In this section, I submit two assignments that demonstrate Competency F which include my INFM 203 mini-project on a canned craft beer dataset and my INFM 210 project on implementing a healthcare analytics program at a medium size hospital. Both of these projects focus around analytics and visualizations and how these can be applied to real-world fields. The INFM 203 and INFM 210 projects differ in their end-goals: the INFM 203 project is about my interactions with a particular dataset and how I intend to answer my questions using analytical methods, whereas the INFM 210 project centers around an implementation plan to introduce analytics into the daily operations of a hospital.

 

In the first project, the INFM 203 mini-project on a canned craft beer dataset, I presented on my interactions and impressions of a CSV dataset, what questions I wanted to ask about the data, explained some steps I took to conduct some exploratory analysis on the dataset, documented my future plans to conduct deeper analysis, and discussed what lessons I took away from the project. The dataset that I explored in this project contained data about different kinds of canned craft beers and their attributes: some of the attributes this dataset featured included alcohol content (abv or Alcohol by Volume), hop content (ibu or International Bitterness Units), beer name, style of beer, brewery id (linked to a second dataset which included the name of the brewery, city, and state brewery is in), and volume of beer in the can measured in ounces. In my presentation, I included sections that discussed the nature of the dataset such as where the dataset came from, how the dataset was originally created by the dataset author, and what the structure of the dataset looks like.

 

After explaining what the dataset was about and how it was organized, I then went on to present my own research. In my research I examined what insights I hoped to gain from the dataset by answering the right questions with the given data (i.e. is there a relationship between the style of a beer and is alcohol content in abv). To answer these questions, I described my processes with light, exploratory analysis and how I intend to conduct future heavier analysis. At the end of the presentation, I described how the mini-project challenged me to think in new ways and how it served as a learning experience by mentioning the skills and insights I gained, as well as what gaps I need to fill in my knowledge in the future. I demonstrated Competency F, in this first piece of evidence, because I was able to assume the role of a data scientist and identify and critically evaluate a dataset relating to a real-world industry (e.g. the brewing industry). I was also given the opportunity to conduct some preliminary data analysis and visualization and construct a future plan to carry out more dataset exploration.

 

In the second project, the INFM 210 project on implementing a healthcare analytics program at a medium size hospital, I presented on a hypothetical analytics implementation plan aimed at improving daily operations at Stanford Healthcare ValleyCare hospital, with the assumed audience being upper management. In the first part of this presentation, I explained what role analytics plays in the healthcare industry and built a case for having an analytics program at ValleyCare. After my argument on behalf of having a healthcare analytics program, I laid out the groundwork needed for the implementation plan. The implementation plan consisted of an iterative four-step lifecycle which is comprised of the following stages: assess, plan, execute, and check.

 

In this four-step cycle, I highlighted that ValleyCare must first begin with the assess step by asking itself what goals it wishes to accomplish from the program and gauge whether it is ready to commit itself to designing a solution. In the next step, plan, the hospital must work together at an organization-wide scale to decide what data it plans to use and what tools and processes will be required for analysis of that data. Following planning, in the execute step, ValleyCare needs to use the tools and processes it has decided on to gain insights. In the last step, check, the hospital evaluates the effectiveness of the analytics program by examining its strengths and weaknesses. Identifying some of the areas of improvement in the check stage is valuable since this feedback can be used to improve current processes, when it is time to restart the lifecycle again with the assess stage. This second piece of evidence demonstrates Competency F, because it examined the role that data analytics and visualization plays in the field of healthcare. Additionally, in this piece of evidence, I suggest a four-step analytics implementation plan which is inspired by the goals and outcomes of the iterative design methodology, which focuses on gradual improvements and optimization.