Malik Hassan A.

Motivated, self-driven, and detail-oriented Data Scientist and Data Analyst with hands-on experience transforming messy, raw, and unstructured data—including Excel spreadsheets and large datasets—into meaningful insights that support better decision-making. Skilled in Python, Pandas, NumPy, Scikit-learn, Microsoft Excel, Google Sheets, Power BI, and data visualization. Experienced in the complete data analytics lifecycle, including data collection, web/internet research, data cleaning, data preprocessing, exploratory data analysis (EDA), statistical analysis, predictive modeling, forecasting, and interactive data visualization. Proficient in developing analytical models, automated data workflows, complex Excel spreadsheets, Pivot Tables, Power Pivot models, dashboards, and interactive visualizations. I combine strong analytical and research skills with practical business understanding to identify trends, uncover patterns, measure KPIs, and translate complex datasets into clear, actionable insights. Areas of Expertise * Data Analysis & EDA: Data cleaning, preprocessing, exploratory analysis, trend analysis, pattern identification, and actionable insights. * Python for Data Science: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and Plotly. * Machine Learning: Predictive modeling, classification, regression, model evaluation, and forecasting. * Business & KPI Analysis: KPI development, performance analysis, business intelligence, and data-driven decision support. * Data Visualization: Interactive dashboards, charts, reports, and visual analytics using Power BI, Plotly, Matplotlib, and Excel. * Microsoft Excel: Advanced formulas, Pivot Tables, Power Pivot, Pivot Charts, complex spreadsheets, data generation, analysis, and reporting. * Google Sheets: Data management, analysis, reporting, and collaborative spreadsheet solutions. * Data Modeling: Data transformation, relationships, Power Pivot models, calculated measures, and analytical reporting. * Statistical Analysis: Descriptive statistics, trend analysis, forecasting, and interpretation of analytical results. * Research & Analytics: Scientific research, literature reviews, research papers, systematic analysis, and meta-analysis. * Data Collection & Internet Research: Online research, structured data collection, data validation, and preparation of datasets for analysis.