IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

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What is “data wrangling”?

The process of developing machine learning algorithms

The process of cleaning and converting raw data into a usable format

Data wrangling refers to the process of cleaning and converting raw data into a usable format, making it a crucial step in the data preparation phase of data analysis. This process involves various tasks, such as removing duplicates, handling missing values, standardizing data formats, and restructuring datasets to suit the needs of analysis or modeling. By transforming messy and unstructured data into a structured and accessible format, data wrangling enables data scientists and analysts to derive meaningful insights and make informed decisions based on accurate data.

The aspects of developing machine learning algorithms, generating synthetic data for models, and extracting and loading data into databases address different stages of the data workflow or tasks within data management, but do not encompass the comprehensive and essential actions inherent to data wrangling specifically.

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The generation of synthetic data for models

The extraction and loading of data into databases

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