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.
The purpose 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 here has been outlined by John Rollins, a seasoned and senior data scientist. The Data Science Methodology aims to answer 10 basic questions in a prescribed sequence. The case study included here, 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 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 the 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!