Python Data Science Essentials - Second Edition: Learn the fundamentals of Data Science with Python

Python Data Science Essentials - Second Edition: Learn the fundamentals of Data Science with Python

by Luca Massaron, Alberto Boschetti


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About This Book

  • 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

Who 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 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 hypothesis
  • 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

In Detail

As the second edition of Python Data Science Essentials, this book offers updated and expanded content. Based on the recent Jupyter notebooks (based on interchangeable kernels, a truly polyglot data science system), this book incorporates all the main recent improvements in Numpy, pandas, and Scikit-learn. Additionally, it offers new content on deep learning (by presenting Keras - based on both Theano and Tensorflow), on beautiful visualizations (seaborn and ggplot), and on web deployment (using bottle).

This book starts by explaining how to set up your essential data science toolbox in Python's latest version, 3.5, using a single source approach (implying that the code in this book will be easily reusable in Python 2.7 as well). Then, it will guide you through all the data munging and preprocessing phases.

Finally, it will complete the overview by presenting you with the principal machine learning algorithms, graph analysis technicalities, and visualization and deployment instruments.

Product Details

ISBN-13: 9781786462138
Publisher: Packt Publishing
Publication date: 10/28/2016
Pages: 378
Product dimensions: 7.50(w) x 9.25(h) x 0.78(d)

About the Author

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.

Table of Contents

  1. First Steps
  2. Introducing data science and Python
  3. Installing Python
  4. Introducing Jupyter
  5. Datasets and code used in the book
  6. Summary
  7. Data Munging
  8. The Data Pipeline
  9. Machine Learning
  10. Social Network Analysis
  11. Visualization, Insights, and Results

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