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Hands-On Exploratory Data Analysis with Python - Perform EDA techniques to understand, summarize, and investigate your data (Paperback)
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Hands-On Exploratory Data Analysis with Python - Perform EDA techniques to understand, summarize, and investigate your data (Paperback)
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Discover techniques to summarize the characteristics of your data
using PyPlot, NumPy, SciPy, and pandas Key Features Understand the
fundamental concepts of exploratory data analysis using Python Find
missing values in your data and identify the correlation between
different variables Practice graphical exploratory analysis
techniques using Matplotlib and the Seaborn Python package Book
DescriptionExploratory 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. What you will learn Import, clean, and
explore data to perform preliminary analysis using powerful Python
packages Identify and transform erroneous data using different data
wrangling techniques Explore the use of multiple regression to
describe non-linear relationships Discover hypothesis testing and
explore techniques of time-series analysis Understand and interpret
results obtained from graphical analysis Build, train, and optimize
predictive models to estimate results Perform complex EDA
techniques on open source datasets Who this book is forThis EDA
book is for anyone interested in data analysis, especially
students, statisticians, data analysts, and data scientists. The
practical concepts presented in this book can be applied in various
disciplines to enhance decision-making processes with data analysis
and synthesis. Fundamental knowledge of Python programming and
statistical concepts is all you need to get started with this book.
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