Data Science Essentials
Data Science Essentials
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Data Science Essentials

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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:

  1. Iteration One: Introduction to Data Science.
  2. Iteration Two: Python.
  3. Iteration Three: Jupyter.
  4. Iteration Four: Numpy.
  5. Iteration Five: Pandas.
  6. Iteration Six: Matplotlib.
  7. Iteration Seven: SKLearn.
  8. 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

1.3.1 C#

1.3.2 Java

1.3.3 Perl

1.4 Basic Features of Python

1.5 Free Software

1.6 Design of the Python System

1.6.1 Indentation

1.7 Limitations of Python

1.8 Python Resources

1.8 Summary

Chapter 2: Crashing Python

2. Getting Started with Python

2.1 Installation

2.1.1 Python 2 and Python 3

2.1.2 Windows

2.1.3 Apple (OS X, macOS)

2.1.4 GNU/Linux

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

2.3.2 IPython

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

2.5.4 Modules

2.5.5 Arithmetic

2.5.6 Functions

2.5.7 Strings

2.5.8 Exceptions

2.5.9 Lists

2.5.10 Tuples

2.5.11 Dictionaries Counter

2.5.12 Sets

2.5.13 Control Flow

2.5.14 Truthiness

2.6 The Advanced

2.6.1 Sorting

2.6.2 List Comprehensions

2.6.3 Generators and Iterators

2.6.4 Randomness

2.6.5 Regular Expressions

2.6.6 Object-Oriented Programming

2.6.7 Functional Tools

2.6.8 enumerate

2.6.9 zip and Argument Unpacking

2.6.10 args and kwargs

2.7 Summary

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

About the Author

Hisham El-Amir
Hisham El-Amir

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

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