Flashcards

Data Preprocessing

Cleaning and transforming raw data

Techniques and tools used to clean and transform raw data to a usable format for analysis.

5 minutes 5 Questions

Data preprocessing is a crucial step in the Big Data analytics pipeline that involves transforming raw, messy data into a clean, structured format suitable for analysis. It encompasses several techniques aimed at enhancing data quality and utility. Firstly, data cleaning addresses issues like miss…

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Big Data Scientist - Data Preprocessing Example Questions

Test your knowledge of Data Preprocessing

Question 1

What is the purpose of data sampling in data preprocessing?

To rescale the data in the dataset To perform feature selection on the dataset To create new features from existing features To remove outliers from the dataset To create a subset of data for analysis To impute missing values in the dataset

Correct Answer: To create a subset of data for analysis

Data sampling is used to create a subset of data for analysis. This can be done to reduce the amount of data being processed or to focus on specific aspects of the data.

Question 2

What is feature scaling in data preprocessing?

The process of data imputation The process of detecting and removing outliers from a dataset The process of scaling the range of feature values to a similar range The process of feature selection The process of feature engineering The process of transforming continuous data to categorical data

Correct Answer: The process of scaling the range of feature values to a similar range

Feature scaling is the process of scaling the range of feature values to a similar range, typically between 0 and 1 or -1 and 1. This can make it easier to compare and interpret the features, and can also help improve the performance of some machine learning algorithms.

Question 3

What is data normalization in data preprocessing?

A way to perform dimensionality reduction A way to perform data imputation A way to detect and remove outliers from a dataset A way to scale features to a similar range of values A way to perform feature selection A way to transform continuous data to categorical data

Correct Answer: A way to scale features to a similar range of values

Data normalization is a way to scale features to a similar range of values, typically between 0 and 1 or -1 and 1. This makes it easier to compare and interpret the features, and also helps improve the performance of some machine learning algorithms.

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