BSc Computer Science graduate focused on Data Analytics and Business Intelligence.

I work with data to uncover trends, measure business performance, and turn raw datasets into actionable insights using Python, SQL, Excel, and Power BI.

🔗 Portfolio: https://lena-basheer.github.io/portfolio/

• Python
• SQL
• Pandas
• NumPy
• Matplotlib

• Data Cleaning & Transformation
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Customer Segmentation
• Business Analysis
• KPI Development
• Data Storytelling

• Power BI

  • DAX
  • Power Query
  • Interactive Dashboards
  • KPI Visualizations

• Microsoft Excel

  • Pivot Tables & Pivot Charts
  • VLOOKUP/XLOOKUP
  • Conditional Formatting
  • Dashboard Development

• SQLite
• Git & GitHub
• Jupyter Notebook
• VS Code

Remote | July 2026 – Present

• Cleaned, validated, and transformed 51,290 sales records from the Global Superstore dataset using Microsoft Excel.
• Built interactive sales performance dashboards using Pivot Tables, Pivot Charts, KPI cards, and slicers.
• Analyzed revenue, customer segments, category performance, and yearly sales trends to support business reporting.

Remote | July 2026

• Researched and structured NGO datasets using Microsoft Excel to support organizational planning and growth strategies.
• Investigated applications of AI in outreach, fundraising, volunteer engagement, and digital presence.
• Translated research findings into structured, decision-ready reports.

Python • Pandas • NumPy • Matplotlib • Jupyter Notebook

Analyzed 500,000+ online retail transactions to understand customer purchasing behavior, revenue drivers, and retention opportunities.

Key Work

• Cleaned and transformed large-scale retail transaction data.
• Performed exploratory data analysis to identify sales and customer trends.
• Engineered customer-level features for segmentation.
• Segmented 4,000+ customers into VIP, Loyal, Regular, and New segments.
• Identified top-selling products, high-value customers, monthly sales trends, and country-level revenue patterns.

🔗 Repository: https://github.com/lena-basheer/Customer-Segmentation-Sales-Analysis

SQL • SQLite • Python • Pandas • Matplotlib

End-to-end business analytics project combining SQL analysis with Python-based reporting and visualization.

Key Work

• Analyzed approximately 10,000 retail records.
• Used SQL joins, aggregations, and business-focused queries.
• Evaluated customer, product, category, and regional performance.
• Identified Technology as the highest revenue-generating category.
• Identified the West region as the strongest-performing region.
• Automated analysis reports and visualizations using Python.

🔗 Repository: https://github.com/lena-basheer/sql-python-business-analytics

Python • Pandas • Data Analysis

Sales analytics project developed as part of the BeeSkilled Data Analyst Internship.

Key Work

• Cleaned and analyzed sales transaction data.
• Investigated revenue, customer segments, product categories, and yearly sales performance.
• Developed analysis outputs to support sales performance reporting.
• Applied data-cleaning and analytical techniques to a real-world business dataset.

🔗 Repository: https://github.com/lena-basheer/Sales-Performance-Analysis

Power BI

Interactive workforce analytics dashboard designed to analyze employee distribution and workforce trends.

Key Work

• Developed KPI-based workforce visualizations.
• Created interactive filters and slicers.
• Analyzed workforce distribution across departments.
• Presented workforce metrics through an interactive Power BI dashboard.

• 🏅 IBM — Data Analysis with Python
• 🏅 NPTEL — Python for Data Science
• 🏅 HP — Data Science & Analytics Job Simulation
• 🏅 Forage (Tata Group) — GenAI Data Analytics Simulation

• Advanced SQL
• Power BI DAX & Data Modeling
• Statistics for Data Analysis
• Data Storytelling
• Business Intelligence Techniques

📧 Email: lenabasheer.in@gmail.com

🔗 LinkedIn: https://linkedin.com/in/lenabasheer

🔗 GitHub: https://github.com/lena-basheer

🔗 Portfolio: https://lena-basheer.github.io/portfolio/

⭐ I build practical analytics projects that transform data into meaningful business insights using Python, SQL, Excel, and Power BI.