Introduction to Data Science Using Python

This book contains two parts, the first is designed to be used in an introductory programming course for students looking to learn Python, without having any prior experience with programming. Basic programming concepts are discussed, explained, and illustrated with a Python program. Ample programming questions are provided for practice. The second part of the book utilizes machine-learning concepts and statistics to accomplish data-driven resolutions. Python programs are provided to apply scientific computing to conclude statistically driven results.

Conditions of Use

This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Introduction to Data Science Using Python for free.

Title: Introduction to Data Science Using Python
Publisher: PA-ADOPT
Author(s): Afrand Agah
Published: 2024-06-20
Edition: 1
Format: eBook (pdf, epub, mobi)
Pages: 117
Language: English
License: CC BY-NC-SA
Book Homepage: Free eBook, Errata, Code, Solutions, etc.

About PA-ADOPT
Preface
About OER
Table of Contents
1. Installing Python
2. Introduction to Programming
	2.1. Variables
		2.1.1. Boolean Variables
		2.1.2. Random Variables
	2.2. Strings
	2.3. ASCII Code
		2.4. Practice Questions
3. Decision Structures
	3.1. Nested Decision Structures
		3.2. Practice Questions
4. Repetitions
	4.1. While Loops
	4.2. For Loops
		4.3. Practice Questions
	4.4. Nested Loops
		4.5. Practice Questions
5. Functions
	5.1. Void and Value Returning Functions
	5.2. Passing Data to and From Functions
	5.3. Mathematical Built-in Functions
		5.4. Practice Questions
6. Recursion
7. File Access
	7.1. Read From a File
	7.2. Write to a File
		7.2.1. New File
		7.2.2. An Existing File
	7.3. Notable Built-in Functions
		7.4. Practice Questions
8. Lists
	8.1. Practice Questions
9. Arrays
	9.1 Practice Questions
10. Plotting Graphs
11. Object Oriented Programming
	11.1 Constructor
	11.2 Inheritance
	11.3 Polymorphism
12. Using Python Packages
13. Python and Graph Theory
	13.1 Networkx
	13.2 Matplotlib
14. Python and Machine Learning
	14.1 Supervised Learning
		14.1.2 Regression
		14.1.3 Linear Functions
		14.1.4 Polynomial Functions
	14.2. Unsupervised Learning
15. Python and Statistics
	15.1 Standardizing Data by Scaling
	15.2 T-Test
References
Appendix
	Solutions for Practice Questions (2.4)
	Solutions for Practice Questions (3.2)
	Solutions for Practice Questions (4.3)
	Solutions for Practice Questions (4.5)
	Solutions for Practice Questions (5.4)
	Solutions for Practice Questions (7.4)
	Solutions for Practice Questions (8.1)
	Solutions for Practice Questions (9.1)