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The Road to 4IR (4th Industrial Revolution) Read chapter Read first chapter

2022 | OriginalPaper | Chapter

8. Data Analytics and Data Mining

Author : Pratim Milton Datta

Published in: Global Technology Management 4.0

Publisher: Springer International Publishing

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Abstract

Data analytics and data mining begin with data collection. Data mining is the process of extracting (E) data from databases, transforming (T) data following a predefined structure, and loading (L) data for performing various analytics. Data mining is commonly associated with big data because it is the basis for systematic analytics for describing and predicting based on data.

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previous chapter Tying Strategic Analytics to Data Analytics
next chapter Introduction to Cybersecurity
Footnotes
1
It is best to run a regression without transformations. Once we have run regression analysis on the raw data. Construct a scatterplot with residuals vs. predicted values. If the scatterplot looks random, don’t transform; transform only when the scatterplot has a pattern.
 
2
For Logistic and non-normal regression, assumptions of (i) Response/dependent variable does not have to be normally distributed, (ii) independent variables do not need to be homoscedastic, (iii) Errors do not need to be normally distributed, and (iv) Independent variables can be of any type (e.g., interval, ratio, ordinal, categorical…).
 
3
You will find Solver under Tools. If you don’t have it there, click on add-ins and make sure that Solver is checked. Else, find it as solver.xla and add it in and then check the box.
 
Metadata
Title
Data Analytics and Data Mining
Author
Pratim Milton Datta
Copyright Year
2022
Book
Global Technology Management 4.0

Print ISBN: 978-3-030-96928-8

Electronic ISBN: 978-3-030-96929-5

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-96929-5_8