Md Shah Nawaj | Data Scientist

About Me

Md Shah Nawaj is a Data Scientist with a strong background in software engineering and AI research. I specialize in creating intelligent systems and data-driven solutions using tools like Python, R, SQL, TensorFlow, and Power BI.

With academic foundations from Aston University and East West University, I blend analytical thinking with creativity to solve real-world problems and deliver outstanding results.

When I’m not analyzing data, I enjoy exploring new tech, contributing to open-source, and reading books on emerging innovations.

Resume

Education

MSc Data Science, Aston University
BSc CSE, East West University

Experience

InsightEdge Analytics Intern
Python, Power BI, SQL

Location

Birmingham, United Kingdom

Languages

English, Bengali

My Projects

Customer Behavior Analysis Using RFM Segmentation in SQL

This project leverages SQL to uncover customer behavior patterns using RFM (Recency, Frequency, Monetary) segmentation. By analyzing transactional data, it reveals high-value customer groups and supports data-driven marketing strategies through targeted insights.

Live Demo Source Code

Adventure Works Bike Sales Dashboard – Power BI

Built a complete Power BI dashboard using the Adventure Works Bike Sales dataset, focusing on bike and accessories sales performance. Conducted data cleaning and transformation in Power Query, then developed four interactive dashboards to visualize key metrics across sales, customers, and product categories. Tools Used: Microsoft Power BI, Power Query.

Live Demo Source Code

Retail Data Analysis with Excel

An excel dashboard project analyzing customer behavior and sales performance using the UCI Online Retail II dataset. This project uncovers key insights into customer segmentation, churn, order behavior, and regional sales distribution.

Live Demo Source Code

Earthquake Anomaly Detection

Unsupervised learning on seismic time series data to identify anomalies that could indicate earthquakes.

Live Demo Source Code

Type 2 Diabetes Risk Prediction

Classification model using health data to predict the likelihood of Type 2 diabetes with ROC-AUC evaluation.

Live Demo Source Code

Football World Cup 2022 Website

Collaborative full-stack project displaying live World Cup data with a responsive design and real-time updates.

Live Demo Source Code

Award Winning Moments

Receiving the Best Project Award for our Poster Presentation at East West University.

Honored for excellence in project design — CSE347 Poster Presentation (Fall 2022).

Publication

Enhancing Early Diagnosis and Precise Classification of Type 2 Diabetes

Published in 7ICOIACT, 2024
This research explores the integration of machine learning and image processing for the early detection and classification of Type 2 Diabetes through retinal imagery. The study combines structured clinical data with visual indicators to increase prediction precision, significantly improving diagnostic capabilities in healthcare systems.

This work was presented at the 7th International Conference on Information and Communications Technology (7ICOIACT) in Japan and is now officially archived on IEEE Xplore, contributing to ongoing advancements in intelligent healthcare solutions.

View on IEEE Xplore →