Making data science practical for business
clear, applied, and built for real-world impact.
Hi, I’m Alex — welcome to AlexDoesData.
I’m a former business student who discovered a passion for data science through internships and real-world experience. Today, I’m pursuing my MSc in Business Analytics & Artificial Intelligence at the University of Warwick, where I continue to explore how data can create real impact in business.
I believe that for data science to truly matter, it has to be practical and connected to business operations — not just technical or overly academic. Too many resources focus on code or theory alone, making it hard for newcomers to see the bigger picture.
Here, I share what I’ve learned (and continue to learn) — from beginner-friendly insights and hands-on projects to the latest techniques I encounter in my MSc. My goal is to make data science understandable, approachable, and directly relevant for anyone who wants to see how analytics can drive business value.
Projects at the Intersection of Data and Business
A selection of projects where I apply data science techniques to real business challenges — blending technical methods with practical insights.
Predictive Analytics in Demand Forecasting: Improving Accuracy in Retail Supply Chains
Analyzed how predictive analytics improves supply chain operations for retail businesses.
Employee Retention Analysis — Salifort Motors
Predicting employee turnover at Salifort Motors using data-driven insights from the Google Advanced Data Analytics Capstone.
Superstore Dashboard: Profitability and Efficiency Insights
An interactive Power BI dashboard analyzing profitability, shipment efficiency, and customer segment performance.
Learning, Building, Sharing
Sharing what I learn as I explore the world of data science and business analytics — in a practical, approachable way.
How Business Professionals Can Ask the Right Data Questions
A guide for business professionals on asking smarter data questions — from clarifying problems to challenging data quality and statistics.
The Framework That Moves Every Data Project Forward
Discover the CRISP-DM framework — the six-stage process that guides every successful data project from business understanding to real-world impact.
Uncovering Hypothesis Tests and Confidence Intervals
A clear breakdown of hypothesis testing and confidence intervals — how they help data professionals make reliable, evidence-based decisions from limited data.
Interested in exploring more or working together?
I’m always sharing new projects and insights on how data science creates business value. Explore my portfolio or connect with me directly.
