Analytical and detail-oriented Data Analyst with a strong foundation in data visualization, statistical analysis, and business intelligence. Skilled in transforming raw data into actionable insights that support data-driven decisions. Experienced in using Python (Pandas, NumPy, Seaborn, Scikit-learn), SQL, Power BI, Tableau, and Excel to analyze large datasets, forecast trends, and build interactive dashboards. Demonstrated ability to manage end-to-end analytics projects, including data cleaning, exploratory data analysis (EDA), forecasting, and performance reporting.
Recently completed an interactive project analyzing the impact of COVID-19 on global trade, featuring ARIMA forecasting, clustering of recovery trends, and country-level comparative dashboards. Passionate about uncovering insights that improve efficiency and business outcomes through data storytelling and evidence-based recommendations.
Proficient
Project
→
Summary
Customer Purchase Pattern Analysis – Use SQL or Python to find frequent itemsets (Market Basket Analysis). • Sales Forecasting Model – Predict future sales using time series models (ARIMA, LSTM). • Product Price Optimization – Analyze price elasticity using regression and visualization. • A/B Testing for Marketing Campaigns – Evaluate the effectiveness of campaigns with statistical tests.
Pandas, NumPy, Exploratory Data Analysis (EDA).
Joins, Subqueries, Aggregations, Data Cleaning.
Matplotlib, Seaborn, Plotly, Excel Charts.
Streamlit, Dash, ipywidgets, Power BI.
ARIMA, Time Series Analysis.
KMeans, Normalization, Feature Engineering.
Jupyter Notebook, VS Code, Git, GitHub.
Problem-solving, Communication, Attention to Detail.