Mastering The Art Of Data Analysis From Basics To Informed Decision-Making

Copyright

Mastering The Art Of Data Analysis From Basics To Informed Decision-Making

Copyright©Space Learn, 2023

Cover design by Space Learn

Interior design by Space Learn, Georgia 30043, USA.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means electronic, mechanical, photocopy, recording, or any other except for brief quotations, not to exceed 400 words, without the prior permission of the publisher.

First published in 2023 by

Space Learn

Table Of Contents

Copyright

Table Of Contents

About

The Data Collection

Internal Acquisition Systems

Procuring Data

Alternative Data

Web Scraping

Open Data

Adherence

Data Storing

Structured

Unstructured

Big Data

Relational Database Management Systems

Enterprise Data Warehouse/ Data Warehousing

Key Value Shop

Distributed File Systems

Cloud Solutions

Variables

Independent and Dependent Variables

Numeric Variables

Continuous vs. Discrete

Data Scraping

Choice Variable

Variables for Merging

One-Hot Encoding

Binning

Retention of Data

Analysis Methods

Statistics

Descriptive Analytics

Inferential Methods

Data Mining

Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Data Mining Vs. Machine Learning

Algorithms

Analysis of Regression

Analysis of Linear Regression

Non-linear Regression

Exponential Regression

Advantages

Drawbacks

Categorization

Logistic Regression

Advantages

Shortcomings

Clustering

Closest Relatives

K-Means Clustering

Advantages

Drawbacks

Sequence Mining And Association Analysis

Frequently Asked Questions & Help

Example of Apriori Method

Mining Sequences

Strengths

Shortcomings

Natural Language Processing

Stemming

Editor's Distance

Named Entity Recognition

Abstraction of Intent

Relationship Extraction

tf–idf

Classification of Text

Strengths

Limitations

Data Visualization

Explanatory And Exploratory Graphics

Data Visualization Examples

Histograms and Bar Charts

Bar Charts

Pie charts

Scatterplots

Box Plots

Violin Plots

Rug Plots

Visual Style

Tools for Visualizing Data

Tableau

Third-Party Libraries And Programming Languages

Business Intelligence

The Business Intelligence Cycle

Bug Bounty

Practical Demonstration: Examination

Environment for Development

Task 1: What Is The Number Of Days That The Restaurant Is Open Every Week?

Task 2: What Day Of The Week Has The Highest Amount Of Bills?

Task 3: Create Heatmap

Practical Demo: Inferential Analysis

Conclusion

About

Improve your decision-making with this stripped-down primer to data analytics. Wish to broaden your skill set to include data analytics? Are you having problems knowing where to begin?

This book gives you the vocabulary, resources, and fundamental techniques you need to think like a data scientist, bit by bit and cell by cell. In order to increase your data literacy, each chapter adds to and links discrete knowledge blocks using the Lego set method. You may go from knowing nothing about data analytics to confidently understanding and debating data issues with this step-by-step guide.

This Book Is for Whom? Anyone interested in understanding data analytics should read this book because it doesn't assume any knowledge of complex math or data science terms. This book is for you if you've attempted to understand data analytics in the past but failed.

This book emphasizes experiential learning. This contains two bonus Python coding exercises with free video content to guide you through both, as well as visual and practical examples. You will have the necessary skills by the end of the book to handle actual data issues in your business or day-to-day activities.

What You Will Learn:

Where to Store Your Data, Including Big Data

New Trends in Data Analytics, Including What is Alternative Data and Why Few People Know About It

How to Recognize the Common Data Types Every Data Scientist Needs to Master

When and how to use Natural Language Processing, Classification, Clustering, Regression Analysis, and Association Analysis

How to use Data Visualization and Business Intelligence to make better business decisions

How to explain to your colleagues the differences between Data Mining, Machine Learning, and Analytics

The Data Collection

Data has developed into a valuable asset that can be hoarded, traded, and even plundered due to its ability to accurately explain the past and forecast the future. Our universe is becoming more and more recorded due to technological advancements and widespread digitalization, and data is the preferred medium for storing this never-ending flow of information. Although data is now widely disseminated as electronically stored information, it is neither a recent development nor a product of the Information Age.

