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.
publication date
2022-01-21
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
key features
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
approach
This book has four parts. The first part helps to develop the technical, conceptual, and technological foundations. The second part will teach the readers the most common analytic goals. In the third part, readers will learn how to perform data preprocessing such as data cleansing and data integration. The fourth part goes through real-world case studies to help reader solidify their learnings.
audience
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.
short description
Whether you're a data analyst new to programming or already familiar with it, this book will teach you the optimum techniques for data preprocessing from both technical and analytical perspectives. You'll explore the world of advanced data manipulation and preprocessing techniques to create successful data analytic solutions.
subtitle
Learn how to effectively prepare data for successful data analytics
keywords
Data analysis, Python data analytics, Data Preprocessing, Data Cleaning, Data Masaging, Python
Product ISBN
9781801072137
