The Life Cycle Of Data Science

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THE LIFE CYCLE OF DATA SCIENCE
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INTRODUCTION
The Data Science Lifecycle is focused on the
application of machine learning and various
analytical methods to extract insights from data
to achieve a company goal. The complete
procedure includes several activities, such as
data cleansing, preparation, modelling, and
model evaluation. It is a time-consuming process
that could take months to complete
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Data Science Process
05 Modelling of Data 06 Evaluation of the
Model 07 Model Deployment
Business Understanding 01
Data comprehension 02 Data Preparation
03 Exploratory Data Analysis 04
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Business Understanding
The overall cycle revolves around the company's
objectives. Considering that the study's
ultimate goal is to fully understand the
business objective, this is essential. For
instance, You must determine if the consumer
wishes to estimate the rate of a commodity or if
he wants to minimise savings loss.
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Data comprehension
The following step is to obtain a better grasp of
the data after gaining a better understanding
of the company. Classifying the data, its
structure, its significance, and the types of
information it contains are all part of this
process. Data can be explored via graphical
charts. Basically, you can extract any facts
about the information by simply viewing the data.
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Data Preparation
In this system, relevant data is selected,
integrated by merging data sets, cleaned,
handled by removing or imputing missing values,
treated by removing incorrect data, and tested
for outliers with box plots and dealt with.
Constantly making data and obtaining new
elements from old data.
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Exploratory Data Analysis
This process involves getting a rough notion of
the behavior and the factors that influence it
before creating the true model. Then, the
correlations between various features are
represented using graphical representations such
as scatter plots and warmth maps. Data
distribution within various character variables
is graphically explored using bar graphs.
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Modelling of Data
This stage is all about selecting the right
model, whether the task is classification,
regression, or clustering problem. Algorithms
must be carefully chosen after deciding on the
number of algorithms in a model family and on
the model's family structure.
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Evaluation of the Model

• The model was examined using a meticulously
  developed set of evaluation criteria and tested
  utilising previously unreported data.
• Furthermore, we must ensure that the model is
  correct. If the evaluation does not give a
  satisfying result, the entire modelling method
  must be repeated until the necessary level of
  metrics is achieved.

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Model Deployment

• After a thorough evaluation, the model is
  finally implemented in the structure and channel
  of your choice. The data science life cycle
  comes to an end with this step.
• Each phase of the data science life cycle
  mentioned above must be carefully considered. If
  one phase is done incorrectly, it will influence
  the next stage, resulting in a loss of time and
  effort. If data isn't collected properly, you'll
  lose records and won't be able to create an
  ideal model. If the data are not sufficiently
  cleaned, the model will stop functioning.

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