Forem: Nicole Onyango

IS EXEL STILL RELEVANT IN THE ERA OF POWER BI AND PYTHON

Nicole Onyango — Wed, 08 Oct 2025 16:56:28 +0000

Excel has been declared to be dying more times than can be counted, but there has to be something that has kept it surviving and relevant for all this time, even in the presence of tools like power bi and python, but I guess it is fair to ask Is excel still relevant?

My short answer, Yes.

INTRODUCTION

Excel is where most people begin to make sense of data, maybe the approachable simplicity? Where you start typing and instant results. We could say that the immediate feedback builds an intuition about how data behaves. This is where core logic lies behind all the data work: filtering, aggregating, and visualizing even in tools like Power BI.

Hidden power of excel

With features like power query, power pivot and DAX it could be considered a mini data engine. You can clean,model and analyze millions of rows of data, all without writing a single line of code. Like how you build a pivot table, like the data modelling power BI does behind the scenes. Like the old question of what came first? egg or chicken, here we could ask what birthed what, was power BI born out of Excel's "power" tools only difference being the presentation. Think of Excel as a full analytical platform hiding in plain sight.

AH Then Python

Python brings scalability, automation, and sophistication. You can build predictive models, automate cleaning, or analyze millions of rows in seconds. It’s a data powerhouse but not everyone wants to code just to find out their best-selling product.

That’s where Excel still shines. It’s immediate. You open it, drag a few fields, apply a filter, and the insight appears. No dependencies.

Sweet Spot

each tool has its place:

-Excel for flexibility, reporting, and hands-on exploration.
-Power BI for interactive dashboards and storytelling.
-Python for automation, advanced analytics, and data science.
The trick is know how to move between them. You might clean data in Excel, publish visuals in Power BI, and automate analysis with Python. Together, they form a complete ecosystem.

So is Excel still relevant?

Absolutely. Excel is a gateway to data literacy. It's where business users become analysts and analysts become data scientists. It is intuitive, immediate, and endlessly adaptable.
Power BI may tell the story beautifully.
Python may do the heavy lifting.
But Excel? Excel teaches you how to think with data.
And I think that is where the relevance lies.

DATA VALIDATION

Nicole Onyango — Mon, 22 Sep 2025 14:21:13 +0000

Data Validation: First Step to Trustworthy Insights 🧹📊

When people think about data science or analytics, the spotlight usually lands on machine learning models, fancy visualizations, or predictive dashboards.

But none of that works without good data.

And that’s where data validation comes in.

Think of it as quality control for your dataset saving you from hours of frustration down the line.

🎯 What Exactly Is Data Validation?

Data validation is the process of checking whether the data you’re working with is:

Accurate → free from typos or wrong entries
Consistent → in the same format across your dataset
Useful → follows the rules you’ve set for your project

In other words, it makes sure your data makes sense before you even think about analyzing it.

🔑 Everyday Examples

Form inputs → making sure emails contain an @ symbol
Surveys → limiting responses to “Yes/No” instead of “Yes/yeah/nah/ok”
Financial data → ensuring that amounts can’t be negative if they’re not supposed to be
Dates → preventing a “due date” that’s earlier than the “start date”

Simple checks like these can save you from massive headaches later.

🛠 How to Do Data Validation in a tool like

📄 Spreadsheets (Excel / Google Sheets)

Dropdown lists to standardize categories
Restricting numbers to specific ranges
Custom rules using formulas (e.g., forcing emails to contain @)

🐍 Python

With pandas or libraries like pandera, you can enforce rules programmatically

Add a dropdown in a spreadsheet
Write a quick Python check
Use a SQL constraint

CLEANING MESSY DATA: Why is it 80% of the job

Nicole Onyango — Wed, 17 Sep 2025 08:57:49 +0000

Cleaning Messy Data: Why It’s 80% of the Job let us Talk About it 🧹📊

When people think of data science, they imagine machine learning models, fancy dashboards, and mind-blowing insights.

But the real tea? Most of the time is spent cleaning messy, chaotic data before you even touch the fun stuff.

Why Data Cleaning Matters

Garbage in = garbage out.
Models can’t save bad data.
Clean data = faster insights.

Common Data Cleaning Struggles

Missing values that mysteriously disappear 👻
Duplicates that never seem to end
Columns with 20 different spellings of the same thing (looking at you, "Nairobi"/"nairobii"/"Nairobiii") see!!

My Go-To Tools

Python + Pandas: the classic combo
Excel: don’t sleep on it!
SQL: when datasets get big and messy

Takeaway

Data cleaning isn’t glamorous, but it’s the backbone of every project.

Think of it like doing dishes before cooking you can’t ignore it if you want a great meal.

💬 What’s the messiest dataset you’ve ever had to clean?