May 1, 2024
Updated May 10, 2025
18 minute read
Data transformation is the process of changing the format, structure, or values of data. At a high level, it involves taking raw data, which might be messy, inconsistent, or in a format unsuitable for analysis, and converting it into a clean, organized, and usable form. This is a critical step in any data-driven workflow, ensuring that information is accurate, consistent, and ready for processing, analysis, or visualization. Imagine trying to bake a cake with ingredients in all sorts of different containers and measurements – some in grams, some in ounces, some liquid, some solid. Data transformation is like converting all those ingredients into a standard set of measurements and a consistent state so you can follow the recipe and bake a delicious cake.
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Find a path to becoming a Data Transformation. Learn more at:
OpenCourser.com/topic/uezht5/data
Reading list
We've selected five books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Transformation.
This comprehensive guide provides a step-by-step approach to data transformation, covering topics such as data quality assessment, data cleaning, data integration, and data standardization. It is ideal for both business and IT professionals who need to understand and implement data transformation projects.
This practical guide focuses on how to transform data for business intelligence applications. It covers data integration, data combining, and data harmonization. It valuable resource for data analysts and business intelligence professionals who need to transform data for reporting and analysis.
Focuses on using Python for data wrangling, which is an important aspect of data transformation. It covers data cleaning, data transformation, and data visualization. It is ideal for data scientists and data analysts who need to use Python for data wrangling tasks.
Discusses how to use data to drive business transformation, which key goal of data transformation. It covers data-driven decision-making, data-driven innovation, and data-driven business models. It is ideal for business leaders and entrepreneurs who need to understand how to use data to transform their businesses.
Provides a practical guide to data transformation using Spark, which popular big data processing engine. It covers data ingestion, data cleaning, data transformation, and data integration. It is ideal for data engineers and data analysts who need to use Spark for data transformation tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/uezht5/data
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