Hi, I’m Amirul 👋

I’m an automation-focused data professional who enjoys building end-to-end data workflows from extraction and cleaning to validation and delivery. My experience spans consumer insights, supply chain, market research, and education operations.

I got serious about data during my research internship in Chiang Mai, Thailand, where I worked on optimising honey storage conditions for local beekeepers. The data didn’t give easy answers at first, and that experience taught me the value of experimentation, debugging, and persistence. It also sparked my interest in building reliable, reusable data workflows and improving data quality

Here you will find my projects in Python, SQL and dashboarding (Tableau/Power BI).

• Highlighted Projects

  • Organisation Data Processor
  • University Allocation Based on WEF Report (2025)
  • Mochi Ice-Cream Sensory Analysis

• More Projects

  • Python
    • TPA Calculator
    • Honey Storage Optimisation
    • Boba Pearl Sensory Science
  • SQL
    • Olist E-commerce Data Exploration
    • Cafe Promotions Data Cleaning
    • Mochi Sensory Database Setup
  • Visualisation (Tableau & Power BI)
    • HR Attrition
    • Superstore Data
    • Olist E-commerce Sales Data

• Education

• Certifications

Repository: Data Processor App

Objective: Automate annual cleaning + transformation workflows for non-technical users

Tech: tkinter, pandas, regex, pathlib

Outcome: Reduced processing time from 5 hours → 30 minutes

Repository: University Allocation Optimisation

Objective: Allocate students to majors using realistic demand curves + constraints

Description: Designed an Integer Linear Programming (ILP) model using PuLP, factoring in costs, major demand, and budget constraints. Simulated multiple demand curve options (exponential, sigmoid, power law) to model student preferences

Tech: PuLP, matplotlib, numpy, pandas

Outcome: Produced interpretable allocation plans useful for education planning

Repository: Mochi Ice cream

Objective: Build an analysis-ready pipeline to identify best-performing Mochi formulation

Methods: Principal Component Analysis (PCA), K-Means Clustering, Data Visualization, Descriptive Statistics

Tech: PostgreSQL, Scikit-learn, Pandas, Plotly, dbt, SQLAlchemy

Outcome: Clean, normalised dataset powering PCA + clustering analytics

• TPA Calculator

• Honey Storage Optimisation

• Boba Pearl Sensory Science

• Olist E-commerce Data Exploration

• Cafe Promotions Data Cleaning

• Mochi Sensory Database Setup

• HR Attrition

• Superstore Data

• Olist E-commerce Sales Data

Bachelor (Tech) of Food Technology with Honours
Singapore Institute of Technology (SIT)

Relevant Modules: Applied Data Science, Design of Experiments (DOE), Sensory Science, Consumer Insights, Python Programming, Multivariate and Descriptive Statistics, Optimisation Modelling (ILP/LP/RSM/Centroid Design)

Microsoft Excel Professional.pdf

Tableau Intelligence Analyst.pdf

Google Business Intelligence.pdf

Google Cloud Analytics.pdf

Google Advanced Data Analytics.pdf