Mostafizur RahmanAbout
The work starts before the dashboard.
Good analysis begins with the right question, reliable data, and a clear method. That thinking guides how I approach analytics, machine learning, and reporting.
I work across data analytics, business intelligence and machine learning. My background in Physics gave me the mathematical foundation; my work in data quality for AI/ML datasets taught me to distrust data until it has been tested; and business development showed me how decisions are actually made.
The result is a way of working I apply to every project: define the decision, validate the data, choose the simplest method that answers the question, and report the result with its limits.
Journey
The route so far
2018–2022
A quantitative foundation
A BSc in Physics developed my grounding in mathematics, statistics, and computational methods, along with a habit of questioning results before accepting them.
2022–2024
Building skills in analytics and data science
IBM Data Analyst and Data Science certificates, plus Google Business Intelligence and Power BI courses, turned that base into applied Excel, SQL, Python and BI skills.
Feb–Apr 2024
Applying analytical skills
Worked with real-world data to identify patterns, investigate findings, and present insights through clear reports and dashboards.
Aug 2024–Aug 2025
Data quality for AI/ML
Prepared and then validated datasets across image, LiDAR and sensor data. This is where accuracy and documentation became routine.
Sep 2025–Present
Business development
Market research, CRM, lead generation and proposals. It showed me what decision-makers actually ask of data, and how to explain findings plainly.
Principles
How I work
Check the data first
I profile and validate before analysing. Missing values, duplicates and odd records are documented, and every exclusion is explained.
Start from the decision
Each project begins with the business question and the decision it informs, then the metric and the chart.
Define every number
I make metrics understandable and traceable, with clear definitions, formulas, and filters where they matter.
State the limits
I explain what the results show, what they don't, and where limitations in the data or method affect the conclusions.
Direction
Where I'm heading
I'm pursuing Data Analyst, BI Analyst, and Data Scientist roles, alongside selected freelance projects. I'm continuing to strengthen my SQL skills, deepen my understanding of model evaluation, and build further expertise in deep learning and computer vision.