DEV Community: Rehana Hassan Muhumed

Day 3 — Moving to Multiple Linear Regression

Rehana Hassan Muhumed — Wed, 06 May 2026 13:58:17 +0000

Today I continued my Machine Learning journey and learned about Multiple Linear Regression.

After understanding linear regression with a single input, this concept made more sense because it extends the same idea to multiple features.

📌 What is Multiple Linear Regression?

Multiple Linear Regression is used to predict a numerical value using more than one input.

For example, predicting the price of a house depends on several factors such as:

Size
Number of rooms
Location

Instead of relying on one feature, the model combines all of them to make a more accurate prediction.

🧠 How it Works

Each input has a weight that represents its importance. The model adjusts these weights to minimize prediction error.

This means the model learns how much each factor contributes to the final result.

💡 Key Insight

This concept shows how machine learning models handle real-world problems where multiple factors influence outcomes.

🚀 Reflection

Today’s lesson felt more practical and closer to real-world applications. It helped me see how machine learning can be used in more complex scenarios.

✨ Step by step, I’m building a strong foundation in Machine Learning.

MachineLearning #AI #LearningJourney

Day 2 — Understanding Cost Function & Gradient Descent

Rehana Hassan Muhumed — Tue, 05 May 2026 08:10:16 +0000

Today I continued my Machine Learning journey and explored two very important concepts: the cost function and gradient descent.

At first, these topics felt a bit confusing, but breaking them down into simple ideas helped me understand them better.

📌 Cost Function

The cost function is used to measure how accurate or inaccurate a model’s predictions are.

A high cost means the model is making large errors
A low cost means the model is performing well

The main goal in machine learning is to reduce this cost as much as possible to improve the model’s performance.

📉 Gradient Descent

Gradient descent is an algorithm used to minimize the cost function.

It works by:

Starting with initial values for the model parameters
Adjusting them step by step
Moving in the direction that reduces the error

I like to think of it as trying to reach the bottom of a valley, where the lowest point represents the best possible model.

⚡ Learning Rate

Another key concept I learned is the learning rate, which controls how big each step is during gradient descent.

A small learning rate makes learning slow but stable
A large learning rate makes learning faster but can be unstable

Choosing the right learning rate is important for finding the optimal solution efficiently.

🧠 Reflection

This topic was challenging, especially understanding how the model updates its parameters. However, visualizing the process as moving down a curve helped me understand the overall idea.

I’m still getting comfortable with these concepts, but I’m starting to see how models improve step by step.

🚀 Learning Machine Learning one step at a time.

MachineLearning #AI #LearningJourney

Day 1 — Starting My Machine Learning Journey

Rehana Hassan Muhumed — Mon, 04 May 2026 07:57:31 +0000

Today I officially started my journey into machine learning through the Machine Learning Specialization by DeepLearning.AI.

At the beginning, I learned the basic idea of machine learning, which is teaching computers to learn from data and make predictions without being explicitly programmed.

I also explored supervised learning, where models learn from labeled data. There are two main types:

Regression: used to predict numbers (like house prices)
Classification: used to predict categories (like spam or not spam)

Then I was introduced to linear regression, which is a method used to predict numerical values by fitting a straight line to the data. Although it was a bit confusing at first, I started to understand that the model tries to find the best line that represents the relationship between input and output.

This is just the beginning, and I’m excited to continue learning step by step.

MachineLearning #AI #LearningJourney

Google Just Changed Looker Studio Back to Data Studio — Here's What That Means for Beginner Data Analysts

Rehana Hassan Muhumed — Mon, 27 Apr 2026 11:49:26 +0000

This post is my submission for the Google Cloud NEXT '26 Writing Challenge.

📺 The Announcement That Caught My Eye
I've been watching the Google Cloud NEXT '26 announcements, and one update stood out to me as a beginner data analyst:

Looker Studio is now called Data Studio again.

Wait — didn't Google change this name before? Yes! And now they're changing it back.

But here's why I actually care about this: It's not just a name change.

🔍 What Actually Changed?
According to the announcement, the new Data Studio comes with:

Feature What It Does
AI-powered insights Automatically finds patterns in your data
Smarter charts Recommends the best chart type for your data
Faster refresh Real-time data updates
Better sharing Easier collaboration with teams
The name went back to Data Studio. But the product got real upgrades.

💭 My Honest First Reaction
What I love:

I learned data visualization in my IBM Data Analyst course. We used Excel and Cognos Analytics for dashboards.

When I heard about Data Studio's AI-powered insights, I got excited. As a beginner, I spend hours figuring out which chart to use. If AI can recommend the best one? That saves me so much time.

