Python Data Science Cookbook

Book Description

Over 60 practical recipes to help you explore Python and its robust data science capabilities

About This Book

• The book is packed with simple and concise Python code examples to effectively demonstrate advanced concepts in action
• Explore concepts such as programming, data mining, data analysis, data visualization, and machine learning using Python
• Get up to speed on machine learning algorithms with the help of easy-to-follow, insightful recipes

Who This Book Is For

This book is intended for all levels of Data Science professionals, both students and practitioners, starting from novice to experts. Novices can spend their time in the first five chapters getting themselves acquainted with Data Science. Experts can refer to the chapters starting from 6 to understand how advanced techniques are implemented using Python. People from non-Python backgrounds can also effectively use this book, but it would be helpful if you have some prior basic programming experience.

What You Will Learn

• Explore the complete range of Data Science algorithms
• Get to know the tricks used by industry engineers to create the most accurate data science models
• Manage and use Python libraries such as numpy, scipy, scikit learn, and matplotlib effectively
• Create meaningful features to solve real-world problems
• Take a look at Advanced Regression methods for model building and variable selection
• Get a thorough understanding of the underlying concepts and implementation of Ensemble methods
• Solve real-world problems using a variety of different datasets from numerical and text data modalities
• Get accustomed to modern state-of-the art algorithms such as Gradient Boosting, Random Forest, Rotation Forest, and so on

In Detail

Python is increasingly becoming the language for data science. It is overtaking R in terms of adoption, it is widely known by many developers, and has a strong set of libraries such as Numpy, Pandas, scikit-learn, Matplotlib, Ipython and Scipy, to support its usage in this field. Data Science is the emerging new hot tech field, which is an amalgamation of different disciplines including statistics, machine learning, and computer science. It’s a disruptive technology changing the face of today’s business and altering the economy of various verticals including retail, manufacturing, online ventures, and hospitality, to name a few, in a big way.

This book will walk you through the various steps, starting from simple to the most complex algorithms available in the Data Science arsenal, to effectively mine data and derive intelligence from it. At every step, we provide simple and efficient Python recipes that will not only show you how to implement these algorithms, but also clarify the underlying concept thoroughly.

The book begins by introducing you to using Python for Data Science, followed by working with Python environments. You will then learn how to analyse your data with Python. The book then teaches you the concepts of data mining followed by an extensive coverage of machine learning methods. It introduces you to a number of Python libraries available to help implement machine learning and data mining routines effectively. It also covers the principles of shrinkage, ensemble methods, random forest, rotation forest, and extreme trees, which are a must-have for any successful Data Science Professional.

Style and approach

This is a step-by-step recipe-based approach to Data Science algorithms, introducing the math philosophy behind these algorithms.

Table of Contents

Chapter 1. Python for Data Science
Chapter 2. Python Environments
Chapter 3. Data Analysis – Explore and Wrangle
Chapter 4. Data Analysis – Deep Dive
Chapter 5. Data Mining – Needle in a Haystack
Chapter 6. Machine Learning 1
Chapter 7. Machine Learning 2
Chapter 8. Ensemble Methods
Chapter 9. Growing Trees
Chapter 10. Large-Scale Machine Learning – Online Learning

中文：

书名：Python Data Science Cookbook

Over 60 practical recipes to help you explore Python and its robust data science capabilities

关于本书

• 本书充斥着简单简洁的Python代码示例，以有效地演示高级概念的实际应用
• 使用Python探索编程、数据挖掘、数据分析、数据可视化和机器学习等概念
• 借助易于遵循、有洞察力的食谱，快速掌握机器学习算法

这本书是为谁写的

本书面向所有级别的数据科学专业人员，包括学生和实践者，从新手到专家。新手可以把时间花在前五章中，让自己熟悉数据科学。专家可以参考从第6章开始的章节，以了解如何使用Python实现高级技术。来自非Python背景的人也可以有效地使用这本书，但如果您以前有一些基本的编程经验，它会很有帮助。

你将学到什么

• 探索完整的数据科学算法
• 了解行业工程师在创建最准确的数据科学模型时使用的技巧
• 有效地管理和使用诸如NumPy、Scipy、SCRIPIT LEARN和matplotlib等Python库
• 创建有意义的功能以解决实际问题
• 了解用于建模和变量选择的高级回归方法
• 对集成方法的基本概念和实现有深入的了解
• 使用来自数值和文本数据形态的各种不同数据集解决现实世界中的问题
• 熟悉现代最先进的算法，如渐变增强、随机森林、旋转森林等

In Detail

Python正日益成为数据科学的语言。就采用率而言，它正在超过R，它被许多开发人员广为人知，并拥有一组强大的库，如Numpy、Pandas、SCRICIT-LINE、Matplotlib、IPython和Scipy，以支持它在该领域的使用。数据科学是一个新兴的热门技术领域，它融合了统计学、机器学习、计算机科学等多个学科。它是一项颠覆性的技术，它改变了当今商业的面貌，并极大地改变了各种垂直行业的经济，比如零售、制造业、在线企业和酒店业。

这本书将引导你完成各种步骤，从简单到数据科学武器库中可用的最复杂的算法，以有效地挖掘数据并从中获取情报。在每一步，我们都提供简单高效的Python食谱，不仅向您展示如何实现这些算法，而且还将彻底阐明基本概念。

本书首先向您介绍了如何使用Python进行数据科学，然后介绍了如何使用Python环境。然后，您将学习如何使用Python分析数据。然后，本书将向您传授数据挖掘的概念，并广泛介绍机器学习方法。它向您介绍了许多可用来帮助有效实现机器学习和数据挖掘例程的Python库。它还涵盖了收缩、集合方法、随机森林、轮换森林和极端树的原理，这些都是任何成功的数据科学专业人员必须具备的。

风格和方法

这是一种基于配方的循序渐进的数据科学算法方法，介绍了这些算法背后的数学哲学。

目录表

第1章：数据科学的Python
第2章.Python环境
第三章：数据分析–探索与争论
第4章：数据分析–深度调查
第5章.数据挖掘-干草堆中的针
第6章.机器学习1
Chapter 7. Machine Learning 2
Chapter 8. Ensemble Methods
Chapter 9. Growing Trees
第10章大型机器学习-在线学习