Python Data Science Essentials: Edition 2

Alberto Boschetti · Luca Massaron

ott 2016 · Packt Publishing Ltd

Informazioni su questo ebook

Become an efficient data science practitioner by understanding Python's key conceptsAbout This BookQuickly get familiar with data science using Python 3.5Save time (and effort) with all the essential tools explainedCreate effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experienceWho This Book Is For

If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

What You Will LearnSet up your data science toolbox using a Python scientific environment on Windows, Mac, and LinuxGet data ready for your data science projectManipulate, fix, and explore data in order to solve data science problemsSet up an experimental pipeline to test your data science hypothesesChoose the most effective and scalable learning algorithm for your data science tasksOptimize your machine learning models to get the best performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataIn Detail

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.

Style and approach

The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Informazioni sull'autore

Alberto 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.