May 1, 2024
Updated May 9, 2025
17 minute read
Data preparation, also known as data preprocessing or data wrangling, is the crucial process of cleaning, transforming, and organizing raw data into a suitable format for analysis, machine learning, or other data processing tasks. Think of it as the meticulous work a chef undertakes before cooking – chopping vegetables, measuring ingredients, and ensuring everything is ready for the main culinary event. Similarly, data preparation ensures that the "ingredients" – the data – are of high quality and properly structured to yield meaningful insights. Without this foundational step, even the most sophisticated analytical tools or algorithms can produce flawed or misleading results.
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Reading list
We've selected 30 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 Preparation.
This book, written by the creator of the pandas library, practical introduction to the tools needed for data manipulation, cleaning, and preparation in Python. It is highly relevant for anyone working with data in Python and serves as an excellent resource for both beginners and those looking to solidify their understanding of using pandas and NumPy for data preparation tasks. is widely used and considered a standard reference in the field.
An excellent resource for those using R, this book provides a comprehensive introduction to data wrangling, transformation, and visualization using the tidyverse suite of packages. It fundamental text for anyone learning data science with R, covering essential data preparation steps. is often used as a textbook in introductory data science courses.
Feature engineering critical part of data preparation for machine learning. focuses specifically on this topic, providing techniques and strategies for creating effective features from raw data. It valuable resource for anyone working on machine learning projects.
Focuses on practical data preprocessing specifically for machine learning applications using popular Python libraries like scikit-learn and pandas. It's highly relevant for those preparing data for modeling and provides hands-on examples. This book is particularly useful for students and professionals in the machine learning domain.
Combines practical Python data wrangling techniques with a focus on data quality. It covers using Python to read, write, and transform data while addressing quality issues. It's a practical resource for those who want to integrate data quality checks into their Python data preparation workflows.
Offers a broader perspective on data wrangling principles beyond specific tools. It delves into the process and techniques for preparing data effectively, regardless of the software or language used. It's valuable for gaining a solid understanding of the underlying concepts of data preparation. This book is suitable for both students and professionals seeking a deeper understanding of data wrangling methodologies.
This handbook takes a pragmatic approach to dealing with messy, real-world data. It provides a collection of techniques and war stories for handling various data quality issues. It valuable resource for practitioners who encounter challenging data problems regularly and offers practical solutions and insights.
Provides a comprehensive guide to best practices for data cleaning, covering steps to take before and after data collection. It emphasizes the importance of a systematic approach to ensure data quality. It valuable resource for researchers and data practitioners focused on rigorous data handling.
Provides practical tips and tools for data wrangling using Python. It is suitable for beginners and those who want to improve their efficiency in handling messy data with Python. It covers various techniques for cleaning, transforming, and reshaping data.
Focuses on data preparation for machine learning. It covers a wide range of topics, including data cleaning, feature engineering, and data transformation. It valuable resource for data scientists and other professionals who work with machine learning.
Focuses on data preparation for computer vision. It covers a wide range of topics, including data cleaning, data augmentation, and data transformation. It valuable resource for data scientists and other professionals who work with computer vision.
Focuses on data preparation for Spark. It covers a wide range of topics, including data cleaning, data integration, and data transformation. It valuable resource for data engineers and other professionals who work with Spark.
While not solely focused on data preparation, this book provides essential context by explaining the overall data-analytic thinking process and where data preparation fits in. It helps readers understand the business value of data and the importance of quality data for effective analysis and decision-making.
Focuses on data preparation for data mining. It covers a wide range of topics, including data cleaning, data integration, and data transformation. It valuable resource for data miners and other professionals who work with data mining.
Focuses on data preparation for business intelligence. It covers a wide range of topics, including data cleaning, data integration, and data transformation. It valuable resource for business intelligence professionals and other professionals who work with data.
Considered a classic in the field of statistical learning and data mining, this book covers various techniques that often require significant data preparation. While mathematically rigorous, it provides foundational knowledge on concepts like feature engineering and data transformation. It is more suitable for graduate students and researchers with a strong mathematical background.
While not directly about data preparation techniques, this book emphasizes the importance of writing clean, maintainable, and readable code. This is crucial for building robust data preparation pipelines and ensuring reproducibility. It's a foundational book for anyone involved in writing code for data tasks.
While focused on data visualization, this book reinforces the need for clean and well-prepared data to create effective visualizations. Understanding how data will be presented can inform the data preparation process. It's a valuable complementary read for anyone involved in presenting data insights.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/q2sl7q/data
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