Python Data Science Handbook: Essential Tools for Working with Data

I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super great depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science. The book is written with Jupyter Notebooks so it is easy to follow along and try code from the book in your own notebook.

When I first received this book, I was surprised that it didn't get to scikit-learn until the last third of the book. The first third is about numpy and pandas, and the middle third is about matplotlib. Now that I've been applying it at work, however, I've found that the items covered in the first two thirds were really essential. I wouldn't be nearly as productive if I had just jumped straight to the sections on scikit-learn. The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems. You will find yourself going back to use this book as a reference.

I am currently taking a Machine Learning course from Udacity and this book has proven to be a great reference guide for several projects and quizes. Although it does not go in depth in regards to machine learning (although almost half of the book is dedicated to it), it does give an understanding of essential concepts. For those interested in machine learning I would recommend bying "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Geron as well as this book. There is no one book for data science, and this one is no exception. Just keep that in mind before buying it. Other than that, I am really happy with my purchase. P.S. For those complaining about black and white graphs and diagrams - check the author's GitHub.

I've just finished this book. The author gives a well-written introduction into Machine-Learning with Python Scikit-Learn and illustrates each chapter with well-designed examples that are easy to follow and understand. I am very pleased with this book and can definitely recommend to everyone who is a beginner in the field and wants to quickly get hold on the practical approaches to ML.

This is by far the best book out in market to get you started with using python for data science. You will need some basic understanding of python and machine learning to understand concepts here, but this book will definitely take you skill to next level.This is no-nonsense book and goes deep into stuff which are relevant and important to do data science in python, every page is rich in information and provides practical use case, optimization tricks and adds new dimensions to your understanding of topic.

This is an excellent reference book for people working with data science. Remember, 80% of the effort in machine learning, data analysis or data science in general is about processing data and understanding data. This book is for that purpose and I think it's the best book out there about data processing, analysis and visualization using python. If you look for hardcore machine learning, go for other books. Highly recommended!

Bought this book for two reasons: 1. It's the best book I've found on data science related capabilities in Python, including great coverage of NumPy, Pandas, etc. 2. The author has made this book available for free on the web - that generosity deserves to be supported.

Not as easy or straightforward as "Python for Data Analysis " but because it uses Jupyter it's easy on the eyes to read. Highly recommend going to Staples or Office Max and getting the book spiral bound.

Ich erkläre zunächst meinen eigenen Background und darauf aufbauend, was ich an anderen Python-Büchern/Tutorials vermisst habe: Ich bin promovierter Statistiker mit langjähriger Erfahrung in R und arbeite seit etwas mehr als 2 Jahren mit Linux. Shell-Skills (bash) sind zwar vorhanden, aber definitiv noch ausbaufähig. Ich stehe am Anfang einer Data Science-Karriere in der Industrie. Da Data Science nach meinem Verständnis aus Computer Science + Statistik + epsilon besteht und da ich einen starken Mathematik/Statistik-Background habe, möchte ich meine Programmier-Skills verbessern. Dazu gehört das Erlernen weiterer Programmiersprachen wie Python und C++. Mein Ziel: Lerne Datenanalyse in Python. Insbesondere NumPy, SciPy, Pandas und Matplotlib. Dies ist nicht mein erstes Python-Buch. Was mir an anderen Büchern/Onlinetutorien aufgefallen ist, dass diese oft auf Computer Scientists (Informatiker) zugeschnitten sind. Es war regelmäßig frustrierend, wenn kleine Details nicht erklärt wurden, die für Informatiker selbstverständlich sind. Das Buch "Python Data Science Handbook" ist anders. Es erklärt vieles, was für einen Nicht-Informatiker nicht selbstverständlich ist. Insbesondere ist das erste Kapitel wertvoll für einen Statistiker wie mich. Es erklärt detailliert, wie man mit ipython in einer Shell arbeitet. Fazit: Für Informatiker, die tiefes Verständnis für Python aufbauen wollen, sind andere Bücher empfehlenswert. Wenn man dagegen Grundkenntnisse in Python mitbringt und hauptsächlich an der Datenanalyse in Python interessiert ist, kann ich dieses Buch herzlichst empfehlen.

This book contains introductions, tips and overview of the five more common Python packages for data science. It is clear, concise and quite fun to read. Only one down side, which is quite minor: some graphics needs colour. This is not a big deal because you can check the online version which available for free.

I really hoped that this would be better. There are a few errors that take a while to work out. Some of the key concepts are completely skipped over. Constantly find myself losing interest in the topics, which could flow a lot better. I had hoped for something like Hadleys book R for Data science, but this is far from it. Wouldn't bother buying.

Has a lot of information to absorb. Read the whole thing to become a star in Python!!

Atendeu às minhas expectativas atuais e será útil em trabalhos futuros.