data science life cycle

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Data Science Life Cycle
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TABLE OF CONTENTS
04
Preparation Of Data
01
Business Understanding
Exploratory Data Analysis
05
02
Data Understanding
Model Evaluation
06
Data Modeling
03
3
Business Understanding
The entire process circles almost the company
destination. What choice do you resolve if you
no longer maintain a specific issue? Knowing the
commercial company destination sincerely is
essential because it will be your most
incredible dream of the analysis.
4
Data Understanding
After business understanding, the next step is
data understanding. This has a series of all the
reachable data. Here you must operate intently
with the retail industry group as they know
current data, what facts should be used for this
marketable business problem, and other details.
5
Data Modeling
Data modelling is the coronary heart of data
analysis. A standard brings organized data as
input and provides the best outcome. This stage
consists of choosing the proper example, whether
the issue is a classification issue
degeneration problem.
6
Preparation Of Data
It consists of actions like selecting relevant
data, combining the data through connecting a
data group, cleaning it, regaling the low
values by either stopping them or attributing
them, feasting incorrect data via blocking them
and testing for outliers via the use of box
plots and coping with them.
7
Exploratory Data Analysis
The step includes bringing some concepts
regarding the solution and the factors impacting
it before completing the sample. The data
allocation inside unique qualities variables is
analysed graphically through line charts
connections between different parts are captured
through graphic expressions like scatter
properties and heat maps.
8
Model Evaluation
The model is calculated to determine whether it
is geared up to deploy. An example is read on
unnoticed data and evaluated on a thought-out
Carful group of estimation metrics. We also need
to make positive that the sample works to fact.
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Thank You
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