Data Science Essentials
This book is 20% complete
Last updated on 2020-05-28
About the Book
#1 About the book
This book comes from my experience teaching Python in a variety of settings and through different stages of its (and my) development. Much of the material has been taken from multiple drafts that I wrote on machine learning and data science as well as the Python Programming drafts too that I wrote through my blog. I’m looking forward to teaching Python to people as long as people will let me, and I’m interested in seeing how the next generation of students will approach it (and how my approach to them will change). Overall, it’s been just an amazing experience to see the widespread adoption of Python over the past decade. I’m sure the next decade will be just as amazing.
#2 How this book is Organized?
This book is not a part-based structured, it is an Iteration-based structured. Each Iteration is meant to teach you a specific thing in Data Science, for example:
- Iteration One: Introduction to Data Science.
- Iteration Two: Python.
- Iteration Three: Jupyter.
- Iteration Four: Numpy.
- Iteration Five: Pandas.
- Iteration Six: Matplotlib.
- Iteration Seven: SKLearn.
- Iteration Eight: Finals.
#3 Table of Content
Iteration One: Introduction to Data Science
Chapter 1: Introduction
Chapter 2: How to be a Data Scientist
Iteration Two: Python
Chapter 2: Introduction to Python and it’s Environment
1. History and Overview of Python
1.1 What is Python?
1.2 The Python Philosophy
1.2.1 Understanding Why Python is So Cool
1.2.2 Unearthing the reasons for using Python
1.2.3 Deciding how you can personally benefit from Python
1.3 Back to Python
1.4 Basic Features of Python
1.5 Free Software
1.6 Design of the Python System
1.7 Limitations of Python
1.8 Python Resources
Chapter 2: Crashing Python
2. Getting Started with Python
2.1.1 Python 2 and Python 3
2.1.3 Apple (OS X, macOS)
2.1.5 Installing or Updating Python Packages
2.2 Getting Started with the Python Interface
2.3 Choosing the IDE
2.3.1 Python Interpreter
2.3.3 Jupyter Notebook & Lab
2.4 Beyond the Python you Know
2.5 The Basics
2.5.1 Getting Python
2.5.2 The Zen of Python
2.5.3 Whitespace Formatting
2.5.13 Control Flow
2.6 The Advanced
2.6.2 List Comprehensions
2.6.3 Generators and Iterators
2.6.5 Regular Expressions
2.6.6 Object-Oriented Programming
2.6.7 Functional Tools
2.6.9 zip and Argument Unpacking
2.6.10 args and kwargs
Iteration Three: Jupyter
Iteration Four: Numpy
Chapter n: Introduction to Numpy
Chapter n: Numpy Basics
Iteration Five: Pandas
Chapter n: Introduction to Pandas
Chapter n: Pandas and Data Wrangling
Iteration Six: Matplotlib
Chapter n: Introduction to Matplotlib
Chapter n: The Art of Matplotlib Data Visualization
Iteration Seven: SKLearn
Chapter n: Introduction to SciKit Learn
Chapter n: Linear Models in SKLearn
Iteration Eight: Finals
Chapter n: whats Next
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