While state-of-the-art technology is employed in the equipment needed to store, handle, and process data, modern servers and smart gadgets are merely the most recent in a long line of evolutionary progress. Hunter-gatherers used tally marks engraved into animal bones to gather data. The Sumerian and Egyptian dynasties were among the ancient societies who created large-scale surveys, counting devices, symbolic writing systems, and cryptography. (In a manner akin to how businesses encrypt critical information today, Mesopotamian artisans employed cryptography to safeguard their proprietary glaze formulations.) Humans have always been naturally obsessed with learning new things, and this urge has only become stronger since the Scientific Revolution.

The Scientific Revolution, which began in 1543 with Nicholas Copernicus' rejection of Earth as the universe's stationary center and ended in 1687 with Isaac Newton's grand synthesis, is renowned for having accelerated scientific discoveries. During this time, scientific advancements reached new heights, and by the time Isaac Newton published Laws of Motion—now regarded as one of the most influential scientific works ever written—scientists in Europe were experimenting with air pressure, electricity, telescopes, microscopes, calculus and logarithms, and Blaise Pascal's mechanical calculator. The calculator was a significant invention because it used a set of cogs to quickly add and subtract big numbers, but it was also based on several innovations made before Pascal, such as the place value system, negative numbers, and the Indo-Arabic numeral system.

The place value system was developed in 3500 BC in Ancient Egypt to represent greater quantities and make mathematics easier. Instead of 151 distinct signs or symbols, 151 could be expressed with seven symbols (1 hundred, 5 tens, and 1 unit) using the place value system. In contrast, negative numerals first emerged in China in 200 BC as red and black rods used to symbolize debts and payments in business dealings. Scholars in the Arab world swiftly embraced modern numerals, which had their beginnings in India circa 100 BC. Indo-Arabic numerals were adopted by Europe throughout the Middle Ages, coinciding with the discoveries of Blaise Pascal and the Scientific Revolution. Although the Scientific Revolution benefited from discoveries made in antiquity, the quick advancement of this period fundamentally altered the way that data was employed.

Data gathered during this time transformed our understanding and study of the natural world, going beyond simple state accounting exercises. Historian of the Scientific Revolution John Henry states that the interpretation of the natural world shifted from reliance on ancient authority as the supreme source of knowledge to meticulous observations and exploratory experimentation. Source: The Scientific Revolution is a busy laboratory of experimentation in all areas of thought and practice, according to Lawrence Principle in The Scientific Revolution: A Very Short Introduction. This encompassed a notable surge in the quantity of individuals posing inquiries concerning the natural world, an abundance of fresh responses to those inquiries, and the creation of novel avenues for acquiring answers.

Data was not just used to count people and countries; it also served as the basis for groundbreaking new theories developed by renowned scientists like Copernicus, Galilei, Pascal, and Newton. John Graunt's 1662 work on Natural and Political Observations provides evidence of the increasing emphasis on data for interpreting external phenomena. based on the Mortality Bills. In response to a pandemic of public health in Europe, Graunt created the first life table, which approximated the likelihood of survival for various age groups. Graunt made an attempt to develop a warning system to stop the development of an epidemic plague in London by examining the weekly bills of mortality (deaths). Even though the technology was never put into action, Graunt's data processing experiment produced some intriguing results and a helpful population estimate for London.

Following the Scientific Revolution, automated data processing emerged thanks to mechanical inventions, progressively replacing the preponderance of human data collection methods. The Latin word datum, which means that is given, is where the word data originates. As new technology advanced in the 20th century, it also came to be associated more and more with computers. In 1946, the definition of data was broadened to include transmittable and storable computer information. The term data processing first arose in the 1950s, and the term big data entered common usage in the 1990s as a result of rapid advancements in database storage.

Even while data is now used on a daily basis by rank-and-file knowledge workers outside of the scientific community, genuine comprehension—referred to in this book as data literacy—lies in understanding what's hidden behind the data. Everything has an impact and determines the route from unprocessed data to business insight, including the source of the data, the selection of independent variables,