Business Analytics graduate with expertise in programming, machine learning, and data analytics. Strong communication and time management skills, with a track record of boosting productivity in fast-paced teams. Ideal ca…
Business Analytics graduate with expertise in programming, machine learning, and data analytics. Strong communication and time management skills, with a track record of boosting productivity in fast-paced teams. Ideal candidate for data analyst or scientist roles.
Developed custom layouts, forms, reports, and time-triggered surveys, improving volunteer campaign dashboards and boosting efficiency by 30%. Managed data cleaning and governance to ensure accurate reporting on employee registration and participation. Conducted staff training, clarified reporting metrics, and developed FAQs. Implemented cloud and backup solutions for data preservation. Managed Salesforce volunteer data integrated with Shopify for accurate verification. Oversaw order management for subscriptions and one-time purchases, ensuring smooth operations. Built dynamic intake forms and program-specific pages using Visualforce, Apex, JavaScript, and Bootstrap for Youth, Entrepreneurship, and Market initiatives. Streamlined data entry workflows, reducing manual input time from 4 hours to 30 minutes and enhancing data accuracy and user experience through improved backend validation.
Addressing student queries
Designing exams
Assisting with assignments
Co-developing course content using R and Julia
Analysed 3,000+ patient records using Python to differentiate Myasthenia Gravis cases, achieving 73.85% Random Forest accuracy
Applied k-means clustering to stratify patients by risk
Visualized demographic trends, diagnosis codes, and claim-RAF score correlations in Tableau and Power BI
Implemented a Random Forest Classifier to identify potential Myasthenia Gravis cases among a non-Myasthenia Gravis patient cohort with 81% precision, using transformed categorical features to aid early diagnosis and treatment planning
Analysed 3,000+ patient records using Python to differentiate Myasthenia Gravis cases, achieving 73.85% Random Forest accuracy. Applied k-means clustering to stratify patients by risk and visualized demographic trends, diagnosis codes, and claim-RAF score correlations in Tableau and Power BI. Implemented a Random Forest Classifier to identify potential Myasthenia Gravis cases among a non-Myasthenia Gravis patient cohort with 81% precision, using transformed categorical features to aid early diagnosis and treatment planning.
Developed custom layouts, forms, reports, and time-triggered surveys, improving volunteer campaign dashboards and boosting efficiency by 30%
Managed data cleaning and governance to ensure accurate reporting on employee registration and participation
Conducted staff training
Clarified reporting metrics
Developed FAQs
Implemented cloud and backup solutions for data preservation
Managed Salesforce volunteer data integrated with Shopify for accurate verification
Oversaw order management for subscriptions and one-time purchases, ensuring smooth operations
Built dynamic intake forms and program-specific pages using Visualforce, Apex, JavaScript, and Bootstrap for Youth, Entrepreneurship and Market initiatives
Streamlined data entry workflows, reducing manual input time from 4 hours to 30 minutes
Enhanced data accuracy and user experience through improved backend validation
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