Most people see a mess of numbers. I see a roadmap to better decisions📊
Hi, I’m Isaiah. I’m a Data Scientist dedicated to turning raw, chaotic data into clear, actionable insights that solve real-world problems.
My Toolkit:🐍Python|🗄️SQL|📊Power BI|📈Excel
👇See more
@Jennnyyyyyy This is straightforward. The answer is 19.
Here's how
The cone is 5
5 + 5 + 5 = 15
The square is 9
9 + 5 + 9 = 23
The pentagon is 2
2 + 2 + 9 = 13
Now, the final expression is
9 + 5 x 2
From BODMAS, we do multiplication first
9 + (5 x 2)
9 + 10 = 19.
@Dussyme If you solve the quadratic equation, you'll see that when a is 20, b is 10, and when a is 10, b is 20.
That means, the last expression will be
20 - 10 = 10
or
10 - 20 = -10
So, this gives + or - 10
@Jennnyyyyyy 35.
This is how I arrived at my answer;
The first fig
(4x6) + 6
24 + 6 = 30.
Let's apply this same logic to the second figure and see if it'll equal 27
(2x9) + 9
18 + 9 = 27
Since it worked, it means the last one is
(6x5) + 5
30 + 5 = 35
Final answer, 35.
Infrastructure vandalism is a national challenge, but data-driven strategies can mitigate the fallout.
What’s the bigger threat to our tech ecosystem: The vandalism itself, or the lack of predictive management?
Let’s discuss. 👇
#TelcoData#NigeriaTech#DataScience#DataAnalytics
656 power assets, ₦2.3 Billion lost🔋📉
That’s the cost of infrastructure theft reported by Nigerian telcos in just one year
I’m looking at this from a Data Science lens @MTNNG@AirtelNigeria@GloWorld, this isn’t just theft; it's a data problem🧵👇
Image source: @Nairametrics
We can reduce this occurrence with predictive modeling.
By layering incident data with site usage, we can:
✅ Identify "Hotspot" towers before they’re hit.
✅ Optimize maintenance routes.
✅ Forecast outages to manage customer expectations.
Data actually is the best defense. 🛠️📊
Thank you so much, @TechSphereAcad, for teaching me Data Science.
I love who I have become💪💪
Excited to start sharing more data projects and insights here.
I analyzed telecom customer churn using a dataset modeled after real-world behavior in Nigeria. The results challenged a common assumption
People think customers leave because they're unhappy
But the data said something else: Even satisfied customers were churning
The breakdown🧵
I’m Isaiah, and I turn raw data into clear, actionable roadmaps for business growth.
Full project & code here: 📂 https://t.co/8EWh6mdN5z
Looking for my next challenge in Data Science. DMs are open! 📩
Note: This analysis was performed using a high-quality synthetic dataset designed to mirror real-world Nigerian telecom patterns.
While the data is simulated, the methodology and insights are 100% focused on solving the actual business challenges faced by local telcos📈