Highly motivated Data Analyst with a Master's in Applied Statistics and experience in data manipulation, statistical modeling, and predictive analytics. Proficient in SQL and Python programming with strong problem-solvin…
Highly motivated Data Analyst with a Master's in Applied Statistics and experience in data manipulation, statistical modeling, and predictive analytics. Proficient in SQL and Python programming with strong problem-solving skills. Proven ability to handle complex analyses and deliver impactful insights. Known for responsibility, ownership, and multitasking abilities. Excels in adaptability, collaboration, consulting, and innovation, driving actionable results. Ready to contribute to analytics-based projects.
Collaborated with Medical Researches to provide statistical analysis and insights for their research papers and Thesis.
Delivered comprehensive statistical support, including data analysis, hypothesis testing, and interpretation of results.
Trained and supervised a team of 15 interns, ensuring they acquired essential statistical skills and knowledge.
Streamlined statistical processes to improve accuracy and efficiency in data analysis.
Developed supervised machine learning models using Logistic Regression and Naive Bayes to predict startup outcomes.
Designed and deployed data visualizations using Power BI.
Used Python to clean and prepare data for various projects, ensuring data accuracy of over 91%.
Leveraged Python and SQL to analyze large datasets for forecasting.
Achieved a 15% improvement in forecasting accuracy.
Led a predictive analysis project using Logistic Regression and Naive Bayes algorithms to determine the economics future status of startups, achieving a 90% prediction accuracy.
Applied advanced data preprocessing techniques, including feature engineering and missing value handling.
Integrated machine learning outcomes into tools for strategic decision-making, enhancing investor and policymaker insights.
Utilized Python and libraries like Pandas, Matplotlib, and Scikit-Learn for data analysis and model development.
Employed KNN and Logistic Regression models achieving an impressive 86% accuracy as the best-fit model.
Engineered and deployed advanced risk analytics and machine learning models in banking, specializing in predicting client default rates, resulting in a 20% decrease in non-performing loans.
Applied PCA, classification, and regression modeling.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
د.جوب منطقة حرة ذ.م.م. 2026 © جميع الحقوق محفوظة
