DEV Community: Mohamed Riham

Software Engineering vs Data Science: A Real Talk for Students

Mohamed Riham — Wed, 05 Nov 2025 10:55:29 +0000

Many students today are stuck between choosing Software Engineering or Data Science. And honestly, I understand why. For years, Software Engineering was seen as the safe and guaranteed tech career. But in 2025, the world has changed. AI has reshaped the industry, companies have changed what they look for, and the job market is not the same anymore. If you choose based on old advice, you might be preparing for a world that no longer exists.

Let’s talk about this in a real and honest way.

Most students pick Software Engineering because everyone else is doing it, or because they believe it has more job opportunities. But here’s the reality: the world is full of software graduates now, and competition is tougher than ever. On top of that, AI tools can now write code, build applications, and automate tasks that beginners used to do. So just knowing how to code is no longer enough to stand out.

On the other hand, Data Science became the trending choice because of AI and the high salaries posted online. But many students jump into it without understanding what Data Science actually is. They hear the word "AI" and think it’s only about training models and earning big money. The truth is deeper than that.

So let’s break this down properly.

Why Software Engineering Is Not a Guaranteed Golden Ticket Anymore

Software Engineering still has value, but it’s no longer an automatic success route. The number of software graduates across the world has exploded. Countries like India, Pakistan, Nigeria, Sri Lanka, and many Asian and European countries produce thousands of programmers every year. Even the US is saturated now.

Here is something students don't like to hear: AI has already replaced a huge portion of entry level programming work.

GitHub Copilot, ChatGPT, Codeium, Cursor, and AI tools inside VS Code and IntelliJ can now write code, debug, build full components, generate test cases, and create full stack apps from a single prompt. Big companies like Google, IBM, Meta, and Microsoft have already stated that AI has increased developer productivity so much that fewer junior developers are needed.

So what does this mean for students choosing Software Engineering?

It means you need to specialize. Just saying "I know Python, Java, HTML, React, or Flutter" is not enough anymore. To stand out, you must go into areas like Cloud, DevOps, AI Engineering, Cybersecurity, Mobile Development, AR VR, Robotics, or high performance computing.

The era of "I know to code, please hire me" is over.

Now, to be fair, companies will always need skilled Software Engineers. Software is not disappearing, it’s evolving. The people who succeed are those who combine software skills with AI, problem solving, and product thinking. If you enjoy building systems, apps, and solving real problems with tech, Software Engineering is still a great path. Just don’t expect it to be easy or automatic.

The Truth About Data Science (Not Just the Hype You See Online)

Data Science became popular mainly because of the growth of AI and the high salaries people see on LinkedIn and YouTube. And yes, Data Science, AI, and ML roles are in high demand. But it’s not a field you join just for money. It needs analytical thinking, statistics, math, Python, data handling, and the skill to convert data into insights and decisions.

It’s not about running a few models in scikit learn and calling yourself a Data Scientist.

To be realistic, Data Science has a steeper learning curve than Software Engineering for many students. You must learn statistics, probability, algorithms, ML concepts, data visualization, and business logic. Many students quit halfway because they joined for the hype, not interest.

But here’s what many people ignore. Data Science matches perfectly with the future. Companies don't just want software anymore. They want systems that can learn, predict, automate, and make decisions.

That’s where Data Science, AI, and ML come in.

Look at the world around you:

TikTok is powered by AI recommendation systems
Netflix uses data to personalize user experience
Amazon uses predictive models for logistics and marketing
Tesla uses machine learning for autonomous driving
Banks use AI for fraud detection, credit scoring, and risk analysis

These are not future jobs. They are happening today.

Global Job Market Reality Check

In countries like the US, Canada, UK, Germany, Singapore, UAE, and Australia, Data and AI jobs are growing faster than traditional software roles. Full stack developer jobs still exist, but companies are now more selective because AI already covers many tasks.

Here are some numbers based on 2024 to 2025 industry reports:

AI and Data jobs are expected to grow 35 to 45 percent by 2030
Software developer roles will grow around 15 percent, but with more automation
AI skills increase salary ranges in tech by 20 to 50 percent
Data Engineering and ML Engineering are among the fastest growing roles

Software Engineering is not dying. But it’s no longer the easiest high salary path. The world now rewards people who can build intelligent systems, not just write code.

