Final project for data science tutorial (taken in third-year of undergrad, fall semester 2022)
Combining everything we learned in python & jupyterhub to analyze a real-world dataset:
- data manipulation (reduced tables using only select variables, mean & median, sorting)
- graphs (bar graphs, histograms, scatter plots)
- correlation coefficient
$r$ (standard units) - classification (k-nearest neighbors, splitting the data into training vs testing sets, classify function, etc.)
The dataset represents data from the study by Turan et al. “Relationship between Chronic Intermittent Hypoxia and Intraoperative Mean Arterial Pressure in Obstructive Sleep Apnea Patients Having Laparoscopic Bariatric Surgery”. Anesthesiology 2015; 122: 64-71.
This study retrospectively examined the intraoperative blood pressure in patients who had laparoscopic bariatric surgery. Specifically, testing the hypothesis that nocturnal intermittent hypoxia consequent to OSA are associated with decreased intraoperative mean arterial pressure (MAP).
This project was a fun and engaging way to implement what we have learned in this data science tutorial and apply it to real-world problems. While we may not have been able to find a significant relationship between our variables (as demonstrated by the clinic), it was still fun to play with the data and visualize it in different ways.