Practical Data Science with Python 3: Synthesizing Actionable Insights from Data
暫譯: 使用 Python 3 的實用數據科學：從數據中提煉可行的見解

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local (multi-core CPU and GPU architectures) and distributed (in premise and cloud based) processing.
As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science, and how to document data engineering practices as shareable assets in an enterprise using OMG Essence.

This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Multi-agent Systems, Game Theory, Machine Learning and Security in Data Science.

Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.

What You'll Learn

• Play the role of a data scientist when completing increasingly challenging exercises using Python 3
• Work work with proven data science techniques/technologies
• Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data
• Apply theory of probability, statistical inference, and algebra to understand the data science practices
• Use OMG Essence to document data science methods/practices as shareable assets

Who This Book Is For
The primary audience of the book will be both newcomers in the field of data science, as well as those who would like to sharpen their computational skills to be more productive in their everyday work with data.