Odunola Atitebi

Using Excel, an analysis was carried out to identify the impact of COVID across five WHO regions. The most impacted and least impacted regions were identified. In addition, the recovery rate across this region was also recognised.

I recently analysed and visualised a dataset from a survey of professionals in the data field. The analysis suggested that the highest number of participants were in data science, which may indicate its growing popularity and demand.

On the other hand, the fields with the least number of participants appeared to be database developers and students, potentially reflecting lower representation or interest in these areas.

This analysis offers insights into the current landscape of data-related professions and could help guide future educational and career decisions in the data industry.

The visualisations created from this data provide a clear and engaging way to understand these trends and are included in my dashboard.

This project explored a dataset on USA population to identify trends in comorbidities. Determined the the association between hypertension, diabetes and heart diseases.In the dataset diabetes is the most prevalent condition, hypertension is the second prevalent condition and heart disease is the least prevalent. The prevalence of this conditions across different ethnic background was also assessed.

I worked with longitudinal data on Breast cancer radiotherapy patients in the REQUITE cohort. I used the R programming language to assess the correlation between two toxicities similar to the Clinician and Patient report (Breast pain and Breast swelling). Patients in the cohort consisted of people from diverse ethnic backgrounds (White/Mixed, Black/African, Jewish, Asian/Hispanic and Others) across seven countries: Belgium, France, Germany, Italy, Leicester, Spain and the United States).