What Companies Want Now

The smartest students today are not choosing between Software Engineering or Data Science. They are choosing the overlap.

The future belongs to those who can code and think with data.

For example:

A Software Engineer with AI knowledge can build smart applications
A Data Scientist who codes well can deploy solutions, not just build notebooks
A Data Engineer who understands both becomes highly valuable

The market now rewards people with blended skills, not students who stick to only one side.

So What Should You Choose?

Choose Software Engineering if you enjoy building applications, systems, products, and solving engineering problems. But be ready to go beyond basic coding.

Choose Data Science if you love data, patterns, AI, problem solving, and analytical thinking. But be ready to deal with math, models, and continuous learning.

Both are good. The wrong choice is choosing based on trends instead of interest.

The Real Student Truth

If you’re confused right now, that’s completely normal. But don’t choose because of hype or fear. Software Engineering is not the safe path anymore, and Data Science is not an easy high salary shortcut. Both paths need hard work. The ones who succeed are the ones who build skills, create projects, and prove value.

AI did not remove opportunities. It increased the standards.

If you're willing to learn and evolve with technology, both paths can lead to a successful future. If you take them lightly, the AI era will push you out of the game.

Just don’t choose based on the crowd. The world doesn’t need more average coders or trend followers. It needs adaptable learners.

Whatever you choose, learn AI. Learn to adapt. That is the real key to surviving the tech world in 2025.

Choose wisely. The future belongs to those who evolve.
Mohamed Riham

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🎯 Why I Chose BSc (Hons) Data Science After My HND in Software Engineering — The Turning Point in My Tech Journey

Mohamed Riham — Tue, 04 Nov 2025 07:53:11 +0000

Hi DEV Community! 👋

I’m Mohamed Riham, currently pursuing my BSc (Hons) in Data Science after completing my Higher National Diploma (HND) in Software Engineering.

This is not just another academic update — this is the story of how I found my path, why I transitioned from Software Engineering to Data Science, and what truly led me to make one of the most defining decisions of my life.

🌱 Where It All Started — The HND Phase

When I began my HND in Software Engineering, my goal was very clear:

Learn programming
Build real projects
Become a software engineer

And I did exactly that.
During the journey, I gained strong foundations in:

Programming fundamentals
Databases & SQL
Web technologies
Object-Oriented Programming (OOP)
Software Engineering principles

I loved building systems, solving problems, and turning ideas into products that worked.
But as time passed, I started feeling a gap — a curiosity that traditional software development alone couldn’t satisfy.

I wanted to understand something deeper…
Not just how to build applications — but how to make them think, learn, and make decisions.

I didn’t just want to build software…

I wanted to build software that could think.

🧠 My First Encounter with AI & Data Science

During my second semester, I accidentally stepped into the world of Artificial Intelligence and Machine Learning. It started small — with YouTube tutorials, GitHub projects, and online courses.

But that “small spark” turned into a passion.
I started exploring:

What makes a machine understand speech?
How can a system recognize a face?
How do companies like Google, Meta, Netflix, and Tesla make data-driven decisions?

The more I learned, the more I realized that data was the core of everything.

“Software Engineering builds systems. Data Science gives those systems intelligence, insight, and purpose.”

That realization changed everything for me.

🚀 The Turning Point — When Curiosity Became Direction

There wasn’t a single moment — but a combination of three key turning points:

1. Building My First AI Projects

I began with my Face Recognition Attendance System — a project that used face recognition to mark student attendance with real-time alerts to parents.

It was my first hands-on experience with machine learning in a real-world use case, and it opened my eyes to how AI can automate, enhance, and transform everyday systems.

Soon after, I built my own Voice Assistant, ‘EDITH’, combining speech recognition, fuzzy matching, and AI-based responses.
This was the moment I realized something important:

It wasn’t just programming anymore — I was building intelligence.

These projects made me feel something new and exciting —
the power of AI to create real impact.

2. Discovering the Power of Data

I started seeing how data shapes:

business decisions
automation
predictions
recommendations
personalization
innovation

Every industry — from healthcare to banking to entertainment — was moving toward AI and Data.

