University of Kentucky Data Modeling Mining & Analytics Discussions

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fcbbegul

Engineering

University of Kentucky

Description

1)From what you have done so far with Rapid Miner, describe a practical usage of data modeling in your line of work.

2)Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics.

3) What are the privacy issues with data mining? Do you think they are substantiated?


Explanation & Answer:
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Explanation & Answer

Hello buddy, check that one out as I work on the last one

Running head: DATA.

Data.
Student Name.
Institution Affiliation.
Date.

DATA.

Data.
Raw Data
Raw data refers to the unprocessed kind of data (Gitelman, 2013). This means that users of the
data may not be able to understand the data fully and this means that the data cannot be used for
analytics. Many would ask why can we not use this data to be used for analytics? The data is raw
this means that there are missing parts and some parts are irrelevant and others well are just unfit
to be used. The data is disorganized since it is the original data obtained from the field. In case
the data will be used for analyzing the results that will be obtained will not be the through value.
This will also take a lot of time since the data will be in large piles which most of it is irrelevant.
There are a series of steps that are carried out to ensure that the data is fit to be used for analysis
and they are called the data preprocessing steps.
Data Preprocessing Steps.
Four mains steps are involved in data preprocessing and they include the following (García,
Luengo, & Herrera, 2015).


Data cleaning – this is the first step that involves the removal of unnecessary data. It
entails the handling of missing data, duplication minimization, noisy data removal and
removing bias within the data.



Data integration – this is the step where the data from different sources are combined to
form consistent data.



Data transformation – this is the step where data is converted to the desired format.
Normalization, Aggregation, and Generalization are among the methods used in data
transformation.

DATA.



Data reduction – this step involves redundancy removal within the data. The data is
finally organized efficiently.

Importance of Data Analytics.
Data analysis is very important in almost every field. Analyzed data may be used to make a
prediction,...


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