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From the course by IBM

Data Science Methodology

32 ratings

IBM

Data Science Methodology

32 ratings

Course 3 of 4 in the Specialization Introduction to Data Science

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 at 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. 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. Accordingly, in this course, you will learn: - The major steps involved in tackling a data science problem. - The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment. - How data scientists think!

From the lesson

From Problem to Approach and From Requirements to Collection

In this module, you will learn about why we are interested in data science, what a methodology is, and why data scientists need a methodology. You will also learn about the data science methodology and its flowchart.You will learn about the first two stages of the data science methodology, namely Data Requirements and Data Understanding. Finally, through a lab session, you will learn how to complete the Business Understanding and the Analytic Approach stages as well Data Requirements and Data Collection stages pertaining to any data science problem.

Meet the Instructors

Alex Aklson
Ph.D., Data Scientist
IBM Cognitive Class

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.

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 practising 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.

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 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 at the top right hand corner of your 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!

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