I realized that the future belongs to data-driven engineers.

🎓 Why I Chose BSc (Hons) Data Science

Here’s the truth — my decision wasn't easy.

I was confused like many students who finish their HND.

Should I continue Software Engineering? Or specialize?

After deep thought, research, and reflection, I chose Data Science because I wanted to:

🔹 Become a Data Scientist / AI Engineer
🔹 Understand data, analytics & machine learning deeply
🔹 Build a future-proof career with global relevance
🔹 Work on impactful, AI-driven real-world solutions

And today, I can confidently say —

I made the right choice.

⚠️ The Struggles No One Talks About

Before choosing Data Science, I faced several challenges that many students experience but rarely speak about:

Challenge	Reality I Faced
⏳ Confusion	I had no idea which specialization to choose next
💰 Financial Limitations	Tech education is expensive, and I wasn’t sure if I could afford the path I wanted
🧠 Self-Doubt	“Am I good enough for AI, math, and data?” constantly ran in my mind
💼 Career Uncertainty	Too many opinions, zero clear guidance — everyone suggested a different route

I almost chose the “safe path”...

But sometimes, the most difficult road is the one that leads to the greatest growth.

✨ What I Have Learned So Far

If I could talk to my past self, I’d say:

“Don’t choose a field just because everyone else does.
Choose the one that makes you feel alive.”

Here’s what truly matters:

Follow your curiosity — it is the first step toward discovering your purpose
Explore before you choose — exposure creates clarity
Find a mentor — the right guidance can change your entire direction
Choose your specialization wisely — it can shape your career and identity

🌍 My Vision Ahead

I’m working towards:

Becoming a Data Scientist building impactful AI/ML solutions
Becoming a researcher in AI/ML, contributing to knowledge and innovation
Creating projects that help people and solve real-world problems
Empowering Sri Lankan students to enter AI and Data Science fearlessly This is just the beginning — and I am excited for the journey ahead.

💬 Final Words for Students Who Are Confused After HND

If you are in that phase where you feel lost, confused, or unsure what to choose next — I want you to know this:

It’s okay not to have it all figured out yet.
It’s okay to explore before you commit to a path.
It’s okay to change direction when you discover what truly excites you.

Don’t settle for what is “safe” or “expected.”
Choose what sparks curiosity, joy, and passion — and build your future around it.

Thank you for reading 🙌
If this helped you or if you’re on a similar journey, let’s connect!

Follow me — Mohamed Riham — for more posts on AI, Data Science, student life, and tech growth stories.

🧠 Building an Accessible Currency Detector for the Sri Lankan Visually Impaired with YOLOv8, ESP32-CAM & Audio Feedback

Mohamed Riham — Sat, 18 Oct 2025 15:11:40 +0000

I built a real-time Sri Lankan Rupee (LKR) currency detector using YOLOv8, ESP32-CAM, and DFPlayer Mini to provide audio feedback. The goal is to create a low-cost assistive tool that helps blind and visually impaired users identify money independently.

💡 The Problem: Identifying Currency Without Sight

For many visually impaired individuals, identifying banknotes is a daily challenge.

While some mobile apps exist, they often require smartphones or lack support for local currencies. I wanted to build a dedicated embedded device — simple, affordable, and tailored for Sri Lankan Rupees — that gives instant audible feedback.

🔧 The Solution: YOLOv8 + Embedded Hardware

My project, Sri Lankan Currency Detector, combines modern computer vision with accessible hardware to deliver fast, reliable, and practical results.

1️⃣ Real-Time Object Detection with YOLOv8

At the heart of the system lies the YOLOv8n model, chosen for its speed and accuracy.

Detects six denominations: Rs. 20, 50, 100, 500, 1000, and 5000.
Trained on a custom dataset of over 1,000 annotated images captured under various lighting and orientation conditions.
Optimized for real-time inference on lightweight systems.

2️⃣ Live Video Capture with ESP32-CAM

To make the system portable and practical:

ESP32-CAM captures live video frames.
The feed is processed on a connected device (PC or Raspberry Pi).
Compact design ensures the system is lightweight and mobile.

