Python for Data Science For Dummies (For Dummies (Computer/Tech)) 1st Edition

by John Paul Mueller (Author), Luca Massaron (Author)

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ISBN-13: 978-1118844182

ISBN-10: 1118844181

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Python for Data Science For Dummies
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Unleash the power of Python for your data analysis projects with For Dummies!

Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide.

Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models
Explains objects, functions, modules, and libraries and their role in data analysis
Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib

Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

John Paul Mueller
136
Paperback
48 offers from $12.97
Eric Matthes
6,433
Paperback
in Python Programming
75 offers from $14.92

John Paul Mueller
136
Paperback
48 offers from $12.97
Eric Matthes
6,433
Paperback
in Python Programming
75 offers from $14.92

Editorial Reviews

From the Back Cover

Learn to:

Take advantage of Python data analysis programming
Work with Python objects, functions, modules, and libraries
Apply statistical concepts such as probability and random distributions
Use NumPy, SciPy, Scikit-learn, and Pandas libraries

Wow 'em with your mastery of Python for managing and analyzing data!

Python is a programming language perfectly suited for data science. Even if it's brand new to you, this straightforward guide will help you learn to use Python programming to acquire, organize, process, and analyze large amounts of information and identify trends and patterns. From installing Python to performing cross-validation, learn with this book!

See why Python works for data science — tour the data science pipeline and learn about Python's basic capabilities
Get set up — install Python, download datasets and example code, and start working with numbers and logic, creating functions, and storing and indexing data
Make it visual — explore MatPlotLib and create graphs, pie and bar charts, histograms, and advanced scatterplots
Delve deeper — venture into classes and multiprocessing, define descriptive statistics for numeric data, and use applied visualization
Advanced data wrangling — examine solutions to dimensionality reduction, perform hierarchical clustering, and learn to detect outliers in your data
Make data tell you something — work with linear models and perform cross-validation, selection, and optimization

Open the book and find:

Fundamentals of Python data analysis programming
All about the Python development environment
How to use random distributions and regression models
Advice on accessing data from the web
What to do with NumPy, pandas, and SciPy
Tips on working with HTML pages
How to create interactive graphical representations
Ten essential data resources

To download the source code files for the examples in this book, go to
www.Dummies.com/extras/pythonfordatascience

About the Author

John Paul Mueller, consultant, application developer, writer, and technical editor, has written over 600 articles and 97 books. His topics range from programming to home security. Luca Massaron is a data scientist and a research director specializing in multivariate statistical analysis, machine learning, and customer insight. He is a pioneer of Web audience analysis in Italy and was named one of the top ten data scientists at competitions by kaggle.com.

Product details

Publisher ‏ : ‎ For Dummies; 1st edition (July 7, 2015)
Language ‏ : ‎ English
Paperback ‏ : ‎ 432 pages
ISBN-10 ‏ : ‎ 1118844181
ISBN-13 ‏ : ‎ 978-1118844182
Item Weight ‏ : ‎ 1.32 pounds
Dimensions ‏ : ‎ 7.3 x 1.1 x 9.1 inches

Best Sellers Rank: #1,469,817 in Books (See Top 100 in Books)
- #1,237 in Cloud Computing (Books)
- #1,666 in Python Programming
- #3,971 in Computer Programming Languages

Customer Reviews:
56 ratings

About the authors

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John Paul Mueller
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John Mueller is a freelance author and technical editor. He has writing in his blood, having produced 117 books and over 600 articles to date. He has also written his second children's book recently, Tail of the Wuggly Bump. His technical topics range from networking to artificial intelligence and from database management to heads down programming. His most recent book is "Machine Learning for Dummies, 2nd Edition." His technical editing skills have helped over 70 authors refine the content of their manuscripts. You can reach John on the Internet at John@JohnMuellerBooks.com and his Web site at: http://www.johnmuellerbooks.com. Make sure to read his blog at http://blog.johnmuellerbooks.com to obtain the latest book information, updates, and extra materials.
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Luca Massaron
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Luca Massaron is a data scientist and a research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten data scientist at competitions held by kaggle.com, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.
See more on the author's page

Customer reviews

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3 star		13%
2 star		9%
1 star		11%

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Top reviews from the United States

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Polka loves Polka Dancing

waste of money

Reviewed in the United States on May 22, 2018

Verified Purchase

This book is for python 2 not 3. That's okay, I have both versions but I couldn't get the examples in this book to work. It was really not helpful at all.

