COMPUTER PROGRAMMING 4 Books In 1: Data Science, Hacking with Kali Linux, Computer Networking for Beginners, Python Programming

BOOK 1: Python for Data Science
Chapter 1: Introduction to Data Analysis
	Python for Data Science
		Why Select Python
		Python vs. R
		Widespread Application of Data Analysis
		Clarity
	Types of Data Analysis
		Descriptive Analysis
		Predictive Analysis
		Prescriptive Analysis
		Why Data Analysis Is on the Rise?
		Summary of the Data Science Process
		Prerequisite and Reminders
		Do You Need Some Expertise in Mathematics?
Chapter 2: Python Review
	Properties
		Getting help
		Data Analysis vs. Data Science vs. Machine Learning
		Possibilities
		Drawbacks of Data Analysis & Machine Learning
		Accuracy & Performance
Chapter 3: Important Python Libraries
	Install the Software and Setting Up
		For Windows
		Numpy Arrays
		Using IPython as a Shell
		Web Scraping Using Python Libraries
		Web Scraping
		Matplotlib
		2nd generation
Chapter 4: Data Manipulation
	6 Key Things You Need to Know About Numpy and Pandas
	Getting Started with Numpy
		Array Indexing
		Array Slicing
		Array Concatenation
	Getting Started with Pandas
		Importing Data
		Missing Data
		Visualize the Data
		Transformation of Feature
Chapter 5: Data Aggregation
	Definition of Data Frame
	Split-Apply-Combine
	Implementation
	How to Group Data Frames?
Chapter 6: Data Visualization
	Data Visualization to the End-User
	Matplotlib
		Line Chart
		Histogram
		Bar Chart
	Visualization Using Pandas
		The Objective of Visualization
	The Simplest Method to Complex Visualization of Data
	Overview of Plotly
		Building Attractive Plots Using Plotly
		Scatter Plots
		Box Plots
		Heat Maps
Chapter 7: Machine Learning
	Machine Learning Algorithms Classifications
		Supervised Learning
		Unsupervised Learning
		Reinforcement Learning
		How to Approach a Problem
	What is Deep Learning
	Neural Networks with Scikit-Learn
	The Structure of Neuron
	Back Propagation
	Scikit-Learn
	The Neural Networks Using TensorFlow
		TensorFlow
		Preparing the Environment
	Installing Scikit-Learn
		Import Scikitlearn
	Installing TensorFlow
Chapter 8: Artificial Neural Networks
	How the Brain Works
	Constraints and Opportunities
	Let’s See an Example
Chapter 9: How to use Scikit-Learn
	Loading Datasets
	Simple Linear Regression
		Import Libraries
		Data Preparation
		Training the Algorithm
		Predicting
		Evaluating the Accuracy
	Multiple Linear Regression
		Data Preparation
		Training the Algorithm
		Predicting
		Evaluating the Accuracy
Chapter 10: K-Nearest Neighbors Algorithm
	Splitting the Dataset
	Feature Scaling
		Training the Algorithm
		Evaluating the Accuracy
	K Means Clustering
		Data Preparation
		Visualizing the Data
		Creating Clusters
Chapter 11: Classification
	Logistics Regression
	K-Nearest Neighbors
	The Decision Tree Classification
	Random Forest Classification
	Clustering
	Objectives and Function of Clustering
	K-Means Clustering
	Anomaly Detection
Chapter 12: Association Rule Learning
	Explanation
	Apriori
Chapter 13: Reinforcement Learning
	What is Reinforcement Learning?
	Comparison with Supervised & Unsupervised Learning
	Applying Reinforcement Learning
	Mastering the Bagging Method
	How to Do It
Conclusion
BOOK 2: Hacking With Kali Linux
Chapter 1: Basics of hacking
Chapter 2: What is Ethical Hacking?
Chapter 3: Cyber Security
Chapter 4: Linux Architecture
Chapter 5: Basics of Linux Operating System
Chapter 6: Basic Linux Commands
Chapter 7: Characteristics of Kali Linux and Why It Is So Important In The Hacking World
Chapter 8: Installation of Kali Linux
Chapter 9: Applications and Use of Kali Linux
Chapter 10: Different Tools of Kali Linux
Chapter 11: How can Kali Linux be Used For Hacking?
Chapter 12: Techniques of Port Scanning using Kali Linux
Chapter 13: Penetration Testing
Chapter 14: VPN
Chapter 15: Firewall
Chapter 16: Cryptography
Conclusion
BOOK 3: COMPUTER NETWORKING FOR BEGINNERS
Introduction
Chapter 1: Computer networking: An Introduction
	Networking Essentials
	Networks Types
	The OSI Model
	Computer Network Components
	Basic Network Troubleshooting
Chapter 2: Network Management
	Hardware Management and Maintenance
	Virtualization in Cloud Computing
	The Concept behind Virtualization
Chapter 3: Computer Network Communication Technologies
	How computers communicate in a network
	Understanding Ethernet
	Peer-to-Peer Communication
Chapter 4: The Internet
	Internet basics
	Sub-net Mask
	Private Networks
Chapter 5: Router and Server Basics
	Routing Types
	Network Servers
	Understanding VLAN
Chapter 6: IP addressing and IP sub-netting
	What is an IP address?
	IP Sub-netting
	IPv4 vs. IPv6
Chapter 7: Introduction to Cisco System and CCNA Certification
Chapter 8: Fundamentals of Network Security
	Network Intruders
	What can be done about these threats?
	Network security best practices
Chapter 9: Wireless Technology and Security
Chapter 10: Introduction to Machine Learning: A Computer Networking Perspective
	What is Machine Learning?
	Machine Learning in Analytics
	Machine Learning in Management
	Machine Learning in Security
Conclusion
BOOK 4: Python Programming
Introduction
Chapter 1: What is the Python Language, and Why Should I Use It?
	How to Use Python
	The Benefits of Python
Chapter 2: How Can I Install Python on My Computer?
	Installing Python on Mac OS X
		Python – V
		Python3 – V
	Installing Python on a Windows System
	Installing Python on a Linux Operating System
Chapter 3: The Basics of the Python Code
	The Keywords
	Looking at the Comments
	The Importance of Variables
	Bringing in Python Strings
	The Python Functions
	The Operators
Chapter 4: The Different Data Types in Python
Chapter 5: The Python Functions
	The Different Types of Functions
	The Advantages of Python Functions
	The Syntax of the Function
Chapter 6: How to Write Your Own Conditional Statements
	Starting with the If Statement
	Moving to the If Else Statements
	Finishing Off with the Elif Statements
Chapter 7: The Python Classes and How to Write Your Own
Chapter 8: Handling Files in Python
	Creating Our Own New Files
	Can I Create a Binary File?
	How to Open a File
	How to Seek One of the Files
Chapter 9: Tips and Tricks to Get the Most Out of Python
	Comment Out the Code
	Print Things Out
	Work with the Code You Know Will Behave
	Read All of the Error Messages
	Run the Code Often
	Take a Break When Needed
	Ask for Help
Chapter 10: A Quick Introduction to Data Analysis
	Define the Question
	Pick Out the Important Measuring Options
	Collect the Data
	Look Through and Analyze the Data
	Interpret the Results
Chapter 11: Some of the Best Python Algorithms for Data Analysis
	Neural Networks
	Clustering
	Support Vector Machines
	Naïve Bayes
	Decision Trees
Conclusion