What confuses me:

Why change the name again?

First it was Data Studio. Then Looker Studio. Now back to Data Studio.

For someone new to Google Cloud tools, this is a little confusing. But honestly? As long as the tool works well, I don't really care what it's called.

📊 Who Should Care About This?
You should care if... Why
You make dashboards Better AI features = faster work
You're learning data viz Smarter recommendations help you learn
You use Google tools Tighter integration with BigQuery & Sheets
You're a beginner Easier to get started
✅ My Takeaway
The name change back to Data Studio made me laugh at first. But after reading what's actually new — AI insights, smarter charts, faster refresh — I'm genuinely interested.

Will I try it? Yes.

Will the name confuse me? Maybe a little.

Do I recommend other beginners try it? Also yes.

Sometimes the best updates come with the funniest names. And hey, at least Data Studio sounds friendlier than Looker Studio.

🔗 What I Used to Write This
Google Cloud NEXT '26 announcements page

Data Studio / Looker Studio official docs

My own experience learning data visualization in the IBM Data Analyst Certificate

🙏 Final Thoughts
The challenge prompt asked for "genuine perspective" — so here it is:

The Data Studio name change is a little silly. But the actual product updates? Pretty solid.

If you're a beginner data analyst like me, give the new Data Studio a try. The AI features might surprise you.

Thanks for reading! And good luck to everyone in the Google Cloud NEXT '26 Writing Challenge. 🍀

googlecloud #datastudio #dataanalytics #datavisualization #cloudnextchallenge #beginners

I completed the IBM Data Analyst Certificate 🎓

Rehana Hassan Muhumed — Mon, 27 Apr 2026 11:26:02 +0000

Alhamdulillah! 🎉

I just completed the IBM Data Analyst Professional Certificate on Coursera!

Here's what I learned in simple words:

📚 What I Studied (11 Courses)

Excel basics & dashboards
Python for data science
SQL & databases
Data analysis with Python (Pandas, NumPy)
Data visualization (Matplotlib, Seaborn, Cognos)
Generative AI for analytics
Capstone Project (real-world data)
Career guide & interview prep

🛠️ Tools I Can Now Use

Tool	What I Can Do
Excel	Pivot tables, dashboards
SQL	Query databases
Python	Clean & analyze data
Pandas/NumPy	Data manipulation
Matplotlib/Seaborn	Create charts
Cognos Analytics	Build dashboards

💡 What I Learned

Real-world data is messy — cleaning it takes time
Python + SQL together = super useful
Charts should tell a story
A good portfolio matters most
AI helps speed up analytics work

🎯 My Capstone Project

I took a real dataset, cleaned it with Python, made visualizations, built a dashboard, and presented my findings. It's now in my portfolio.

🔗 My Certificate

Verify here:

https://coursera.org/share/b23bc821868ef9508c777f489bbdd6d9

Certificate ID: 1J0WD04M66UB

Completed: April 27, 2026

📢 My Advice

Just start. One course at a time. You don't need a degree — just consistency.

Alhamdulillah for everything 🤍

💬 Let's Connect

I'm looking for entry-level Data Analyst roles. Feel free to reach out!

LinkedIn:https://www.linkedin.com/in/rehana-hassan/
GitHub:https://github.com/rihhanna

dataanalytics #ibm #coursera #python #sql #alhamdulillah

Hi 👋 I'm Rehana - A Software Engineer & Data Analyst Who Enjoys Debugging

Rehana Hassan Muhumed — Sun, 26 Apr 2026 16:54:49 +0000

Hi 👋 I'm Rehana

I'm a Software Engineer & Data Analyst from Hargeisa, Somaliland.

🧕 About Me

🏆 Hackathon Winner 2024 at Telesom Academy
🎓 Magna Cum Laude graduate (3.86 GPA)
🐛 I actually enjoy debugging (yes, really!)
💻 Tech stack: Python, Django, JavaScript, Power BI, SQL

🚀 What I've Built

Crop IQ – Crop management system for better yield
Telesom Customer Churn Analysis – Data analysis with Power BI
Online Book Store – E-commerce site with Django

📚 What I'm Learning

Machine Learning
Advanced Data Analytics

✍️ What I'll Write About

Data analysis with Python and Power BI
My journey learning new skills
Debugging tips (because I actually like it!)
Lessons from building real projects

🤝 Let's Connect

GitHub: rihhanna
LinkedIn: rehana-hassan

Excited to learn and share with this community! 🚀

Feel free to comment and introduce yourself too 👇