Over the past year, I've noticed most businesses don't struggle because they lack data โ they struggle because that data never turns into a decision.
That's the gap I work in:
โ Cleaning and analyze data with Python & R
โ Turning findings into investor-ready pitch decks
โ Polishing the final write-up so nothing gets lost in translation
If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land โ that's exactly where I can help.
Behind every clean chart is a messy spreadsheet ๐งน
This is what a real data cleaning job looks like before it becomes an elegant bar chart โ duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
FadeFlow: From โCan I book Saturday?โ to โYouโre booked.โ
I built FadeFlow for Fade District, a fictional solo barbershop in Yaba, Lagos, run by Malik Adeyemi.
The problem is simple: a lot of bookings start with a message like:
โCan I get a haircut Saturday afternoon?โ
From there, Malik has to ask for the service, check availability, reply with time options, create the booking, confirm it, collect the deposit, send reminders, and handle changes.
FadeFlow removes that back-and-forth.
Customers can choose a service, see available times, pay a โฆ5,000 deposit, and confirm their appointment themselves.
And when they still message instead, FadeFlow turns the inquiry into a structured booking request. Malik can see what they want, send available times, and create the booking in one action.
Once it's booked, FadeFlow keeps everything connected: