Review new book: Practical Data Analysis

Hi all,

I was invited by the Packtpub team to review the book Practical Data Analysis by Hector Cuesta.  I started reading it for the last two weeks and I really enjoyed the topics approach covered by the book.

The book goes through the data science hot topics by presenting several practical examples of data exploration, analysis and even some machine learning techniques. Python as the main platform for the sample codes was a perfect choice, at my opinion. Since Python has becoming more popular at scientific community. 

The book brings several examples in  several science study fields such as stock prices, sentiment analysis on social networks,  biology modelling scenarios, social graphs,  MapReduce, text classification,  data visualisation, etc.  Many novel libraries and tools are presented including Numpy, Scipy, PIL, Python, Pandas, NLTK, IPython, Wakari ( I really liked a dedicated chapter for this excellent tool for scientific python environment on-line),  etc.  It also covers NoSQL databases such as MongoDB and visualisation libraries like D3.js.

I believe the biggest value proposition of this book is that it brings together in one book several tools and how they can be applied on data science. Many tools mentioned lacks further examples or documentation, which this book can assist any data scientist on this task.

However, the reader must not expect learn machine learning and data science in this book.  The theory is  scarce, and I believe it was not the main goal of the author for this book. For anyone looking to learn data science, this is not the right book. It is focused on who desires an extra resource for sample codes and inspirations, it will be a great pick!

The source code is available on Github, but you can explore them better explained inside the book, including illustrations. To sum up, I have to congratulate Hector for his effort writing this book. For the Scientific community, including the Python group, they will really enjoy this book! I really missed more material about scientific stack software installations, since for the beginners it can be really painful.  But in overall, it was well written focused on practical problems! A guide for any scientists.

For me the best chapters were the Chapter 6: Simulation of Stock Prices, the visualisation using D3.js was great, and the last chapter, 14, about On-line Data Analysis with IPython and Wakari. It's the first time I see Wakari covered in a book! Everyone who works with scientific Python today must give a chance some day to experiment this on-line tool! It's awesome!

Congratulations to PacktPub and Hector for the book!

Regards,

Marcel Caraciolo

Review about the book Learning IPython for Interactive Computing and Data Visualization

Hi all,

I was invited to review a copy of the book recently released titled "Learning IPython for Interactive Computing and Data Visualization" by the author Cyrille Rossant.  The book focus on one of the best tools for working with Python with the interactive incremented shell IPython.  By the way, it was the time to the tool receive a special book about it.

Learning IPython for Interactive Computing and Data Visualization

IPython is covered through the six chapters using several basic examples related to scientific computing  along with another Python tools such as Matplotlib, Numpy, Pandas, etc.  The first chapters explore the IPython basics such as installation, basic commands to get used with the tool.

The next chapters introduces NumPy and Pandas basics with the IPython shell active. Don't expect advanced examples with those tools. The idea is a simple demonstration of what we can do at IPython.

There is a chapter to discuss the visualization data with graphs, plottings with IPython Notebook. However I missed more details abot IPython notebook. It lacks more deep examples related to the topic.  

I really liked the chapter 5 when they showed some basics of MPI (Message Passing Theme), although the topic wasn't vasted explored. But the introduction gives a greate potential of usefulness to the more advanced books about IPython.

The last chapter shows how to create pluggins to IPython, for instance, create an simple extension that introduces a new cell magic (write C++ code directly in the cell, and it will be automatically compiled and executed).

My conclusion about the book is that it achieves the expected goal: a technical introduction to IPython. If you want a book to explore scientific computing or a advanced stuff to IPython, this is not the the book yet. I can say that this book is a first start for much more topics about the use of IPython.  MPI, IPython notebook, etc.  I recommend the book for start exploring the IPython as reference! :D

Regards,

Marcel

Review of the book Learning Scipy for Numerical and Scientific Computing

Hi all,

I've finished reading the book "Learning Scipy for Numerical and Scientific Computing".  This book comes to the scientific python series that PacktPub are bringing to the Python Developers! Congratulations!  As the title informs: it includes Scipy, Numpy and Matplotlib.  I only missed some further information about IPython, but it wasn't the goal of the book, so it goes well even leaved out.

It covers several important topics that are not as commonly covered, specially with several snippets illustrating special functions presented at Scipy library. For the developers it will be another great reference book to complement the native docs that comes with the library.  I enjoyed the author focused more on numerical analysis functions, it is one of the most used functions at the library.  

The bool also brings chapters on more specific applications: signal processing, data mining and computational geometry.  There is an extra chapter about the integration with another languages, but I found it not dense enough to explain those integrations. I really missed more start-off examples showing how to install the f2py or how to use Scipy with C/C++. 

Overall the Learning Scipy for Numerical and Scientific Computing book is a good book on Scipy covering  lots of mathematics with examples in Python. The book has a good size and it helps the scientists and scientific developers (by the way the non-developers will face some difficulties due to the heavy math that comes with the examples) to have a good overview on the library before exploring the reference material.

Thanks Kenny for the invitation to review this book, and congratulations to Francisco for bringing one more technical book for scientific python computing to the series!

Regards,

Marcel Caraciolo