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Data Analytics

Simple and Effective Tips and Tricks to Learn Data Analytics Effectively

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:

You cannot amend, distribute, sell, use, quote, or paraphrase any part of 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. 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. 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 the information contained within this document, including, but not limited to, errors, omissions, or inaccuracies.

Table of Contents

Chapter 1: Data Analytics Tips and Tricks for Beginners

Introduction

Two of the most prominent and frequently used terminologies in the present era are Data Science and Data Analytics. It will not be an exaggeration if we say data is like oil for industries. Normally, data is gathered into a crude form and then processed according to the needs of a company. This data is processed so that it can be used for the purpose of better decision making. This very same process plays a critical role in expanding the operations of a company or an organization in a broad market. When we say the process, it means Data Analytics. And those that perform these processes and operations are called data analysts and data scientists.

Normally, the data sometimes also called the information available in raw format. The rapid expansion of data in recent years has increased the need to perform a detailed inspection, data cleaning, and modification, and better modeling of data to get the most out of the data for effective conclusions and ultimately to make better decisions. This process as a whole is called data analysis.

Analytics can be defined as: Combination of raw facts and figures of business, statistics, mathematics, data modeling, data mining, machine learning, and artificial intelligence used to obtain the outcomes to make best business strategies. It is a broad knowledge and divided into the following categories:

Descriptive analytics: This category relates to the past and explains what has happened in the past.

Diagnostic analytics: It tells about the reasons for the events that happened.

Predictive analytics: Predictive deals with the future forecasting depending upon past events.

Prescriptive analytics: It provides important pieces of advice for future events.

Figure 1: Data Analytics

Data Mining

There are various ways for data analysis to do efficient data modeling, but the most popular way is Data Mining. Data Mining is also good for knowledge discovery related to predictive purposes. Business Intelligence processes give forth to multiple data analysis abilities that depend on data aggregation and also give due attention to the domain process of the businesses. When it comes to Statistical Applications, there are two types of business analytics i.e. EDA (Exploratory Data Analysis) and CDA (Confirmatory Data Analysis).

The main focus of EDA is to find up to date features in the data while the CDA focal point is either affirming or falsifying the currently existing hypothesis. Predictive Analytics focuses on forecasting or categorization by utilizing statistical models. Besides statistical models, other tools including the in-text analytics, linguistic, and various structural methods are used to obtain and categorize information out of textual sources. All the above-given tools are a few kinds of data analysis.

The improved data wave has changed its working in various positive ways. With time, different requirements are making an appearance for using complex and more efficient analytical ways to the Big Data spectrum. Therefore, experts are now able to make accurate and beneficial decisions.

There is a lot of confusion between Data Analysis and Data Reporting. Lets have a look at their differences and other functionalities.

Data Analysis vs. Data Reporting

When we say analysis, it means the interactive process of a person to manage a problem, searching the appropriate data, analyzing that searched data to get a suitable answer to the problem, and finally explaining the results to give a suitable recommendation for taking action.

The reporting environment, sometimes also called a business intelligence environment includes calling and reporting the execution. This way the outputs are then obtained in a required form. Here reporting means the process of structuring and summing up the data in easy to understand format and convey critical information.

Reports play an important role in monitoring different fields of performance and boosting the customers' satisfaction in an organization. Sometimes report also includes the reshaping of raw data in the form of useful information. The same is true for analysis which changes the information into important and useful insights.

Key Differences between Data Analysis and Data Reporting

• A report informs a user about the past happenings, allowing them to ignore inferences and get the proper idea of the data. On the other hand, the analysis gives answers to under review questions or issues. An analysis process is not as limited as it includes all types of steps necessary to obtain the answers to the prevailing questions.
• Reporting gives you the requested data, and analysis normally gives answers to what is needed actually.
• A report is always compiled in a standardized way, but the analysis can be tailor-made. This means reporting is done in fixed standard formats but for analysis, we do it as per our requirement and its customization is easier to perform.
• Generally, no person is required in reporting as it is possible to perform reporting via using a tool. But a person is needed for performing analysis as well as completing the whole analysis process.
• Reporting is considered inflexible; on the other hand, analysis is very flexible. The reason behind reporting being inflexible is it gives no or very limited context related to whats taking place in the data. At the same time, the analysis highlights important and unique data points and elaborates on their importance to the business.

Data Analysis Process

In the Data Analytical process, we will learn how data is normally analyzed with step by step guide.

Figure 2: Data Analysis Process

Understanding the Business

Any Data Analysis process for a business cant begin properly without having deep knowledge of that particular business. When we say understanding the business, it means knowing the objective and goals of that business. The process of understanding the business also includes: knowing the prevailing situation, decide the goals of data mining, and bring forth the project plan according to the requirements. Overall, the objectives of the business are defined in this first step.

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