Book Description
Are You Looking For The Best Beginners Guide To Discovering Data Analysis And Analytics With Python?
Do You Want To Enter The World Of Data Science And How To Leverage Python For It?
Do Want To Get A Thorough Introduction To Machine Learning?
If yes, then this Guide is for you! This is the Top Guide to learning Data Analysis & Analytics, Data Science and Machine Learning with 4 Manuscripts in 1 book:
- Python for Data Analysis & Analytics
- Data Science Core
- Data Science Annex
- Machine Learning Introduction.
A great opportunity: Simplicity, Best Order and Selection of topics to Learn Fast and Selected Practice Exercises and Examples.
In Manuscript 1 “Python for Data Analysis & Analytics” you’ll discover:
- What Data Analysis is and why it is important
- What are the different types of Data Analysis
- What are the 6 key steps of the Data Analysis process that you should follow
- What are the applications of Data Analysis and Analytics
- How to set up the Python environment for Data Analysis
- What are and how to use Python Data Structures
- How to work with IPython/Jupyter Notebook
- How to work with NumPy
- How to visualize data with Matplotlib
- What other visualization libraries are out there
In Manuscript 2 “Data Science Core” you’ll discover:
- What is Data Science and what does it encompass
- What are the 5 key steps of the Data Science process that you should follow
- How to set up the Python environment for Data Science
- How to work with Seaborn data visualization module
- How to leverage the Scikit-Learn module for Machine Learning
- How to leverage Data Science in the Cloud
In Manuscript 3 “Data Science Annex” you’ll discover:
- What are the most important applications of Data Science
- What are the most important Machine Learning Algorithms
- Why is Big Data important and how to get the best out of it
- How to leverage Neural Networks
- What are the main Data Mining Techniques
- And much more…
In Manuscript 4 “Machine Learning Introduction” you’ll discover
- What is Machine Learning and what does it encompass
- What are the 7 Steps of the Machine Learning Process
- What are the different Machine Learning types
- How is Machine Learning applied to the real world
- What are the most important Python libraries for Machine Learning
- And much more…
中文：
书名：用于数据分析的Python：从头开始发现数据科学的顶级初学者指南，数据分析，分析；机器学习与商业案例结合使用#8211；4册合一
您是否正在寻找《使用Python发现数据分析和分析的最佳入门指南》？
你想进入数据科学的世界吗？你想如何利用Python吗？
Do Want To Get A Thorough Introduction To Machine Learning?
如果是，那么本指南就是为您准备的！这是学习数据分析与分析、数据科学和机器学习的顶级指南，一本书有4篇手稿：
- 用于数据分析和分析的Python
- Data Science Core
- 数据科学附件
- 机器学习导论。
一个很好的机会：简单、最好的顺序和选择快速学习的主题和精选的练习和例子。
在手稿1中“用于数据分析和分析的Python“你会发现：
- 数据分析是什么？为什么它很重要
- 数据分析的不同类型有哪些
- 数据分析流程中您应该遵循的6个关键步骤是什么
- 数据分析和分析的应用有哪些
- 如何设置用于数据分析的Python环境
- 什么是以及如何使用Python数据结构
- 如何使用IPython/Jupyter笔记本
- How to work with NumPy
- 如何使用Matplotlib可视化数据
- 现在还有哪些可视化程序库
在手稿2中“Data Science Core“你会发现：
- 什么是数据科学？它包括什么？
- 数据科学流程中您应该遵循的5个关键步骤是什么
- 如何建立适用于数据科学的Python环境
- How to work with Seaborn data visualization module
- 如何利用Scikit-Learn模块进行机器学习
- 如何在云中利用数据科学
在手稿3中“数据科学附件“你会发现：
- 数据科学最重要的应用是什么
- 最重要的机器学习算法是什么
- Why is Big Data important and how to get the best out of it
- 如何利用神经网络
- 主要的数据挖掘技术有哪些
- 和更多的…
在手稿4中“机器学习导论” you’ll discover
- 什么是机器学习？它包括什么？
- 机器学习过程的7个步骤是什么
- 有哪些不同的机器学习类型
- 机器学习如何应用于现实世界
- 机器学习的最重要的Python库是什么
- 和更多的…
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