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Hands-On Exploratory Data Analysis with Python

You're reading from   Hands-On Exploratory Data Analysis with Python Perform EDA techniques to understand, summarize, and investigate your data

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789537253
Length 352 pages
Edition 1st Edition
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Authors (2):
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Suresh Kumar Mukhiya Suresh Kumar Mukhiya
Author Profile Icon Suresh Kumar Mukhiya
Suresh Kumar Mukhiya
Usman Ahmed Usman Ahmed
Author Profile Icon Usman Ahmed
Usman Ahmed
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Table of Contents (17) Chapters Close

Preface 1. Section 1: The Fundamentals of EDA
2. Exploratory Data Analysis Fundamentals FREE CHAPTER 3. Visual Aids for EDA 4. EDA with Personal Email 5. Data Transformation 6. Section 2: Descriptive Statistics
7. Descriptive Statistics 8. Grouping Datasets 9. Correlation 10. Time Series Analysis 11. Section 3: Model Development and Evaluation
12. Hypothesis Testing and Regression 13. Model Development and Evaluation 14. EDA on Wine Quality Data Analysis 15. Other Books You May Enjoy Appendix

Understanding statistics

In data science, both qualitative and quantitative analyses are important aspects. In particular, the quantitative analysis of any dataset requires an understanding of statistical concepts. Statistics is a branch of mathematics that deals with collecting, organizing, and interpreting data. Hence, by using statistical concepts, we can understand the nature of the data, a summary of the dataset, and the type of distribution that the data has.

Distribution function

In order to understand the concept of the distribution function, it is essential to understand the concept of a continuous function. So, what do we mean when we refer to a continuous function? Basically, a continuous function is any function...

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