Python Programming · 2 Books in 1 · Python for Data Analysis and Science With Big Data Analysis, Statistics and Machine Learning. by Academy, Computer Science -- Read -- Imperial Library of Trantor

Index

Python for Data Analysis

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

Python for Data Science

Introduction Chapter 1: Foundational Data Science Technologies

Data Science Lifecycle

Stage I – Business Understanding Stage II – Data Acquisition and Understanding

Stage III – Modeling Stage IV – Deployment Stage V – Customer Acceptance Types of Data

Data Science Strategies

Data Science vs Data Analysis Data Science in Cyber Security

Chapter 2: Introduction to Python Coding

Installation Instructions for Python Python Variables Python Data Types Python Numbers Python Strings Python Booleans Python Lists Python Tuples Python Sets Python Dictionary Python Classes and Objects

Chapter 3: Data Visualization and Analysis with Python Chapter 4: Machine Learning and Predictive Analysis Conclusion