Learn Data Cleaning and Preprocessing for Data Science with This Free eBook

In this free ebook, readers will learn how to employ data cleaning and preprocessing for data science using the Python ecosystem.

Learn Data Cleaning and Preprocessing for Data Science with This Free eBook

Data Science Horizons recently released an insightful new ebook titled Data Cleaning and Preprocessing for Data Science Beginners that provides a comprehensive introduction to these critical early stages of the data science pipeline. In the guide, readers will learn why properly cleaning and preprocessing data is so important for building effective predictive models and drawing reliable conclusions from analyses. The ebook covers the general workflow of collecting, cleaning, integrating, transforming, and reducing data in preparation for analysis. It also explores the iterative nature of data cleaning and preprocessing that makes this process as much an art as it is a science.

Why is such a book needed?


In essence, data is messy. Real-world data, the kind that companies and organizations collect every day, is filled with inaccuracies, inconsistencies, and missing entries. As the saying goes, "Garbage in, garbage out." If we feed our predictive models with dirty, inaccurate data, the performance and accuracy of our models will be compromised


A major highlight of the ebook is the hands-on demonstration of key Python libraries used for data manipulation, visualization, machine learning, and handling missing values. Readers will become familiar with essential tools like Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and Missingno. The guide concludes with a case study that enables readers to apply all of the concepts and skills covered in the previous chapters.

Data Cleaning and Preprocessing provides a comprehensive guide to tackling common data quality issues. It explores techniques for handling missing values, detecting outliers, normalizing and scaling data, selecting features, encoding variables, and balancing imbalanced datasets. Readers will learn best practices for assessing data integrity, merging datasets, and handling skewed distributions and nonlinear relationships. With its Python code examples, readers will gain practical experience identifying data anomalies, imputing missing data, extracting features, and preprocessing messy datasets into a form ready for analysis. The case study ties together all the major concepts into an end-to-end data cleaning and preprocessing workflow.


At the heart of a data scientist's toolkit is the ability to identify common data quality issues.


Data Cleaning and Preprocessing for Data Science Beginners is a great place to start for anyone eager to get into data science, but still needing to get the hang of dealing with real-world data in all its messy, imperfect glory. This guide really takes you through the nitty-gritty of getting raw data into tip-top shape so you can actually get somewhere with it. By the time you reach the end, you'll have all the know-how you need to clean and preprocess data like it's second nature. No more getting bogged down by wonky, error-filled data! With the skills this ebook arms you with, you'll be able to wrangle even the most unruly datasets into submission and extract meaningful insights like a pro.

Whether you're new to the field or looking to level up your skills, Data Cleaning and Preprocessing for Data Science Beginners is an invaluable addition to your data science library.

Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.