Final year Statistics student passionate about transforming data into actionable insights. Specializing in statistical analysis, data visualization, and predictive modeling.
Get In TouchHello! I'm Md. Hasib, a final year Statistics student with a passion for data analysis and interpretation. I'm dedicated to uncovering meaningful insights from complex datasets to drive informed decision-making.
My academic journey has equipped me with a strong foundation in statistical theories, probability, and quantitative methods. I've developed expertise in applying statistical techniques to solve real-world problems and communicate findings effectively.
As an aspiring Data Analyst, I'm continuously expanding my skill set through hands-on projects and staying current with industry trends. I'm proficient in various data analysis tools and programming languages, with a particular interest in data visualization and predictive modeling.
I'm seeking opportunities to apply my analytical skills in a professional setting where I can contribute to data-driven solutions and continue growing as an analyst.
Contact MeConducted comprehensive analysis of retail sales data to uncover trends, customer purchasing patterns, and product performance. Used Python for data cleaning, exploratory data analysis, and visualization with Matplotlib and Seaborn, Power BI for creating an interactive dashboard and MS Word for writing a report.
View ProjectConducted a statistical study on stroke risk prediction using SPSS. Performed data cleaning, categorical recoding, and regression modeling to identify significant risk factors influencing stroke occurrence.
View ProjectDeveloped an interactive sales dashboard for Adidas US operations using Power BI. Analyzed sales performance across regions, product categories, and retail partners with dynamic visualizations and key performance indicators.
View DashboardDeveloped a predictive model to identify heart disease risk using patient clinical data. Implemented multiple ML algorithms including Logistic Regression, SVM, and Random Forest. Achieved 85% accuracy and identified key risk factors like cholesterol levels and blood pressure.
View Project Prediction AppDeveloped multiple regression models to predict medical insurance charges using Python's statsmodels. Performed comprehensive EDA, feature engineering, and statistical testing to identify significant predictors like age, BMI, and smoking status.
View AnalysisDeveloped an interactive Excel dashboard to explore coffee shop sales trends, KPIs, and performance. This project demonstrates dashboarding, data visualization, and storytelling in Excel.
View ProjectConducted comprehensive statistical analysis of Pima Indians diabetes dataset using SPSS. Performed descriptive statistics, correlation analysis, and developed logistic regression models to identify significant risk factors for diabetes.
View AnalysisThis Power BI project visualizes sales performance data from 2023 to 2025, providing key business insights such as total sales, product performance, and regional trends.
View Dashboard+880 01725599937
Chittagong, Bangladesh