Statistics: Practical Concept of Statistics for Data Scientists

By John Slavio

Statistics are not a tool but rather a set of techniques that you have access to that will help you analyze a set of data that you either generate, receive, or give. Statistics are absolutely vital for those attempting to study Big Data because it allows the scientists studying the data to make sense of the information when the information is on such a large and global scale. Unlike local neighborhood statistics or marketing statistics, big data encompasses a huge range of information and often this big data will be populated by thousands if not millions of data points. Statistics help you break down these data points so that you can reasonably understand them and work with the data that comes into you. 
 

Here's What's Included In This Book

Basics of StatisticsExploratory Data AnalysisDifferent Sampling MethodsDifferent Types of Structured DataRun Charts and Statistical Process ControlVariation AnalysisPractical Application of Statistics

• Language: English
• Publisher: Abhishek Kumar
• Release date: Nov 7, 2017
• ISBN: 9781979455473

Book preview

Statistics

Author: John Slavio

TABLE OF CONTENTS

Exploratory Data Analysis

Basics of Statistics

Different Types of Structured Data

Run Charts and Statistical Process Control

Variation Analysis

Practical Application of Statistics (Above Tools)

Conclusion

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DISCLAIMER

Copyright ©Kumar 2017

All Rights Reserved

No part of this eBook can be transmitted or reproduced in any form including print, electronic, photocopying, scanning, mechanical or recording without prior written permission from the author.

While the author has taken the utmost effort to ensure the accuracy of the written content, all readers are advised to follow information mentioned herein at their own risk. The author cannot be held responsible for any personal or commercial damage caused by information. All readers are encouraged to seek professional advice when needed.

ABOUT THE AUTHOR

John Slavio is a programmer who is passionate about the reach of the internet and the interaction of the internet with daily devices. He has automated several home devices to make them 'smart' and connect them to high speed internet. His passions involve computer security, iOT, hardware programming and blogging. 

Exploratory Data Analysis

What is EDA?

Exploratory data analysis is not a tool but rather a set of techniques that, if possessed, will help one analyze a set of data that had been previously generated, received, or given. Exploratory data analysis is absolutely vital for people attempting to study big data because it allows them to make sense of information on a large or global scale. Unlike local neighborhood statistics or marketing statistics, big data encompasses a huge range of information and is often populated by thousands if not millions of data points. Exploratory data analysis helps to break down these data points so that they can be reasonably understood and put into good use.

What Can EDA do?

Insights into a Data Set

Exploratory data analysis helps in two different ways. By presenting a graphical view of the data set being analyzed and the quantitative view that the data can be seen from. With the graphical view of the dataset, one can begin to see trends in data without a need to run algorithms to see if such a pattern exists. By using Scatter Plots or bar graphs, one can also easily gauge where certain commonalities are occurring for better predictions and understanding of the data being studied. With the quantitative view, one is able to take those graphical views and explore them in depth while also determining the reasoning behind the patterns that can be seen in the dataset. Additionally, the quantitative view of the dataset may be used to test hypotheses and provide an estimated guess as to what future data will look like during certain intervals.

See Underlying Structure

Thanks to the fact that the information can be viewed in a more graphical form, you can begin to understand the underlying structure that comprises the variables present in such information. When viewing the raw data format, all one sees is a large mass of numbers that doesn't really provide a cohesive relationship of everything that is going on inside of those numbers but whenever the graphical form of the numbers is adapted, an understanding of how these numbers come into existence is realized. One can then take it even further by using quantitative formulas to break down the data points and test hypotheses so as to see the true underlying structure that make up the data points, how those data points got to that location and how those data points