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1. THE LIFE CYCLE OF DATA
SCIENCE

2. 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

3. Data Science Process
Business Understanding
Data comprehension
Data Preparation
Modelling of Data
Evaluation of the Model
Model Deployment
01
02
03
05
06
07
Exploratory Data Analysis
04

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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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