tests your basic knowledge about the course of the subject.

  • 9. How are missing values and impossible values represented in R?

One of the central problems when working with real data is handling missing values. NA represents these in R. Impossible values (division by 0, for example) are represented by NAN (not a number).

Few other questions are:

  • 10. How do you create a table in R?

Out of the many options available, using the various available packages meant for making tables is the easiest way.

The packages that one can use are:

  • gt
  • kableExtra
  • formattable
  • DT
  • Reactable
  • flextable
  • huxtable

This question has been mentioned in the data science interview questions GitHub.

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