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Python Data Science Essentials
Key Features
Quickly get familiar with data science using Python 3.5
Save time (and effort) with all the essential tools explained
Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience
Book Description
Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow.
Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.
What you will learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data About the AuthorAlberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.
Luca Massaron is a data scientist and marketing research director specializing in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience of solving real-world problems and 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 a top ten Kaggler, he has always been very passionate about every aspect of data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, Luca believes that a lot can be achieved in data science just by doing the essentials.
Table of Contents First Steps Data Munging The Data Pipeline Machine Learning Social Network Analysis Visualization, Insights, and Results Strengthen Your Python Foundations565 pages, Kindle Edition
First published April 30, 2015
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139 people want to read
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Displaying 1 - 2 of 2 reviews
December 23, 2016
It's hard to explain this book, mostly because it's hard to get to whom it is targeted.
Is it targeted to people that already know Machine Learning and want to learn Python? No, the book goes into lengths into some algorithms and has "easy to write, but not quite right" code to do so.
Is it targeted to people that know Python but want to learn Machine Learning? No; even if some algorithms are explained in length, some aren't and there is very little "you use this when you have data like that" explanations. Actually, there is very little explanation on where an algorithm should be used.
Is it targeted to people that don't know Python and don't know Machine Learning and want to learn but? This is the gray area of the book: Again, the code is pretty simple and does not follow Python coding standards and the ML part is really shallow on the "when" and "why" sections.
In the end, the book is simply an extended version of Scikit-Learn manual -- and I even have doubts if the manual isn't better because it explains when an algorithm should be used.
Is it targeted to people that already know Machine Learning and want to learn Python? No, the book goes into lengths into some algorithms and has "easy to write, but not quite right" code to do so.
Is it targeted to people that know Python but want to learn Machine Learning? No; even if some algorithms are explained in length, some aren't and there is very little "you use this when you have data like that" explanations. Actually, there is very little explanation on where an algorithm should be used.
Is it targeted to people that don't know Python and don't know Machine Learning and want to learn but? This is the gray area of the book: Again, the code is pretty simple and does not follow Python coding standards and the ML part is really shallow on the "when" and "why" sections.
In the end, the book is simply an extended version of Scikit-Learn manual -- and I even have doubts if the manual isn't better because it explains when an algorithm should be used.
September 11, 2017
If someone contextualizes their practical challenges as guidelines, it is a great upper hand for the learners. Alberto Boschetti and Luca Massaron give advice with clearly set out boundaries to contextualisation to ensure readers can readily determine what is acceptable to the industry. This advice develops around scenarios, examples and codes of data science projects.
The authors are data scientists with expertise in statistics, linking with other sophisticated technical subject fields. This book has simplified the complexities that are relevant to beginners and intermediate data scientists with their understanding may have faced in using Python. In this book, users are recommended Python 3.4 or above for all its examples to practice.
The book engages and absorbs the reader into the subject matter involving almost all the human senses. The beauty of the book is that it has six chapters linked with resources (data and source codes). These resources are of immense value and will surely intrigue both beginners, and intermediate users. At the beginning of each chapter readers are able to clearly visualise what will be learnt during the chapter. The book gives more extensive knowledge about practical data mining principals through scientific methodology and effectively tests the performance of the user's machine learning hypothesis.
If the reader studies the book and completes the lab practice, it is a great chance to enhance user data manipulation and machine learning skills.
In this second edition, it is evident the authors have invested both time and effort, and have listened to user feedback to improve this particular edition. This edition displays more maturity and delivers more focus on updated and expanded content. Chapter four on Machine Learning in this second edition is an excellent move I think, as it’s one of the most widely used data science techniques with python.
Visualize the machine learning and optimisation processes the authors discuss in chapter 3, ‘The Data Pipeline’, and chapter 4, ‘Machine Learning’. If readers choose to get colour images of this book, there is the facility, and I am sure it is a bonus for the readers.
I recommend this book to all data science labs if they are dedicated to investing real industry experiences to successfully obtain their future research project deliverables.
This 354 page book is an excellent guide on learning data science through python for those aspiring to become experienced in it. It is also one of the few books that one will find truly practical and engaging.
Review by Prof. Kalum Priyanath Udagepola
Originally posted: http://www.bcs.org/content/conWebDoc/...
The authors are data scientists with expertise in statistics, linking with other sophisticated technical subject fields. This book has simplified the complexities that are relevant to beginners and intermediate data scientists with their understanding may have faced in using Python. In this book, users are recommended Python 3.4 or above for all its examples to practice.
The book engages and absorbs the reader into the subject matter involving almost all the human senses. The beauty of the book is that it has six chapters linked with resources (data and source codes). These resources are of immense value and will surely intrigue both beginners, and intermediate users. At the beginning of each chapter readers are able to clearly visualise what will be learnt during the chapter. The book gives more extensive knowledge about practical data mining principals through scientific methodology and effectively tests the performance of the user's machine learning hypothesis.
If the reader studies the book and completes the lab practice, it is a great chance to enhance user data manipulation and machine learning skills.
In this second edition, it is evident the authors have invested both time and effort, and have listened to user feedback to improve this particular edition. This edition displays more maturity and delivers more focus on updated and expanded content. Chapter four on Machine Learning in this second edition is an excellent move I think, as it’s one of the most widely used data science techniques with python.
Visualize the machine learning and optimisation processes the authors discuss in chapter 3, ‘The Data Pipeline’, and chapter 4, ‘Machine Learning’. If readers choose to get colour images of this book, there is the facility, and I am sure it is a bonus for the readers.
I recommend this book to all data science labs if they are dedicated to investing real industry experiences to successfully obtain their future research project deliverables.
This 354 page book is an excellent guide on learning data science through python for those aspiring to become experienced in it. It is also one of the few books that one will find truly practical and engaging.
Review by Prof. Kalum Priyanath Udagepola
Originally posted: http://www.bcs.org/content/conWebDoc/...
Displaying 1 - 2 of 2 reviews