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Index
Table of Contents
Introduction
Chapter 1: Introduction of Python and Python’s History
The History of Python
Why Use Python?
Chapter 2: Data Analysis
What is Data Analysis?
The Main Data Analysis
Chapter 3: Why Choose Python for Data Analysis?
It is Easy to Read and Simple
The Libraries are Nice to Work with
The Large Community
Chapter 4: Understanding the Data Analytics Process
The Discovery Phase
The Data Preparation Phase
Planning Out the Model Phase
The Model Building Phase
The Communication Phase
Operationalize
Chapter 5: NumPy Package Installation
Installing NumPy on a Mac OS
Installing NumPy on a Windows System
Installing NumPy on the Ubuntu Operating Systems
Installing NumPy on Fedora
Chapter 6: NumPy Array Operations
What are the Arrays in NumPy?
How to Create NumPy Arrays
Chapter 7: Saving NumPy Arrays\
Saving Your NumPy Array to a .CSV File
Saving the NumPy Array to a Binary or .NPY File
How to Save the Array in a .NPZ File (Compressed)
Chapter 8: All About Matplotlib
What is Matplotlib?
The Types of Plots
Chapter 9: All About Pandas and IPython
Pandas
IPython
Chapter 10: Using Python Data Analysis with Practical Examples
Chapter 11: Essential Tools with Python Data Analysis
GraphLab Create
Scikit-Learn
Spark
Tableau Public
OpenRefine
KNIME
Dataiku DSS
Chapter 12: Data Visualization
The Background of Data Visualization
Why is Data Visualization so Important?
How Can We Use Data Visualization?
How to Lay the Groundwork
Chapter 13: Applications of Data Analysis
Security
Transportation
Risk and Fraud Detection
Logistics of Deliveries
Customer Interactions
City Planning
Healthcare
Travel
Digital Advertising
Conclusion
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