Welcome to Data Science Methodology 101!
This is the beginning of a story -one that you'll be telling others about for years
to come.
It won't be in the form you experience here, but rather through the stories you'll be
sharing with others, as you explain how your understanding of a question resulted in an
answer that changed the way something was done.
Despite the recent increase in computing power and access to data over the last couple of
decades, our ability to use the data within the decision making process is either lost
or not maximized as all too often, we don't have a solid understanding of the questions
being asked and how to apply the data correctly to the problem at hand.
Here is a definition of the word methodology.
It's important to consider it because all too often there is a temptation to bypass
methodology and jump directly to solutions to come.
Doing so, however, hinders our best intentions in trying to solve a problem.
This course has one purpose, and that is to share a methodology that can be used within
data science, to ensure that the data used in problem solving is relevant and properly
manipulated to address the question at hand.
The data science methodology discussed in this course has been outlined by John Rollins,
a seasoned and senior data scientist currently practicing at IBM. This course is built on
his experience and expresses his position on the importance of following a methodology
to be successful.
In a nutshell, the Data Science Methodology aims to answer 10 basic questions in a prescribed
sequence. As you can see from this slide, there are two questions designed to define
the issue and thus determine the approach to be used; then there are four questions
that will help you get organized around the data you will need, and finally there are
four additional questions aimed at validating both the data and the approach that gets designed.
Please take a moment now to familiarize yourself with the ten questions, as they will be vital
to your success.
This course is comprised of several components:
There are five modules, each going through two stages of the methodology, explaining
the rationale as to why each stage is required. Within the same module, a Case Study is
shared that supports what you have just learned. There's also a hands-on lab, which helps
to apply the material. - And finally, there are 3 review questions
to test your understanding of the concepts.
When you're ready, take the final exam to earn your very first Data Science Level 1
badge!
The Case Study included in the course, highlights how the data science methodology can be applied
in context.
It revolves around the following scenario: There is a limited budget for providing healthcare
to the public. Hospital readmissions for re-occurring problems can be seen as a sign of a failure
in the system to properly address the patient condition prior to the initial patient discharge.
The Core Question is: What is the best way to allocate these funds to maximize their
use in providing quality care? As you'll see, if the new data science pilot
program is successful, it will deliver better patient care by giving physicians new tools
to incorporate timely, data-driven information into patient care decisions.
The Case Study sections display these icons (top right corner of the screen) to help you
differentiate theory from practice within each module.
A glossary of data science terms is also provided to assist with clarifying key terms used within
the course.
While participating in this course, if you come across challenges, or have questions,
then please explore the discussion and wiki sessions.
So, now that you're all set, adjust your headphones and let's get started!