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Data Analytics. Fast Overview.

Data Analytics. Fast Overview.

By George L.

Shakespir Edition

Copyright © 2017 by George L.

Shakespir Edition, License Notes

Thank you for downloading this ebook. You are welcome to share it with your friends. This book may be reproduced, copied and distributed for non-commercial purposes, provided the book remains in its complete original form. If you enjoyed this book, please return to your favorite ebook retailer to discover other works by this author. Thank you for your support.

 

 

 

 

 

 

=> Click here to get access to a FREE BONUS VIDEO COURSE to discover more about Data Analysis in Excel

 

 

 

 

 

 

 

 

 

 

 

 

Introduction

Thank you for taking the time to download my book: “Data Analytics. Fast Overview” This book is meant to provide you with everything you need to know about the ins and outs of data mining.

 

This book has been laid out in straightforward and clear chapters with each chapter focusing on a particular part of data science for business to be able to ensure that you gain the maximum amount to knowledge without having to weed through unnecessary information. I hope this book answers any question you have and leaves you feeling confident on the subject of data science, data analytics and business intelligence.

 

[P.S. FREE BONUS VIDEO COURSE in the end.*]

 

 

 

 

 

Chapter 1

Wholeness of Data Analytics

There is a lot of data that comes rushing towards an organization of any type and sometimes it can be hard to decipher just what it means to the team and how they can use it to benefit them. This is where data analytics is the more helpful. The data is analyzed through a process of inspecting, cleaning, transforming and modeling that makes the information easier to look at and read. By narrowing down the amount of information, an organization is looking at they are going to be better able to utilize the relevant information and use the conclusions the data suggests to make decisions that are most likely to bring rewards.

 

Although data analytics are most frequently used in business to consumer applications, there are many different facets of the data analysis. Some of the most common places data analytics are utilized in the worlds of business, science, and social science, in a variety of ways. Regardless of the type of organization, you are involved with, and even in your personal life, there are ways to make data analysis work for you.

 

An example of where data analytics would be used in regards to a social networking site. A social networking website collects the information which relates to user preferences as well as the community interested and can segment according to the criteria that have been specified such as age, gender or where the community member lives.

 

 

 

 

 

Chapter 2

Business Intelligence Concepts & Applications

Business intelligence is the gathering of business data through asking questions to find information. To be able to gain business intelligence, querying, reporting, analytical processing, and business analytics are combined to bring statistics, prediction, and optimization together to enable the functionality to be reported.

 

There is five primary applications business intelligence can be used in business to increase the value.

 

1 – Analytics: This is a part of business intelligence that allows for a company to arrive at decisions that are based on knowledge.

2 – Collaboration/Collaboration Platform: This part of business intelligence allows different areas from inside and outside the business to work together through the sharing and interchanging of data.

3 – Knowledge Management: This is the aspect of business intelligence that allows a company to be driven by the data they must create strategies and practices that enable the business to have accurate information about how they are doing.

4 – Measurement: Business intelligence allows a hierarchy of performance metrics and benchmarking to be created and inform business leaders of the progress that is occurring in their business.

5 – Reporting/Enterprise Reporting: This branch of business intelligence builds infrastructure for strategic reporting. This serves the strategic management of the business in a way operational reporting cannot.

 

Business intelligence is also capable of providing a proactive approach to running a business by notifying the end user when certain conditions are being met and when they are not.

 

 

 

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Chapter 3

Data Warehousing

Data warehousing is a core component of business intelligence and is necessary to get the answers a business needs to be successful. Data warehousing is the electronic storage of large amounts of information. This information is stored in a secure, reliable, and easy to retrieve and manage location. Often, businesses decide to warehouse data to be able to find patterns of information that can help them to improve their business functions.

 

An alternative to data warehousing is a data mart. A data mart is a simple version of a data warehouse that is used to focus on a simple subject and the data drawn from a data mart is based on a limited number of sales. Often, a data warehouse is divided up into data marts to allow different areas of a business to use the information they need.

 

Businesses approach data warehousing in two different ways. One approach works from the top-down and the other looks at it from the bottom up. The top-down approach first collects all the data and then sorts it into data marts to be sourced to select groups. The bottom-up way builds data marts first and then merges them into a single data warehouse.

 

 

 

Chapter 4

Data Mining

Once all the information for a company has been compiled into a data warehouse, the next step is to mine the data to get the information that you need. Data mining is the process of finding the patterns in the large set of data. There are six standard classes of tasks in data mining.

 

1 – Anomaly Detection is the process of identifying unusual data records that are going to require further investigating to find out if they are merely interesting differences or errors in the data.

