Python Data Science Essentials
By Alberto Boschetti, Luca Massaron
Publisher: Packt Publishing
Final Release Date: April 2015
Pages: 258

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results.

In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

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5.0

Must to read and have it on your bookshelf

By Oleg O.

from Hamburg, Germany

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  • Well-written

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    Comments about oreilly Python Data Science Essentials:

    Although I am an experienced Data Scientist who knows well Python's stack for Data Science (scikit-learn, pandas, statsmodels, numpy, scipy, matplotlib, IPython), this book captured my attention and I have read a half of it during the first two days after getting the book. This book is easy to read for novices and experts alike (it does not contain a lot of math and wherever there are formulas they are not difficult to grasp), though some familiarity with Python packages comprising the Data Science stack will greatly facilitate material understanding. The writing style authors chose is excellent as it teaches readers in a very logical and pedagogically appealing way: the way of data pre-processing and analysis occur in projects that data scientists and engineers often encounter when aiming to solve the real-worlds tasks.

    The books begins with a description of how to install Python and various packages needed to run the code. The purpose of these packages is also explained. Different Python distributions are briefly discussed together with their characteristics, so that a reader can select a distribution particularly suitable to his/her needs. As all code examples in the book are run in IPython Notebook, special attention is paid to a short but comprehensive introduction into IPython itself. Data sets used in the book are described too.

    After advising on installation of Python and its packages, the book guides readers towards fast and easy data loading from a file, including the case when the entire data set cannot be loaded during one read in the memory and the solution offered is to load it in chunks by using pandas.
    Furthermore, answers to the following problems are provided: how to deal with erroneous records, how to treat categorical and text data, what are useful data cleansing and transformation operations implemented in pandas, how to use the optimized data structures - numpy arrays - and what operations on them can be done.

    Once data is loaded and converted to a suitable representation, the book then spends a chapter on the general Data Science pipeline that can be implemented with scikit-learn. The pipeline includes dimensionality reduction via either feature extraction or feature selection, outlier detection, predictive modeling (classification and regression), optimization of model's hyper-parameters, and model's performance evaluation. This material creates the holistic view what typical data analysis is comprised of.

    The next chapter introduces several popular machine learning algorithms in detail. Among them are linear and logistic regression, Naive Bayes, support vector machines, bagging and boosting ensembles. Special attention is paid to scikit-learn solutions of the 3Vs of big data: namely, volume, velocity and variety. Scalability with volume is solved with incremental learning when at any given moment of time, only a portion (batch) of the entire data fit to the available memory is used to update a model, hence, a model learns incrementally as new batches arrive. To keep up with velocity, scikit-learn offers a number of classification and regression algorithms optimized for speed. Data variety is deal with the help of hashing and sparse matrices. The chapter ends with short examples of doing basic operations of Natural Language Processing with the NLTK package and data clustering.

    Final two chapters are devoted to social network analysis with the NetworkX package and data visualization with the matplotlib and pandas packages, respectively.

    Although I have both paper and electronic versions of this book, I would advise first to buy the paper version as numerous code is much easier to understand in this format because one can see the entire snapshot at once.

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