Ikharona Isaiah | Data Scientist

Data Scientist || I analyze and turn raw data into clear, actionable insights.
Python | SQL | Power BI || Telling data stories that drive decisions.

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@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.

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@ThaboB443579 @Jennnyyyyyy Do you mean square root or square?

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@Jennnyyyyyy This is easy. The answer is 4.

Here's how

12/4 = 3
3 x 3 = 9

24/3 = 8
8 x 8 = 64

8/4 = 2
2 x 2 = 4.

The answer is 4.

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@renabaddie_ 45.

3 + (7 x 1) = 10
10 + (7 x 2) = 24
24 + (7 x 3) = 45

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@Gosleepriya This is easy.

338.

7 x 7 = 49
49 + 49 = 98

11 x 11 = 121
121 + 121 = 242

so,

13 x 13 = 169
169 + 169 = 338

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@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

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@Dussyme I'll say the answer to this is + or - 10.

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@Jennnyyyyyy The answer is 97.

9 x 7 = 63
9 - 7 = 2

So, 97.

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

@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.

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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. 🛠️📊

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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.

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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🧵

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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! 📩

[Ikharona Isaiah | Data Scientist @dWeb3Translator]

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📈