Python: Data Analytics and Visualization


Python: Data Analytics and Visualization
by Phuong Vo.T.H , Martin Czygan , Ashish Kumar 
pages 页数:  866 pages
Publisher Finelybook 出版社:  Packt Publishing (11 April 2017)
Language 语言:  English
ISBN-10 书号:  1788290097
ISBN-13 书号:  9781788290098
B072P2CYWN


Book Description
Understand,evaluate,and visualize data
About This Book
Learn basic steps of data analysis and how to use Python and its packages
A step-by-step guide to predictive modeling including tips,tricks,and best practices
Effectively visualize a broad set of analyzed data and generate effective results

Who this book is for
This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner.

What you will learn
Get acquainted with NumPy and use arrays and array-oriented computing in data analysis
Process and analyze data using the time-series capabilities of Pandas
Understand the statistical and mathematical concepts behind predictive analytics algorithms
Data visualization with Matplotlib
Interactive plotting with NumPy,Scipy,and MKL functions
Build financial models using Monte-Carlo simulations
Create directed graphs and multi-graphs
Advanced visualization with D3
In Detail
You will start the course with an introduction to the principles of data analysis and supported libraries,along with NumPy basics for statistics and data processing. Next,you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on,you will get a brief overview of the Matplotlib API .Next,you will learn to manipulate time and data structures,and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms,that is,applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn.
After this,you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts,and implement them in Python using libraries such as Pandas,scikit-learn,and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression,Decision Tree,and Logistic Regression. Finally,you will master best practices in predictive modeling.
After this,you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks,which explains the transformation of data into information and eventually knowledge,this path subsequently cover the complete visualization process using the most popular Python libraries with working examples
This Learning Path combines some of the best that Packt has to offer in one complete,curated package. It includes content from the following Packt products:
Getting Started with Python Data Analysis,Phuong Vo.T.H &Martin Czygan
Learning Predictive Analytics with Python,Ashish Kumar
Mastering Python Data Visualization,Kirthi Raman
Style and approach
The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization
Contents
1. Module 1
1. Introducing Data Analysis and Libraries
2. NumPy Arrays and Vectorized Computation
3. Data Analysis with Pandas
4. Data Visualization
5. Time Series
6. Interacting with Databases
7. Data Analysis Application Examples
8. Machine Learning Models with scikit-learn
2. Module 2
1. Getting Started with Predictive Modelling
2. Data Cleaning
3. Data Wrangling
4. Statistical Concepts for Predictive Modelling
5. Linear Regression with Python
6. Logistic Regression with Python
7. Clustering with Python
8. Trees and Random Forests with Python
9. Best Practices for Predictive Modelling
3. Module 3
1. A Conceptual Framework for Data Visualization
2. Data Analysis and Visualization
3. Getting Started with the Python IDE
4. Numerical Computing and Interactive Plotting
5. Financial and Statistical Models
6. Statistical and Machine Learning
7. Bioinformatics,Genetics,and Network Models
8. Advanced Visualization
了解,评估和可视化数据
关于这本书
了解数据分析的基本步骤以及如何使用Python及其软件包
包括提示,技巧和最佳实践在内的预测建模分步指南
有效地显示一组广泛的分析数据并产生有效的结果
这本书是谁
本书适用于渴望进行数据分析的Python开发人员,希望以更有效率和洞察力的方式可视化其分析数据。
你会学到什么
熟悉NumPy,并在数据分析中使用阵列和面向阵列的计算
使用熊猫的时间序列功能处理和分析数据
了解预测分析算法背后的统计学和数学概念
Matplotlib数据可视化
与NumPy,Scipy和MKL功能的交互式绘图
使用蒙特卡罗模拟构建金融模型
创建有向图和多图
使用D3进行高级可视化
详细
您将开始介绍数据分析和支持的库的原理,以及NumPy的统计和数据处理基础知识。接下来,您将概述Pandas软件包,并使用其强大的功能来解决数据处理问题。接下来,您将简要介绍Matplotlib API。接下来,您将学习操纵时间和数据结构,并使用Python包将数据加载和存储在文件或数据库中。您将学习如何在Python中应用强大的包,以使用示例将原始数据处理为纯粹有用的数据。您还将简要了解机器学习算法,即应用数据分析结果作出决策或构建有用的产品,如使用Scikit学习的建议和预测。
之后,您将继续进行数据分析专业化预测分析。社交媒体和IOT导致了数据的雪崩。您将开始使用Python进行预测分析。您将看到如何从数据创建预测模型。您将获得有关统计学和数学概念的平衡信息,并使用Pandas,scikit-learn和NumPy等库实现它们。您将了解更多关于最佳预测建模算法,如线性回归,决策树和逻辑回归。最后,您将掌握预测建模中的最佳实践。
之后,您将获得所需的所有实际指导,帮助您实现有效的数据可视化。从数据框架的一章开始,它解释了将数据转换为信息和最终知识,该路径随后覆盖了使用最流行的Python库的完整可视化过程,其中包含工作示例
这个学习路径结合了Packt在一个完整的,策划的包中提供的一些最好的。它包含以下Packt产品的内容:
Python数据分析开始,Phuong Vo.T.H&Martin Czygan
用Python学习预测分析,Ashish Kumar
掌握Python数据可视化,Kirthi拉曼
风格和方法
该课程作为一个循序渐进的指南,借助真实世界的示例和数据集,帮助您熟悉数据分析和Python支持的库。它还可以通过使用Python在公共数据集上实现预测分析算法,从而获得对预测建模的实用见解。该课程提供了丰富的实践指导,帮助您了解数据可视化的旅程
目录
模块1
1.数据分析与图书馆介绍
数字数组和矢量化计算
3.熊猫数据分析
4.数据可视化
时间序列
6.与数据库进行交互
数据分析应用实例
8.机器学习模型与scikit学习
模块2
1.预测建模入门
2.数据清理
数据争吵
4.预测建模的统计概念
Python的线性回归
Python的逻辑回归
7.使用Python集群
树木和随机森林与Python
9.预测建模的最佳实践
3.模块3
1.数据可视化的概念框架
数据分析与可视化
3.开始使用Python IDE
数值计算和互动绘图
5.财务和统计模型
统计和机器学习
7.生物信息学,遗传学和网络模型
8.高级可视化

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