3️⃣ Audio Feedback via DFPlayer Mini

This turns the project from just a detection model into a true assistive technology.

When a note is detected, the DFPlayer Mini plays a pre-recorded audio clip (e.g., “100 Rupee note detected”).
Provides instant audible feedback, allowing users to identify currency hands-free.

⚙️ How It Works (Simplified Flow)

Capture: ESP32-CAM captures the live video feed.
Process: Frames are analyzed by the YOLOv8 model.
Detect: The model identifies the denomination of the banknote.
Announce: DFPlayer Mini plays the corresponding audio message.

(You can include a block diagram or workflow image here.)

🧩 Technical Challenges & Lessons Learned

Dataset Diversity: Collected images in various lighting and orientations to ensure robustness.
Model Optimization: YOLOv8n provided a great balance between speed and accuracy; quantization is a future step for full edge deployment.
Hardware Integration: Coordinating serial communication between ESP32-CAM and DFPlayer Mini required careful synchronization.

🚀 Future Improvements

Quantized YOLOv8 model for direct on-device inference.
Multi-language audio support for wider accessibility.
3D-printed enclosure for a portable, user-friendly design.

🧠 Try It Yourself!

All code, model weights, and dataset details are available on GitHub.

I’d love your feedback, suggestions, or collaboration ideas!

🔗 GitHub: mohamed-riham/Sri-Lankan-Currency-Detector-YOLOv8

🎥 Demo Video: Watch on YouTube

🏁 Conclusion

This project shows how AI and embedded systems can create real-world impact when applied thoughtfully. By combining YOLOv8 with ESP32-CAM and DFPlayer Mini, we can bring independence and accessibility to those who need it most.

If you enjoyed this project or have ideas for improvement, let’s connect in the comments! 👇

Tags:

deeplearning #computervision #yolov8 #esp32 #iot #accessibility #assistivetech #python #machinelearning #opensource #MohamedRiham #mohamedriham #riham

I Was Locked Out of My LinkedIn Account for Over a Month — Here's the Brutal Truth

Mohamed Riham — Tue, 15 Jul 2025 11:47:40 +0000

After years of building my LinkedIn profile, growing my network, and engaging with the community, I suddenly got locked out of my account — with no explanation.

LinkedIn asked me to verify my identity using Persona, its automated identity verification system.

At that point, I didn’t even have my NIC due to delays and backlogs from the Sri Lankan government. The only ID I had was my valid driving license.

The AI Said: ❌ “Your license is expired.”

It wasn’t.

My license was issued in 2024 and is valid until 2029.

But the layout of the license — where both issue and expiry dates are printed on the same line — confused the AI system.

There was no human review, no appeal, no support. Just: locked out.

Then I Got My NIC. Tried Again. ❌ “Details not clear.”

Why?

Because Sri Lankan NICs are printed in very small font sizes that the AI couldn’t even read.

Once again:

No manual review
No explanation
No way to get help

Just silence.

🧠 What I Did (and What Finally Worked)

After weeks of frustration, I discovered something:

The AI reads the first date it sees and assumes it’s the expiry date.

So, I covered the issue date on my license, submitted it again...

✅ It worked.

✅ I got my account back.

✅ After one month of hell, I’m finally back online.

💥 A Message to LinkedIn

Please allow users to appeal, explain, or connect with a human when the system fails.
Your AI identity verification system doesn’t understand documents from many countries — especially South Asia.
Professionals lose jobs, visibility, and opportunities when locked out for weeks.

💥 A Message to the Sri Lankan Government

Modernize and digitize our official documents to meet global digital standards.
NICs and licenses should be high-contrast, clearly labeled, and AI-readable.
You're holding back the youth and professionals trying to represent Sri Lanka on global platforms.

This Is Bigger Than Me

This is both a global tech problem and a local document design issue.

If I didn’t find a workaround, I’d still be locked out. And I know many others are stuck in the same situation.

We need:

Better verification systems from tech companies
Smarter, globally compatible documents from governments
Human fallback systems when AI fails

If you’re facing the same issue, feel free to message me — I’ll gladly share what worked for me.

And if anyone from LinkedIn or the Sri Lankan government is reading this — we need to talk.

