Book Image

Hands-On Data Preprocessing in Python

By : Roy Jafari
5 (2)
Book Image

Hands-On Data Preprocessing in Python

5 (2)
By: Roy Jafari

Overview of this book

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.
Table of Contents (24 chapters)
1
Part 1:Technical Needs
6
Part 2: Analytic Goals
11
Part 3: The Preprocessing
18
Part 4: Case Studies

Summary

Congratulations on your excellent progress in this chapter and this book! By finishing this chapter, you have also finished the second part of this book. In this chapter, we learned about clustering analysis and some techniques we can use to perform it. In this part of this book, we learned about the four most in-demand data analytics goals: data visualization, prediction, classification, and clustering.

In the first part of this book, you learned about data and databases, as well as programming skills that allow you to effectively manipulate data for data analytics. In the second part, which is the one you just finished, you learned about the four most important data analytics goals and learned how they can be met using programming.

Now, you are ready to take on the next challenge: learning how to effectively preprocess data for the data analytics goals you just learned about in the second part of this book using your programming skills, your fundamental understanding...