Data Science
Simple and Effective Tips and Tricks to Learn Data Science
Copyright 2020 - All rights reserved.
The contents of this book may not be reproduced, duplicated or transmitted without direct written permission from the author.
Under no circumstances will any legal responsibility or blame be held against the publisher for any reparation, damages, or monetary loss due to the information herein, either directly or indirectly.
Legal Notice:
This book is copyright protected. This is only for personal use. You cannot amend, distribute, sell, use, quote or paraphrase any part or the content within this book without the consent of the author.
Disclaimer Notice:
Please note the information contained within this document is for educational and entertainment purposes only. Every attempt has been made to provide accurate, up to date and reliable complete information. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. The content of this book has been derived from various sources. Please consult a licensed professional before attempting any techniques outlined in this book.
By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, which are incurred as a result of the use of information contained within this document, including, but not limited to, errors, omissions, or inaccuracies.
Table of Contents
Introduction
Todays world is all about artificial intelligence and big data. As astonishing as it sounds, around 2.5 exabytes of data are created each day, which means they need for data has risen significantly, especially in the last decade. Many companies have even changed the business model and centered it on data. Data has also added a new department in the IT industry. Before data science, statisticians used to analyze the data qualitatively. Companies employed such statisticians to check and analyze the overall sales and performance of the company. With the emergence of a strong computing process, cloud technology, and analytical tools, the computer science field, combined with statistics, gave birth to Data Science.
Let us begin with understanding what data science is. In simple terms, data science is the branch of study that involves obtaining useful and meaningful insights and trends from a raw set of data or information. The great number of data sets that is obtained is then processed through programming, analytical, and business skills. Sounds tough? Do not worry. Most people do not know how to start with data science or understand how to learn it effectively.
This book deals with tips and tricks to learn data science effectively. With the world turning towards data to make any decision day by day, it is important to know and learn about Data Science. The aim is to learn what data science is and get an exact framework for how to approach the learning process with tips and tricks to make the study effective.
The field of Data Science goes back to its roots of statistics. But it is a combination of statistics, programming, and business acumen. Learning about each of the topics is important and will give a complete idea of how to approach the learning process, making it easy. The art of finding trends and insights from the obtained large set of data goes a long way back.
The ancient Egyptians used census data to collect tax efficiently, and they forecasted the flooding of the river Nile each year accurately. Learning from the past data to make an insight, which makes sense to the business, has been done ever since it will eventually lead to making smart business decisions.
It is not a secret anymore that data scientists are in high demand. If you love working with data, then this field is for you. If you learn data science, you could grab an opportunity to work in this well-compensated field. Also, the employees who are skilled in data science can make the work, and the company more data-driven and hence be in demand across industries. Moreover, data science jobs are making revolutionary changes in the technologies to the extent that we now have self-driven autonomous vehicles and image recognition tools, which have created a huge impact on the advancement of industries and academia.
Every data scientist is expected to have some skills, both technical and non-technical so that he can excel in the field. These skills also make it easier for the data scientist to identify patterns in the data set and help the management gather more information from the data. The management can use this information to assess how they should proceed further. It also helps them understand the various changes they need to make to the existing products and services to increase revenue.
With the expert tips and tricks in this book, you will be able to learn how to drive insights efficiently from a data set and, more importantly, get a definite framework on how to proceed to become a data scientist that companies are in search of.
So, if you are ready to learn more, lets read on and get started.
Chapter One: History of Data Science
The term Data Science might have emerged only recently to mark a new profession in the IT industry, but making insights from a large repository of data goes a long way back. It has been mainly discussed by statisticians, mathematicians, and computer engineers for years. In 1962 John W. Turkey termed a field called data analysis, which is the same as modern-day data science. Later in 1992, the attendees at a statistics symposium accepted the birth of a new module focused on data of numerous origins and forms, which was a combination of the established principles and concepts of statistics and data analysis with computing.
The term Data Science traces back to 1974 when renowned Danish computer scientist Peter Naur suggested it a possible alternative name for computer science. In 1996, the International Federation of Classification Societies was termed the very first conference to feature data science as a separate topic particularly. In 1997, C.F Jeff Wu proposed that the name statistics be replaced as data science as he thought a new name would help statistics differentiate itself from accounting, which otherwise was considered synonymous.
In 1998, Chikio Hayashi suggested that data science is a very prominent, interdisciplinary concept with three main concepts: data design, assembling, and data analysis.
The modern concept of data science as a separate discipline is attributed to William S. Cleveland. In 2002 and 2003, Data science journals were launched by the committee of Data Science Technology and Columbia University, respectively.
Chapter Two: Data Dominance and Data Revolution The Value of Data
Oil is surely one of the most valuable and important resources of human society. Oil has dominated the human world for centuries as one of the most important and valuable resources. The more you look back at history, the more you will see how controlling the sources and trade of oil equal to controlling the whole economy. It is no wonder that data is considered to be the new oil now. It is a perfect phrase because currently, we live in a data economy where data or information is supposed to be the most important asset. Raw data is as valuable as oil as it holds the potential to be refined and used as a commodity.
With the help of refined data, companies can make crucial changes in the initial stages before the costs are incurred instead of looking at the problems retrospectively. This should serve as a guiding principle for everyone as it will help you turn data into insight. If the input is transparent, the output will be more credible and trustworthy. It is necessary to clean, review, and verify data because you need to check that nothing is incorrect or missing before the refinement process begins. It is necessary for the business leads to check the opportunities provided by the refined data. With their knowledge of organization, culture, and priorities, they can make the best use of data by comparing it against the current environment to make the company's best possible decision.