🚀 From Student to Engineer: My Journey into Applied AI, Voice Assistants, and Beyond

Mohamed Riham — Sun, 15 Jun 2025 13:58:33 +0000

Hi DEV community! 👋

I'm Mohamed Riham, a passionate Software Engineering student and AI developer based in Sri Lanka 🇱🇰.

I've spent the past few years immersed in building real-world applications that bridge academic learning with hands-on experience — and I’d love to share a bit about my journey here.

💡 What I Do

🎓 Software Engineering Student at BCAS Campus
💼 Software Engineer Intern at SoftXpertz
👨‍🏫 Web Developer at Axis Academy

🛠️ Projects I’m Proud Of

Voice Assistant "EDITH"

Rule-based + AI-enhanced personal assistant using speech recognition, fuzzy matching, and edge-tts voice responses — built from scratch in Python.

Sales Data Analysis Dashboard

A full Streamlit & Flask Python app built for Sampath Food City, applying SOLID principles, design patterns (Factory, Singleton, Observer), and deep insights into product performance.

Face Recognition Attendance System

A full student tracking system with QR scanning, facial recognition, real-time alerts to parents, and a local dashboard — with an emphasis on scalability and modularity.

Credit Card Fraud Detection Research

A comparative big data study on Random Forest vs. XGBoost, analyzing their performance for fraud detection with real-world datasets.

🎯 What I Write About

Clean Code & Design Patterns in Python
Applied AI and ML for Real-World Use Cases
Building Voice Interfaces and Assistive Tech
Full-Cycle Student Projects with Professional Touch
From Research to Production in Data Science

📬 Let’s Connect

DEV Profile: @mohamedriham
GitHub: @mohamedriham
LinkedIn: mohamed-riham

I'm here to share, learn, and collaborate.

If you're working on something cool in ML, AI, or system design — or you're a fellow student pushing boundaries — let's connect!

Thanks for reading! 🙌

Follow me for future deep dives into AI engineering, practical ML projects, and how students can build production-grade systems from day one.

🚀 I Built an Offline J.A.R.V.I.S. in Python (No Internet Needed!) DEMO VIDEO INSIDE!

Mohamed Riham — Mon, 26 May 2025 10:45:11 +0000

No Internet. No Privacy Leaks. Just You and Your AI Assistant.

Have you ever dreamed of building your own offline AI voice assistant like J.A.R.V.I.S. or EDITH from the Marvel universe?

Well, I made that dream real. 💥 Introducing EDITH: a smart, voice-controlled, Python-powered AI assistant that works 100% offline, can control your PC, talk back to you, take selfies, and even open apps—with zero reliance on Google, Amazon, or any cloud services.

🧠 What is EDITH?

EDITH stands for Enhanced Digital Interface for Task Handling. It’s a fully offline voice assistant coded in Python that does everything you'd expect from an AI companion:

✨ Key Features:

✅ Voice recognition
✅ Text-to-speech (TTS) with realistic voice
✅ System control: open apps, shut down, restart, sleep
✅ Takes selfies & screenshots
✅ Weather and location updates
✅ Music playback
✅ Local AI fallback if commands are unclear
✅ Works without an internet connection 🔒

🔥 Why This Project is a Game Changer

In a world where AI assistants spy on every word you say, EDITH breaks the pattern. She listens, responds, and acts—without sending your data to the cloud.

This isn't just a fun project—it's a statement:

🛡️ Privacy-first AI is possible.
⚡ Speed without servers.
🧠 Smart without surveillance.

🛠️ Tech Stack Behind the Magic

Python Libraries:
speech_recognition, edge-tts, pyautogui, cv2, pydub, httpx, fuzzywuzzy, keyboard, and more.
Hardware:
Just a mic, and your machine.
APIs (Optional):
OpenWeatherMap (for weather), IPInfo (for offline geolocation).

