Business Understanding

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Из курса от партнера IBM

Data Science Methodology

67 оценки

IBM

Data Science Methodology

67 оценки

Курс 3 из 9 — Specialization IBM Data Science Professional Certificate

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! LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

Из урока

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.

Business Understanding5:02

Analytic Approach3:23

Познакомьтесь с преподавателями

Alex Aklson
Ph.D., Data Scientist
IBM Developer Skills Network

Welcome to Data Science Methodology 101 From Problem to Approach Business Understanding!

Has this ever happened to you?

You've been called into a meeting by your boss, who makes you aware of an important

task one with a very tight deadline that absolutely has to be met.

You both go back and forth to ensure that all aspects of the task have been considered

and the meeting ends with both of you confident that things are on track.

Later that afternoon, however, after you've spent some time examining the various issues

at play, you realize that you need to ask several additional questions in order to truly

accomplish the task.

Unfortunately, the boss won't be available again until tomorrow morning.

Now, with the tight deadline still ringing in your ears, you start feeling a sense of

uneasiness.

So, what do you do?

Do you risk moving forward or do you stop and seek clarification.

Data science methodology begins with spending the time to seek clarification, to attain

what can be referred to as a business understanding.

Having this understanding is placed at the beginning of the methodology because getting

clarity around the problem to be solved, allows you to determine which data will be used to

answer the core question.

Rollins suggests that having a clearly defined question is vital because it ultimately directs

the analytic approach that will be needed to address the question.

All too often, much effort is put into answering what people THINK is the question, and while

the methods used to address that question might be sound, they don't help to solve

the actual problem.

Establishing a clearly defined question starts with understanding the GOAL of the person

who is asking the question.

For example, if a business owner asks: "How can we reduce the costs of performing an activity?"

We need to understand, is the goal to improve the efficiency of the activity?

Or is it to increase the businesses profitability?

Once the goal is clarified, the next piece of the puzzle is to figure out the objectives

that are in support of the goal.

By breaking down the objectives, structured discussions can take place where priorities

can be identified in a way that can lead to organizing and planning on how to tackle the

problem.

Depending on the problem, different stakeholders will need to be engaged in the discussion

to help determine requirements and clarify questions.

So now, let's look at the case study related to applying "Business Understanding"

In the case study, the question being asked is: What is the best way to allocate the limited

healthcare budget to maximize its use in providing quality care?

This question is one that became a hot topic for an American healthcare insurance provider.

As public funding for readmissions was decreasing, this insurance company was at risk of having

to make up for the cost difference,which could potentially increase rates for its customers.

Knowing that raising insurance rates was not going to be a popular move, the insurance

company sat down with the health care authorities in its region and brought in IBM data scientists

to see how data science could be applied to the question at hand.

Before even starting to collect data, the goals and objectives needed to be defined.

After spending time to determine the goals and objectives, the team prioritized "patient

readmissions" as an effective area for review.

With the goals and objectives in mind, it was found that approximately 30% of individuals

who finish rehab treatment would be readmitted to a rehab center within one year; and that

50% would be readmitted within five years.

After reviewing some records, it was discovered that the patients with congestive heart failure

were at the top of the readmission list.

It was further determined that a decision-tree model could be applied to review this scenario,

to determine why this was occurring.

To gain the business understanding that would guide the analytics team in formulating and

performing their first project, the IBM Data scientists, proposed and delivered an on-site

workshop to kick things off.

The key business sponsors involvement throughout the project was critical, in that the sponsor:

Set overall direction

Remained engaged and provided guidance.

Ensured necessary support, where needed.

Finally, four business requirements were identified for whatever model would be built.

Namely:

Predicting readmission outcomes for those patients with Congestive Heart Failure

Predicting readmission risk.

Understanding the combination of events that led to the predicted outcome

Applying an easy-to-understand process to new patients, regarding their readmission

risk.

This ends the Business Understanding section of this course.

Thanks for watching!

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