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Hands-On Exploratory Data Analysis with Python

Hands-On Exploratory Data Analysis with Python

By : Kumar Mukhiya, Ahmed
2.5 (2)
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Hands-On Exploratory Data Analysis with Python

Hands-On Exploratory Data Analysis with Python

2.5 (2)
By: Kumar Mukhiya, Ahmed

Overview of this book

Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.
Table of Contents (17 chapters)
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1
Section 1: The Fundamentals of EDA
6
Section 2: Descriptive Statistics
11
Section 3: Model Development and Evaluation

Summary

Portraying any data, events, concepts, information, processes, or methods graphically has been always perceived with a high degree of comprehension on one hand and is easily marketable on the other. Presenting results to stakeholders is very complex in the sense that our audience may not be technical enough to understand programming jargon and technicalities. Hence, visual aids are widely used. In this chapter, we discussed how to use such data visualization tools.

In the next chapter, we are going to get started with exploratory data analysis in a very simple way. We will try to analyze our mailbox and analyze what type of emails we send and receive.

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