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.