Day 14 ๐
Todayโs focus was analytical thinking.
Big realization:
Data analysis isnโt about charts first.
Itโs about asking the right questions before touching the data.
Anyone can build a dashboard.
Not everyone can extract useful insights.
#DataAnalytics#Python
Been quiet on here for a bit.
Not because I stopped learning.
Just been busy turning tutorials into projects and bugs into life lessons ๐ญ๐
What's everyone building these days?
The more I learn, the less interested I become in building things that only work when everything goes right.
What interests me now is building systems that keep working when things go wrong.
That's where the real engineering begins.
#SystemsThinking#Technology
Day 20 of #30DaysOfPython ๐
Built a Nigerian Disease Outbreak Analyzer with Pandas.
A few findings:
๐ด Rivers State recorded the most cases (591)
๐ฆ Malaria was the most common disease (944 cases)
โ ๏ธ Cholera had the highest death rate (10%)
Been a little quiet lately ๐
Tests happened.
The 30 Days of Python challenge didn't stop, though. I just wasn't posting as much.
Still learning.
Still building.
Still debugging.
Back to sharing the journey ๐๐
Day 18 of my #30daysofPython
Day 17 Python project just got an upgrade.
Turned my expense tracker CLI into a web app using Streamlit.
Add expenses. View them. See a chart. All in the browser.
From terminal to web app in one night. ๐ฅ
It just keeps getting better.
Day 17 of #30DaysOfPython๐
Just finished building an Expense Tracker from scratch.
โ Add expenses
โ View records
โ Monthly summaries
โ Budget alerts
Built it.
Broke it.
Debugged it.
Fixed it.
Starting to realize that programming isn't about getting it right the first timeโit's
Day 17 of #30DaysOfPython
Building an expense tracker from scratch tonight.
Deleted the CSV file 3 times because I kept writing headers as data ๐ญ
Debugged for 30 mins. Fixed in 2 lines.
The struggle is real but the function works. Progress. ๐ง
Honest confession: today was not my most consistent day.
Test, AWS studying, and building a Python project all fighting for the same hours.
Tomorrow I make up for it. ๐ง
Is it just me or are most hackathons built for frontend and backend devs?
Where are the hackathons specifically for data analysts, data scientists and ML engineers?
If you know any kindly drop them below
The job market is a joke right now. You'll have a portfolio, a certificate, and a well Curated CV. But apparently, you lose to the guy who knows a guy who knows the hiring manager.
nepotism is undefeated.
SQL question that trips most beginners:
You have a table of transactions. You want only customers who spent more than โฆ50,000 in total.
Do you use WHERE or HAVING โ and why?
Think before you answer ๐
Day 16 Python update:
Expense tracker isn't done.
Spent 30 mins debugging duplicate headers from a bug I created myself ๐ญ
This is what building actually looks like.
Back at it tomorrow ๐ง
What's the most useful thing you've ever built with Python?
Doesn't have to be impressive. Just useful.
Mine today: an expense tracker that immediately called me out ๐
Data analysts in Nigeria, genuine question:
Do you work with any cloud tools (AWS, Azure, GCP) in your current role or projects?
Or is it mostly local Excel/Tableau/Power BI setups?
Trying to understand where the gap actually is.