Navigating Scope Creep in Data Projects
I just completed my first BI Sprint Series project, and one of the things that stood out was how scope […]
Navigating Scope Creep in Data Projects Read More »
I just completed my first BI Sprint Series project, and one of the things that stood out was how scope […]
Navigating Scope Creep in Data Projects Read More »
In earlier stages of this project, I defined StaffWise’s revenue leakage problem and outlined key metrics for analysis. The previous
Report: Investigating Churn at Staffwise Read More »
This phase of the project focused on executing the actual analysis. While I had already done significant prep work in
Documenting the Analysis Process Read More »
As part of my Subscription Churn project for StaffWise, I decided to use simulated data to practice creating a clean
The Location Table That Had Nowhere to Go Read More »
If you’re on the journey to becoming a data analyst (or already in the field), you’ve probably heard this a
Why Is It So Hard to Make SQL Shine in a Portfolio? Read More »
Clean data isn’t just about tidy spreadsheets; it’s what ensures that insights are reliable and decisions are well-informed. This stage
Data Cleaning Progress Update Read More »
You’ve probably heard it before – maybe from mentors, online forums, or blogs: “You don’t need to spend a cent
Free vs Paid Tools: How Far Can You Really Go When Building Your Portfolio? Read More »
I recently set out to create a mock dataset for a BI Sprint Series project I’m working on. I figured
Top 6 Tips for Simulating Realistic Data with ChatGPT and Python Read More »
I asked Python to create random cities, states, and countries – and it did, separately. Now I’ve got Nairobi in
What Happened to My Location Table? Read More »
One of the biggest hurdles in building a business intelligence (BI) pipeline is… well, getting the data. But what happens
Simulating Realistic Subscription Data for Business Intelligence Read More »