Last Updated on by Kumar Raja

Data Science Most Frequently Asked Interview Questions

Data Science can be interpreted as an interdisciplinary field that makes use of numerous techniques to extract hidden information from large sets of structured or unstructured formats of Big Data. Being interdisciplinary in nature, Data Science makes use of techniques in statistics, computer science, machine learning, deep learning, data visualization, and various other technologies to make interpretations from Big Data. In order to be a successful Data Scientist, one needs to develop skills in all these interdisciplinary areas and our Data Science Training in Hyderabad program helps you in this regard.  

Frequently Asked Interview Questions on Data Science:

  • How Do You Define the Term Data Science?

Data Science is an interdisciplinary that makes use of numerous scientific processes, statistical techniques algorithms, tools, and machine learning techniques to extract actionable insights from large sets of Big Data.

  • What are Some of the Techniques Used for Sampling?

Sampling techniques can be categorized into two different types based on the usage of statistics, they are

Probability Sampling Techniques: Clustered sampling, Simple random sampling, Stratified sampling.

Non-Probability Sampling Techniques: Quota sampling, Convenience sampling, snowball sampling, etc.

  • What do you Understand by Imbalanced Data?

If data is distributed unequally across different categories then it is termed as Imbalanced Data. Imbalanced Data would often result in causing errors in model performance and result in inaccurate results.

  • What do you understand by Survivorship Bias?

Survivorship Bias refers to the logical error while focusing on aspects that survived some process and overlooking those that did not work due to lack of prominence. Such type of bias will result in attaining wrong conclusions.

  • What are the Most Commonly Used Cross Validation Techniques?

The most commonly used techniques are:

  • K- Fold method
  • Leave p-out method
  • Leave-one-out method
  • Holdout method
  • Differentiate Test Set and Validation Set

Using the test set we will be evaluating the performance of the trained model. It evaluates the predictive power of the model.

The validation set is a part of the training set that is used to select parameters for avoiding model overfitting.

  • What Is Better – Random Forest Or Multiple Decision Trees?

When it comes to comparison, Random forest are preferred over decision trees as random forests are much more robust, accurate, and lesser prone to overfitting.

  • Is it Advisable to go with Dimensionality Reduction before Fitting a Support Vector Model?

If the features number is greater than observations then doing dimensionality reduction improves the SVM (Support Vector Model).

You can master real-world job centric skills in Data Science and prepare for the interview rounds in the best way possible with the help of our advanced Data Science Course in Hyderabad program by the domain experts.

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