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Leverage the power of Python to clean, scrape,
analyze, and visualize your data
A course in three modules
BIRMINGHAM - MUMBAI
Python: End-to-end Data Analysis
Copyright © 2016 Packt Publishing
All rights reserved. No part of this course may be reproduced, stored in a retrieval
system, or transmitted in any form or by any means, without the prior written
permission of the publisher, except in the case of brief quotations embedded in
critical articles or reviews.
Every effort has been made in the preparation of this course to ensure the accuracy
of the information presented. However, the information contained in this course
is sold without warranty, either express or implied. Neither the authors, nor Packt
Publishing, and its dealers and distributors will be held liable for any damages
caused or alleged to be caused directly or indirectly by this course.
Packt Publishing has endeavored to provide trademark information about all of the
companies and products mentioned in this course by the appropriate use of capitals.
However, Packt Publishing cannot guarantee the accuracy of this information.
Published on: May 2017
Production reference: 1050517
Published by Packt Publishing Ltd.
35 Livery Street
Birmingham B3 2PB, UK.
Luiz Felipe Martins
Hai Minh Nguyen
Kenneth Emeka Odoh
Hang (Harvey) Yu
Content Development Editor
[ i ]
The use of Python for data analysis and visualization has only increased in
popularity in the
last few years.
The aim of this book is to develop skills to effectively approach almost any data
analysis problem, and extract all of the available information. This is done by
introducing a range of varying techniques and methods such as uni- and multi-
variate linear regression, cluster finding, Bayesian analysis, machine learning, and
time series analysis. Exploratory data analysis is a key aspect to get a sense of what
can be done and to maximize the insights that are gained from the data. Additionally,
emphasis is put on presentation-ready figures that are clear and easy to interpret.
What this learning path covers
Module 1, Getting Started with Python Data Analysis, shows how to work with time-
oriented data in Pandas. How do you clean, inspect, reshape, merge, or group data
– these are the concerns in this chapter. The library of choice in the course will be
Module 2, Python Data Analysis Cookbook, demonstrates how to visualize
data and mentions frequently encountered pitfalls. Also, discusses
statistical probability distributions and correlation between two variables.
Module 3, Mastering Python Data Analysis, introduces linear, multiple, and logistic
regression with in-depth examples of using SciPy and stats models packages to test
various hypotheses of relationships between variables.
PS D:\python> python3 -m pip install --upgrade pip PS D:\python> pip --version pip 23.0.1 from c:\users\黄迪涛\appdata\local\programs\python\python38-32\lib\site-packages\pip (python 3.8)
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