Welcome to Data Science Methodology 101 From Modeling to Evaluation Modeling Concepts!
Modelling is the stage in the data science methodology where the data scientist has the
chance to sample the sauce and determine if it's bang on or in need of more seasoning!
This portion of the course is geared toward answering two key questions:
First, what is the purpose of data modeling, and
Second, what are some characteristics of this process?
Data Modelling focuses on developing models that are either descriptive or predictive.
An example of a Descriptive model might examine things like: if a person did this,
then they're likely to prefer that.
A Predictive model tries to yield yes/no, or stop/go type outcomes
These models are based on the analytic approach that was taken, either statistically driven
or machine learning driven.
The Data Scientist will use a training set for predictive modelling.
A training set is a set of historical data in which the outcomes are already known.
The training set acts like a gauge to determine if the model needs to be calibrated.
In this stage, the Data Scientist will play around with different algorithms to ensure
that the variables in play are actually required.
The success of data compilation, preparation and modelling, depends on the understanding
of the problem at hand, and the appropriate analytical approach being taken.
The data supports the answering of the question, and like the quality of the ingredients in
cooking, sets the stage for the outcome.
Constant refinement, adjustments and tweaking are necessary within each step to ensure the
outcome is one that is solid.
In John Rollin's descriptive Data Science Methodology, the framework is geared to do
3 things: 1.
Understand the question at hand, 2.
Select an analytic approach or method to solve the problem, and
3.
Obtain, understand, prepare, and model the data.
The end goal is to move the data scientist to a point where a data model can be built
to answer the question.
With dinner just about to be served and a hungry guest at the table, the key questions
is: Have I made enough to eat?
Let's hope so.
In this stage of the methodology, model evaluation, deployment, and feedback loops ensure that
the answer is near and relevant.
This relevance is critical to the data science field overall, as it ís a fairly new
field of study, and we are interested in the possibilities
it has to offer.
The more people that benefit from the outcomes of this practice, the further the field will
develop.
This ends the Modeling to Evaluation section of this course, in which we reviewed the key
concepts related to modeling. Thanks for watching!