 

2 – Association Rule Learning requires searching for relationships between the variables in the data warehouse. This is often referred to as a market basket analysis.

 

3 – Clustering is the process of finding groups and structures in the data that are similar without placing known structures in the data.

 

4 – Classification requires the generalization of the known structure to be able to apply more data to the system.

 

5 – Regression is the attempt to find a function within the data warehouse that models the data with the least percentage of error.

 

6 – Summarization is used to provide a representation of the data that is compact. This is usually accomplished with the use of data visualizations such as graphs and report generations.

 

It is important to know that often data mining is unintentionally misused, and produces data that appears significant but is not an accurate prediction of future behavior.

 

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Chapter 5

Data Visualization

Data visualization is used to describe any visual context that information is put into to help people better understand it. Patterns, trends, and correlations are often missed when they are in text-based data, while data visualization makes them significantly easier to see. Data visualization is often used to communicate information in a clear and efficient manner. Many different forms of visualization charts can be used, depending on what sort of information you want to share.

 

The first thing you need to know is if you are presenting a comparison, composition, distribution, or relationship. Once you know what type of presentation you want, you need to consider a couple of other variables.

- How many variables do you want a single chart to show?

- How many data points are you going to display for each of those variables?

- Are you going to display values over a specific time or among items or groups?

 

Using the right chart for the information being presented is an essential piece of data visualization as information can get lost when shown in the wrong format.

 

Some of the types of charts that are frequently used for data visualization are listed here:

3D Area Chart

Bar Chart

Bubble Chart

Circular Area Chart

Column Chart

Column Chart

Column Histogram

Line Chart

Line Chart

Line Histogram

Pie Chart

Scatter Chart

Stacked Area Chart

Stacked Column Chart

Table

Table with Embedded Charts

Variable Width Column Chart

Waterfall Chart

 

 

Chapter 6

Decision Trees

A decision tree is one way to display an algorithm. Most commonly for research and analysis, it is used to find a strategy that will assist in reaching a goal by listing decision and their possible consequences. Decision trees are very like a flowchart, where each node stands for a test on the attribute, each branch stands for the outcome of a test, and a leaf node stands for a class label.

 

There are three different nodes that can be utilized in a decision tree, as a general rule, these remain the same for all decision trees that are created to allow for the natural reading of the chart. A square represents a decision node, a circle accounts for a chance node, and a triangle represents an end node.

 

A decision tree is always drawn from left to right and only has splitting paths (burst nodes) and no converging paths (sink nodes). Decision trees tend to get very large and are often not drawn by hand.

 

In general, a decision tree shows that if condition one and condition two and condition three then outcome happens. The advantage to using a decision tree is that they are simply to understand and interpret and they quickly help determine the best, worst and expected values for different scenarios. On the other hand, decision trees are not always the best choice if many benefits are uncertain or many outcomes are linked.

 

 

 

Conclusion + FREE BONUS VIDEO COURSE

 

 

 

Thank you for downloading my book “Data Analytics. Fast Overview.” I hope the information in this book was able to help you better understand data mining and data-analytic thinking.

 

Finally, if you enjoyed this book, then I’d like to ask you for a favor, would you be kind enough to leave a review for this book? It’d be greatly appreciated!

 

FREE BONUS VIDEO COURSE

Now that you have completed this book, you should be aware of all the basics of data science and how it relates to the business world from data warehousing and data mining to data visualization and decision trees. With this knowledge, you are going to be able to make business decisions that are based on actual collected data and facts in a timely fashion. I recommend you check out this FREE BONUS VIDEO COURSE for a more complete information.

If this link does not work go here: http://datasciencebonus.weebly.com

 

 

Thank you and good luck!

 

 

 

 

 

 


Data Analytics. Fast Overview.

You want to learn Data Analytics FAST but you don’t know where to start? And you don’t have any previous experience? This book is an overview for anyone interested in learning about data analytics. This book covers all aspects of Big Data Analytics in a nutshell. It will give you all the basics, the techniques for data analysis, some algorithms, and a lot more. If you just want to get a very basic idea of data science, then this book is definitely for you. It is an ideal book for people in business who want to better understand the power, capability and value of data analytics and how to access gold mine. It would also serve well as a textbook for students in the field of information technology or business administration. You Will Discover: -Data Visualization -Decision Trees -Data Warehousing -Business Intelligence Concepts -And Much, Much More... You want to learn the basics of Data Analytics in the shortest possible amount of time? You better grab this book and start reading​ it up!

  • Author: George L.
  • Published: 2017-03-24 16:05:11
  • Words: 1796
Data Analytics. Fast Overview. Data Analytics. Fast Overview.