Capilla del Rosario, Santo Domingo Church, Quito,
Ecuador
Photo
by Jonathan Acuña (2018)
Gray Matter
in Data Science
Some Insights
in Methodology
By Prof. Jonathan Acuña-Solano, M. Ed.
School of English
Faculty of Social Sciences
Universidad Latina de Costa Rica
Saturday, August 3, 2019
Post 335 / DS Log 6
When one talks about information found
in Big Data, info can be referred as gray
matter. Why gray? The answer is simply; it turns gray because in the process
of making use of Data Science, when a question is asked, the response cannot be
referred as black or white. In other words, the grayness is
the result of having various ways of answering a question (problem) stated by
stakeholders. Based on Dr. Mustaza Haider (Laureate Education Inc., 2018) , a data scientist will
not find answers clearly. Responses won’t be found quickly and easily; they are
the gray matter that needs to be molded into the answers that institutions need
to describe what is currently happening or what could come in the future.
Based on Laureate Education Inc. (2018), Data Science Metholodology has to follow
certain sequential steps to “yield” answers to questions made by stakeholders
in an institution. This methodology is here to turn gray matter into a visible
black-and-white “object” that can be manipulated, analyzed, and understood.
Take a look at the methodological model.
1
|
Business
Understanding
|
The process begins with the
search for clarification about the research focus.
|
2
|
Analytic
Approach
|
Next step is to find
clarification from the stakeholders who are asking the question.
|
3
|
Data
Requirements
|
After that, the data
scientist prepares the parameters to meet the desired outcome.
|
4
|
Data
Collection
|
The following phase consists
of the actual collection of data to answer the question.
|
5
|
Data
Understanding
|
Next stage refers to using
the collected data to construct the data set for cleansing.
|
6
|
Data
Preparation
|
The subsequent maneuver is
the actual cleansing of the data from “dirty data.”
|
7
|
Modeling
|
Next procedure implies the
development of a descriptive or predictive model for the data.
|
8
|
Evaluation
|
Next in line is to evaluate
the depurated data to see if it provides insight into the question.
|
9
|
Deployment
|
Our before-last step is to “move
strategically” the collected data to “push out” an answer.
|
10
|
Feedback
|
Finally, refinement of the
model created is ready to run or to get adjusted.
|
Created by Prof. Jonathan Acuña with information
from Laureate
Education Inc. (2018)
|
||
Parallel to these methodological
considerations, the following infographic presents what happens when these
processes (or methodological steps) are transformed into questions that need to
be fully answered to get to provide an answer to what is being asked by institutional
stakeholders.
To conclude, the Data Science
Methodology used to answer a question is precise and must be followed to the
very detail. Doing otherwise will probably trigger answers that do not provide
any light to problem a company wants to explain or an issue that can
materialize in the short or long run.
References
Laureate Education Inc. (2018). Asking Questions
with Data Science. Retrieved from One Faculty:
https://dtl.laureate.net/webapps/blackboard/content/listContent.jsp?course_id=_165016_1&content_id=_801203_1&mode=reset
Laureate Education Inc. (2018). Things Data Science
People Say. [Video File]. Retrieved from https://dtl.laureate.net/webapps/blackboard/content/listContent.jsp?course_id=_165016_1&content_id=_801203_1&mode=reset
Gray Areas in DS by Jonathan Acuña on Scribd
Saturday, August 03, 2019
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