2 people found this helpful

Helpful

M. Athar

For really big dummies

Reviewed in the United States on June 28, 2016

Verified Purchase

I guess I've been a data scientist without knowing it because there have so far been very few new things introduced in this book
Lots of "padding" chapters. For example-
If you know a bit of python, you can skip section I. If you don't know python, you'll need another book for that!

The examples in section ii are really useful, but kind of basic. It leaves me wanting more

I skimmed section iii, since it was basically the helpfile to the software used in this book. The types of visuals are important to know, but very basic. I was hoping to encounter something new here!

I'm about to start section iv, but I'm not too hopeful.

5 people found this helpful

Helpful

obsolete and glossy

Reviewed in the United States on May 8, 2018

Verified Purchase

The book was published in 2015 and the code samples are obsolete (often using deprecated code). The explanations tend to gloss over the hard parts and dwell on the obvious.

2 people found this helpful

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Andres HOU

Good Book

Reviewed in the United States on January 15, 2019

Verified Purchase

Nice book to take you step by step, good shipment condition.

Helpful

Factual Actuals

Easy book to help you start understanding Data Science

Reviewed in the United States on January 6, 2016

Verified Purchase

I was taking Data Mining at my University when I decided that I wanted to get a bit of help and get an edge on the subject so I was ahead of the class. This book helped explain everything and even had the output of the code to show what each code block would do. I ended up searching around on the author's website and he does have all of the source code in a zip file. Easy book to help you start understanding Data Science.

7 people found this helpful

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Md Shah Alam

Two Stars

Reviewed in the United States on March 5, 2018

Verified Purchase

Need to explain with examples

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John A. Quandt

Five Stars

Reviewed in the United States on October 12, 2015

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It really helped me get up to speed quickly on numerical processing in Python 2.7

5 people found this helpful

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Chris

Nothing dummy about this book

Reviewed in the United States on May 29, 2017

Verified Purchase

There's nothing dummy about this book. Offers clear and useful explanations and code and gives you extra bowl results.

2 people found this helpful

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Top reviews from other countries

Joana

Chapter review basic python has gross errors

Reviewed in the United Kingdom on August 20, 2019

Verified Purchase

In table 4-1 the columns Operator and description have correct data but the column for the examples is full of errors.

Example " Myvar +=2 results in Myvar containing 7" is wrong.

Prof Ngo

Good

Reviewed in the United Kingdom on August 16, 2018

Verified Purchase

Good introductory book into data science with python.

Balaji MV

Excellent dummies book to get started on data science with python

Reviewed in India on March 6, 2019

Verified Purchase

Covers numpy, pandas, matplotlib and scikitlearn really well for a starter. For anyone looking to headstart in data science with python, this book is highly recommended

One person found this helpful

Trading Central

... books often come with the pretense of being a dumb downed guide for the intellectually challenged

Reviewed in Canada on August 29, 2015

Verified Purchase

Dummie books often come with the pretense of being a dumb downed guide for the intellectually challenged. This guide is quite the opposite providing a concise overview without the detailed mathematical notation of a PhD dissertation. Overviews are very useful for the busy practitioner too busy to deal with dogma of creating a peer reviewed chronicle that would never approach the size of readers that purchase books in this series.

In terms of specific information, enough detail is given to get going and begin the hands on process of developing workable python solutions. The synopsis of the new "data science" field is also covered so that the practitioner will not feel overwhelmed by semantics that frankly distract that analysis is analysis without all the hype of introducing new verbiage for age old problem solving.

So for the intellectually gifted or the newbie wanting just a good introductory overview to the emerging world of "data science" this is a good starting point and supplement to the other pedagogical texts encapsulated within the intellectual snobbery that comes from trying to impress one's peers versus providing useful information in a readable format.

2 people found this helpful

Oleksandr Yermolayev

Not clearly written

Reviewed in Germany on March 16, 2020

Verified Purchase

I would not recommend buying this book, as it is not written properly - lots of this are unclear or written as a cookbook.

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