Practicing R skills and exploring correlations with a fictional data set created by IBM data scientists.
Creating visualizations from the NBA’s data on the 2021-2022 season using Tableau.
Conducting an exploratory data analysis on a healthcare data set. The goal being to uncover several trends around on how long patients are admitted for on average, what clinics are providing a bulk of the services delivered, and if race plays a role in the level of treatment patients recieve.
Describing how I used SQL commands to analyze the IDA dataset, which provides information on loans, grants, and guarantees provided by the IDA to member countries. Sharing several key insights, including the total number of transactions, loans issued to Nicaragua, the largest transaction recorded, and the average interest rate for loans issued. All to demonstrate that SQL is a powerful tool for extracting insights from large datasets.
Here I analyzed an educational dataset to determine if there were any correlations between average class size and percentage of students accepted into college, if economic status has anything to do with that, and identifying underperforming schools.
In this case study from Data Analytics Accelerator, I was prompted to analyze an iFood (Brazilian equivalent to DoorDash) customer data. The main focuses were: What were the ages of the customers? How much does a customer spend relative to their income? How does income level influence the total amount spent in the app?