Hands-On Exploratory Data Analysis with Python - Suresh Kumar Mukhiya, Usman Ahmed

Understand how to shorten and deal with your data using Python tools, including PyPlot, NumPy, SciPy, and pandas. With the aid of this book titled “Hands-On Exploratory Data Analysis with Python, ” you understand the relevant information about EDA while demonstrating the ways of cleaning, preparing, and exploring the data and the methods of visualizing it. You will discover how to deal with the lack of data, significant differences, and correlations between diverse characteristics while using effective visualization instruments, such as Matplotlib and Seaborn, for your data representation.

Using the open source datasets, you will acquire practice in completing basic and intricate data analyses. You will learn a number of methods to perform calculations on your data and get the fundamental summary of the data and the distribution to more advanced techniques such as time series analysis. It is a companion that, as you advance toward the end of a chapter, directs you to apply EDA and related techniques for creating and testing your models to transform data into business insights.

At the end of this book, “Hands-On Exploratory Data Analysis with Python,” the reader will be equipped with skills in the preliminary analyses of data, data mining, and visualization of results. You will also learn how models can be built that could forecast future conditions, which positions you in a very good place once you step out, venturing into the professional world of data analysis. Cleaning data, realizing connections, and visualizing outcomes are explained in detail within this book, and it is aimed at becoming a perfect guide to EDA with Python for beginners.

Hands-On Exploratory Data Analysis with Python Table of Contents:

1. Exploratory Data Analysis Fundamentals
2. Visual Aids for EDA
3. EDA with Personal Email
4. Data Transformation
5. Descriptive Statistics
6. Grouping Dataset
7. Correlation
8. Time Series Analysis
9. Hypothesis Testing and Regression
10. Model Development and Evaluation
11. EDA on Wine Quality Data Analysis
12. Appendix

Who is this course for?

1. The intended audience is students who want to get information about data analysis.
2. Statisticians who would wish to increase their knowledge in analyzing data.
3. Data analysts are searching for useful methods for data analysis.
4. Data Scientists thus desire to improve their data exploration and how they visualize it.

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