Data Science Methodology Overview

 Welcome to the overview of data science methodology. By the end of this video, you'll gain an understanding of the term methodology, its relevance to data science, John Rollins's contributions to data methodology, the 10 stages of standard data methodology, and the questions associated with each stage.

Data science has emerged as a powerful discipline that integrates statistical analysis, technological proficiency, and domain knowledge to extract valuable insights from large datasets. Despite advancements in computing power and data accessibility, understanding the questions posed and applying data effectively to solve problems remains a challenge. This is where methodology plays a crucial role.

So, what exactly is a methodology? In simple terms, it's a set of methods used within a specific field of study. In the context of data science, methodology serves as a structured approach guiding data scientists in solving complex problems and making informed, data-driven decisions. It encompasses data collection forms, measurement strategies, and comparisons of data analysis methods tailored to various research objectives and scenarios.

Adhering to a methodology provides practical guidance for conducting scientific research efficiently. While there may be a temptation to skip methodology and jump straight to solutions, doing so often impedes problem-solving efforts.

Now, let's delve into methodology as it relates to data science. The data science methodology discussed in this course is based on the framework outlined by John Rollins, an esteemed IBM Senior Data Scientist. Drawing from his professional experience, this course emphasizes the importance of following a structured methodology for achieving successful data science outcomes.

The data science methodology comprises 10 stages:

Business Understanding

Analytic Approach

Data Requirements

Data Collection

Data Understanding

Data Preparation

Modeling

Evaluation

Deployment

Feedback

Questions serve as the cornerstone of data science methodology, guiding each stage of the process. Let's break down the 10 fundamental questions aligned with the stages:

What is the problem you're trying to solve, and how can data address it?

What data do you need, where is it sourced from, and how will you acquire it?

Does the collected data accurately represent the problem, and what additional steps are needed for data manipulation?

Do data visualizations provide insights aligned with the business problem?

Does the data model effectively address the initial question, or does it require adjustment?

Can the model be implemented in practice?

Can constructive feedback be obtained from stakeholders to refine the approach?

In summary, data science methodology serves as a guide for data scientists in navigating complex problems with data. It encompasses various stages and questions designed to ensure thorough problem-solving and informed decision-making throughout the process.