Introduction

The goal of this project is to get more hands-on practice with SAS and to see about doing some exploratory data analysis using a sample consisting of 10,000 individuals in relationship to diabetes. There is a comparison between diabetic and non-diabetic individuals in relationship to body mass index (BMI), blood glucose levels, heart disease, hypertension, smoking habits, and age. General statistics comparing the differences between diabetics and non-diabetics are examined. Looking into the age demographics of diabetics in relationship to current smoking status will be explored. Lastly, there are some insights as to which factors are likely to lead to diabetes.

Methodology

This project will follow a Power Point presentation format. There is short introduction as to the content of each slide or series of slides. There will be an image of the code that is used to generate the statistical output for the variables examined. There will also be charts with explanations as to clarify what the chart is showing.

Data Analysis, Visualization, and Insights

Recommendations

A large part of working in data analytics is being able to use various technologies to draw insights from the data. For this project, I decided to use SAS to see what insights I can gain from the data. Practicing with one data tool makes learning and or becoming more proficient in other data tools much easier. There can be many similarities when working with the various data analytic tools out there.

Further work on this project will include investigating the relationship between BMI, blood glucose levels, and diabetic status. This process will likely involve creating a series of dummy columns. Many factors are involved when it comes determining the risk factors involved with developing diabetes. Knowing which factors are most associated with the disease can enable both physicians and patients to come up with important lifestyle changes that can help reduce the risk of developing the disease.