Data Science from Scratch Using Python 3.7: Fun Based Learning for Data Science for Beginners

How to Use This Book (Data Science from Scratch)
We presume that the data scientists reading this book are knowledgeable about data science, common machine learning methods, and popular data science tools, and have in the course of their work run proof of concept studies, and built prototypes. We offer a book that introduces advanced techniques and methods for building data science solutions to this audience, showing them how to construct commercial-grade data products.

Table of Contents
1.Data Science in a Big Data World
2.Data Scientists and Data Science
3.Key Objectives of Data Science
4.Data-Rich Strategic Data Science
5.General Introduction to Machine Learning
6.The Practical Concepts of Machine Learning
7.Data Science and Business
8.Transitioning from Data Developer to Data Scientist
9.Data Science Roots
10.Neural Networks and Data Science problems
11.Introduction to the Python World
12.Python Functions, Modules, and Built-ins
13.Python 3.7 Data Structures
14.Natural Language Processing
15.Designing for Artificial Intelligence
16.Artificial Intelligence and Social Futures
17.Big Data and Data Science
18.Data for Big Data
19.Ansible for Data Science 
What is Data Science?
Data Science can be seen as the interdisciplinary field that deals with the creation of insights or data products from a given set of data files (usually in the unstructured form), using analytics methodologies. The data it handles is often what is commonly known as “big data,”. Data science is not a guaranteed tool for finding the answers to the questions we have about the data, though it does a good job at shedding some light on what we are investigating.

What is Machine learning?
Machine learning is a computer science research area that deals with methods to identify and implement systems and algorithms by which a computer can learn, based on the examples given in the input. The challenge of machine learning is to allow a computer to learn how to automatically recognize complex patterns and make decisions that are as smart as possible. It conjures all forms of images around artificial intelligence which includes Neural Networks, Support Vector Machines (SVMs), and so on.
Machine learning works differently to traditional prediction methods because it is the machines themselves that create the equations, not the researcher. The family tree of machine learning has three main branches: supervised and unsupervised methods, and reinforcement learning.

What Does a Data Scientist Actually Do?
A data scientist applies the scientific method on the provided data, to come up with scientifically robust conclusions about it, and to engineer software that makes use of their findings, adding value for whoever is on the receiving end of this whole process, be it a client, a visitor to a website, or the management team.

There are three major activities within the data science process:
Data engineering
This involves a number of tasks closely associated with one another, aiming at getting the data ready for use in the stages that follow. It is not a simple process and difficult to automate. That’s why around 80% of our time as data scientists is spent in the stage of data engineering.

Data modeling
This is probably the most interesting part of the data scientist’s work.
The data modeling phase also involves validating the prediction, as well as repeating the process until a satisfactory model is created. It is then applied to data that hasn’t been used in the development of this model.

Information distillation
This aspect of the data scientist’s work has to do with delivering the insights acquired from the previous stages, communicating them, usually through informative visuals, or in some cases, developing a data product.