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Key benefits

Develop the skills to perform data cleaning, data integration, data reduction, and data transformation

Make the most of your raw data with powerful data transformation and massaging techniques

Perform thorough data cleaning, including dealing with missing values and outliers

Description

Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who’s developed college-level courses on data preprocessing and related subjects. With this book, you’ll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you’ll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.

Who is this book for?

This book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don’t need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.

What you will learn

Use Python to perform analytics functions on your data

Understand the role of databases and how to effectively pull data from databases

Perform data preprocessing steps defined by your analytics goals

Recognize and resolve data integration challenges

Identify the need for data reduction and execute it

Detect opportunities to improve analytics with data transformation

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Rodrigo Oct 18, 2023

Its really practical and its explanations are quite clear. I like that this uses different techniques that could be applied to real problems.

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Machiel Kruger Feb 22, 2024

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Young Apr 04, 2022

Totally worth reading. There are lots of example code and charts within the book which make every concept hands-on.

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Javad Seif Mar 28, 2022

In almost every problem-solving subject in schools, the data and all the probabilities are "given." In the real world, however, you need to process whatever data that is available to you, *before* you begin solving a problem. You need to prepare those givens. That is indeed the gap between academia and industry. I highly recommend this book by Dr. Roy Jafari, which addresses this very problem. It gives the reader all the necessary background, introduces the highly important aspects of ML and walks the reader through various scenarios for data preprocessing. It is really great that Python is used as the programming language because not only Python is probably the easiest language to follow, but also this book serves as a great resource for improving the reader's programming skills. I have just started reading this book but I have fully examined its outline and sampled various parts of it. I will edit this review with more detailed descriptions once I finish reading it.

Heather Feb 12, 2022

I found this book to be an all encompassing guide on how to properly sort, preprocess, prepare, and use your datasets. Whether you are new to python and data analytics or a seasoned professional, I believe this book has value for any skillset. The level of detail in each chapter really aides in a wholistic understanding of each topic that takes the reader's learning further than just memorization, allowing the reader to have a complete understanding of the "why" behind each concept in the book. In particular, I found it extremely helpful that nearly half the book focused on data preprocessing methods when other textbooks would have covered this in too few of pages. Data preprocessing is truly the most important step in any analysis and this book definitely highlights the importance of the strong foundation.My favorite part of the book were the hands on exercises at the end of each chapter and case studies at the end of the book. I am a hands on learner, and these really helped me test each concept and fully grow as a data enthusiast. I highly recommend this book as an addition to anyone's library!

Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics

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