Motivated Junior Data Analyst proficient in Python, SQL, VBA, and Excel. Experienced in developing databases, data validation, KPI dashboards, and predictive modeling. Skilled in data visualization and process improvemen…
Motivated Junior Data Analyst proficient in Python, SQL, VBA, and Excel. Experienced in developing databases, data validation, KPI dashboards, and predictive modeling. Skilled in data visualization and process improvement. Strong analytical skills with the ability to translate complex data into actionable insights. Proficient in data visualization tools like Tableau and Power BI, with experience in building data models, dashboards, and reports. Proven ability in code development for data analysis. Experienced in statistical modeling with Microsoft Power BI, and conducting predictive analytics.
Gathered, organized, and analyzed data to support IT project requirements, ensuring data lineage and transformations were accurately documented.
Developed and delivered functional and ministry-specific analytics using Power BI and Python, optimizing statistical efficiency and data quality.
Improved data validation processes in Excel, ensuring accurate and reliable data entry for evidence-based decision-making.
Conducted monthly financial and operational reporting, preparing journal entries, contribution margin analytics, and collaborating with stakeholders on process improvements.
Collaborated with internal stakeholders (e.g., Investment Operations, Performance, and Investment teams) on project tasks and ad-hoc requests.
Improved data validation and accuracy using MS Excel, resulting in a 20% increase in data quality.
Enhanced data management and project success by 30% through the use of advanced SQL queries and data transformation techniques.
Collaborated with various departments to ensure data integrity and compliance with data standards.
Performed in-depth literature reviews and analyzed existing research to identify gaps and formulate new research questions.
Reviewed published articles and provided an overview of previous research.
Enhanced research efficiency by 40% by analyzing published articles, identifying key gaps, and informing studies that supported multi-disciplinary data-driven insights.
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