Data Analysis from Scratch with Python: Step by Step Guide

✍ Scribed by Morgan, Peters

• Publisher: Createspace Independent Publishing Platform
• Year: 2016
• Tongue: English
• Leaves: 113
• Category: Library

✦ Synopsis

* BUY NOW (Will soon return to 25.59) *Free eBook for customers who purchase the print book from Amazon*** Are you thinking of becoming a data analyst using Python? If you are looking for a complete guide to data analysis using Python language and its library that will help you to become an effective data scientist, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn pandas, NumPy, IPython, and Jupiter in the Process. Target Users This book is a practical introduction to data science tools in Python. It is ideal for analyst's beginners to Python and for Python programmers new to data science and computer science. Instead of tough math formulas, this book contains several graphs and images. What's Inside This Book?
Introduction
Why Choose Python for Data Science & Machine Learning
Prerequisites & Reminders
Python Quick Review
Overview & Objectives
A Quick Example
Getting & Processing Data
Data Visualization
Supervised & Unsupervised Learning
Regression
Simple Linear Regression
Multiple Linear Regression
Decision Tree
Random Forest


Classification
Logistic Regression
K-Nearest Neighbors
Decision Tree Classification
Random Forest Classification


Clustering
Goals & Uses of Clustering
K-Means Clustering
Anomaly Detection


Association Rule Learning
Explanation
Apriori


Reinforcement Learning
What is Reinforcement Learning
Comparison with Supervised & Unsupervised Learning
Applying Reinforcement Learning


Neural Networks
An Idea of How the Brain Works
Potential & Constraints
Here's an Example


Natural Language Processing
Analyzing Words & Sentiments
Using NLTK


Model Selection & Improving Performance
Sources & References
Frequently Asked Questions

Q: Is this book for me and do I need programming experience? A: if you want to smash Python for data analysis, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.

Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data analysis and further learning will be required beyond this book to master all aspects.

Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected].

AI Sciences Company offers you a free eBooks at http: //aisciences.net/free/

✦ Table of Contents

Who Should Read This?......Page 4
From AI Sciences Publisher......Page 7
Author Biography......Page 12
Table of Contents......Page 13
Introduction......Page 16
Python vs R......Page 17
Clarity......Page 18
Installation & Setup......Page 20
Is Mathematical Expertise Necessary?......Page 21
4. Python Quick Review......Page 22
Tips for Faster Learning......Page 28
Data Analysis vs Data Science vs Machine Learning......Page 29
Limitations of Data Analysis & Machine Learning......Page 30
Accuracy & Performance......Page 31
Iris Dataset......Page 33
Potential & Implications......Page 34
CSV Files......Page 37
Feature Selection......Page 40
Internal Data Source......Page 42
Goal of Visualization......Page 44
Importing & Using Matplotlib......Page 45
What is Supervised Learning?......Page 51
What is Unsupervised Learning?......Page 53
How to Approach a Problem......Page 54
Simple Linear Regression......Page 55
Multiple Linear Regression......Page 58
Decision Tree......Page 63
Random Forest......Page 65
Logistic Regression......Page 69
K-Nearest Neighbors......Page 73
Decision Tree Classification......Page 76
Random Forest Classification......Page 79
Goals & Uses of Clustering......Page 83
K-Means Clustering......Page 84
Anomaly Detection......Page 87
Explanation......Page 89
Apriori......Page 91
What is Reinforcement Learning?......Page 95
Applying Reinforcement Learning......Page 96
An Idea of How the Brain Works......Page 100
Potential & Constraints......Page 101
Here’s an Example......Page 102
Analyzing Words & Sentiments......Page 106
Using NLTK......Page 107
Thank you !......Page 109
Online books, tutorials, & other references......Page 110
Thank you !......Page 112