Python: Data Analytics and Visualization

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

Table of 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

中文：

书名：Python: Data Analytics and Visualization

了解、评估和可视化数据

About This Book

• 了解数据分析的基本步骤以及如何使用Python及其包
• 预测建模的逐步指南，包括提示、技巧和最佳实践
• 有效地可视化大量的分析数据并生成有效的结果

Who This Book Is For

这本书是为热衷于进入数据分析并希望以更高效和更有洞察力的方式可视化他们分析的数据的Python开发人员而写的。

What You Will Learn

• 熟悉NumPy并在数据分析中使用数组和面向数组的计算
• 使用熊猫的时间序列功能处理和分析数据
• 理解预测分析算法背后的统计和数学概念
• 利用Matplotlib实现数据可视化
• 使用NumPy、Scipy和MKL函数进行交互打印
• 使用蒙特卡罗模拟建立金融模型
• 创建有向图和多重图
• 借助D3实现高级可视化

In Detail

本课程首先介绍数据分析原理和支持的库，以及统计和数据处理的NumPy基础知识。接下来，您将概述Pandas包并使用其强大的功能来解决数据处理问题。接下来，您将获得Matplotlib API的简要概述。接下来，您将学习如何操纵时间和数据结构，并使用Python包在文件或数据库中加载和存储数据。您将通过示例学习如何应用Python中强大的包来将原始数据处理成纯的、有帮助的数据。您还将获得机器学习算法的简要概述，即应用数据分析结果做出决策或使用Scikit-Learn构建有用的产品，如建议和预测。

在此之后，您将继续学习数据分析专业–预测分析。社交媒体和物联网导致了数据的雪崩。您将开始使用Python进行预测分析。您将了解如何根据数据创建预测模型。您将获得有关统计和数学概念的平衡信息，并使用诸如Pandas、SCRICIT-LEARN和NumPy等库在Python中实现它们。您将了解更多关于最佳预测建模算法的知识，如线性回归、决策树和Logistic回归。最后，您将掌握预测建模的最佳实践。

在此之后，您将获得所需的所有实用指导，帮助您踏上有效的数据可视化之旅。本课程从介绍数据框架的章节开始，该章节解释了将数据转换为信息并最终转换为知识的过程，随后介绍了使用最流行的Python库和工作示例的完整可视化过程

此学习路径将Packt必须提供的一些最好的功能组合在一个完整的、经过精心策划的程序包中。它包括来自以下Packt产品的内容：

• 《Python数据分析入门》，Phuong Vo.T.H&&Martin Chiygan
• 《使用Python学习预测分析》，Ashish Kumar
• 掌握Python数据可视化，基尔蒂·拉曼

Style and approach

本课程是一个循序渐进的指南，通过真实世界的示例和数据集，让您熟悉数据分析和Python支持的库。它还通过使用Python在公共数据集上实现预测分析算法，帮助您获得对预测建模的实际见解。本课程提供了丰富的实践指导，帮助您踏上数据可视化之旅

Table of Contents

1. Module 1
1.介绍数据分析和库
2.NumPy数组与矢量化计算
3.熊猫的数据分析
4. Data Visualization
5. Time Series
6.与数据库交互
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.用PYTHON实现线性回归
6.使用PYTHON进行Logistic回归
7.使用Python进行集群
8. Trees and Random Forests with Python
9.预测性建模的最佳实践

3. Module 3
1. A Conceptual Framework for Data Visualization
2.数据分析与可视化
3. Getting Started with the Python IDE
4. Numerical Computing and Interactive Plotting
5.金融和统计模型
6.统计和机器学习
7. Bioinformatics, Genetics, and Network Models
8. Advanced Visualization