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 Rollins' descriptive Data Science Methodology, the framework is geared to do

3 things: First,

understand the question at hand. Second,

select an analytic approach or method to solve the problem, and

third,

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 question

is: Have I made enough to eat?

Well, 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!