🧪 What Can EDITH Do? (Real Commands You Can Try)

🎤 "Open Chrome"
📸 "Take a selfie"
🕒 "What time is it?"
🎶 "Play music"
🧠 "Who is Elon Musk?" → (AI fallback answer)
🔌 "Shutdown the system"
💻 "Open Notepad and type a message"

👀 See It in Action

Here’s a sneak peek of EDITH:
👉 See EDITH in action on LinkedIn

🙌 Final Thoughts: Why You Should Build Your Own AI Assistant

✅ Learn real-world Python
✅ Boost your resume
✅ Impress recruiters and tech friends
✅ Take control of your digital life
✅ Build something genuinely cool

💬 What’s Next?

I’m working on adding:

🎯 Wake-word activation (“Hey EDITH”)
📅 Calendar integration
🧠 GPT-powered local reasoning
🤖 Custom task automation

👉 Want to contribute? Fork the project or drop your thoughts in the comments below.

⭐ Like This Project?

🔗 Follow me for more offline AI and Python magic DEV.to, LinkedIn, GitHub.
💬 Drop a comment if you want a detailed tutorial or full code.
📢 Let’s make privacy-first AI assistants the new standard.

Understanding the Basics of REST APIs

Mohamed Riham — Sat, 24 May 2025 16:04:44 +0000

🌐 Understanding the Basics of REST APIs

If you’ve ever used a weather app or fetched data from a website, chances are you’ve interacted with a REST API. But what exactly is a REST API? And why is it so essential in today’s web development world?

Let’s break it down step by step.

🤔 What is an API?

An API (Application Programming Interface) is like a waiter at a restaurant. You (the client) tell the waiter what you want (make a request), the waiter takes it to the kitchen (server), and then brings back your food (the response).

In tech terms, an API allows two software systems to communicate with each other.

🔄 What is REST?

REST (Representational State Transfer) is a set of rules and conventions for building web APIs. RESTful APIs use standard HTTP methods to perform actions on resources.

The 4 most common HTTP methods are:

GET - Retrieve data
POST - Create new data
PUT - Update existing data
DELETE - Remove data

🧱 REST API Example

🛠️ Tools to Try It Out

Postman — For sending API requests
JSONPlaceholder — Free fake REST API for testing

🧠 Final Thoughts

REST APIs are everywhere. Understanding how they work is a key skill for modern developers. Once you grasp the basics, you can build full-stack apps that talk to servers, fetch live data, and power real-world features.

🚀 Stop Wasting Time: 7 AI Tools Every Developer Should Be Using in 2025

Mohamed Riham — Fri, 16 May 2025 18:18:20 +0000

Are you still writing boilerplate code manually or debugging at slow speed? It's 2025 and AI is no longer a nicety, it's your business partner. Whether you are a student, junior programmer, or master engineer, these 7 AI tools can enhance your productivity, creativity, and code quality. Let's start.

🛠️ 1. GitHub Copilot – Your AI pair programmer

Speeds up writing functions, autocompletes code, and even writes tests.
🔥 Tip: Use it for repetitive code, but always review for logic.

🚀 2. Cursor.sh – VS Code + ChatGPT in one

Ask AI to refactor, explain, or generate code in your editor.
💡 Try it when stuck in a bug loop.

⚖️ 3. ChatGPT + Code Interpreter (Advanced Data Analysis)

Analyze logs, CSVs, and visualize data instantly.
✅ Great for backend devs or students working on final-year projects.

🔍 4. Bloop.ai – Search across your codebase with AI

Type a question, get code snippets and function references.
⚡ Game-changer for navigating large codebases.

💻 5. Tabnine – AI autocomplete that respects your style

Lightweight and works offline.
🌐 Use it with privacy-focused projects.

🔑 6. Codeium – Open-source AI code assistant

Free alternative to Copilot.
🔓 Perfect for those not using GitHub tools.

🎤 7. Whisper by OpenAI – Convert voice to code comments or commands

Record your thoughts and let AI write your docs.
🧠 Useful during brainstorms or explaining complex logic.

👨‍💻 Real-Life Example:
I built a Flask web app using Copilot + Cursor.sh in half the usual time. The AI helped debug a routing error that I couldn’t catch for hours. And guess what? I now use ChatGPT for writing commit messages too. 🤖✨

💬 What Tools Do You Use?
What AI tools do you use daily? Drop them in the comments. Let’s build the ultimate 2025 dev toolkit together. 🔧🧠

🏁 Conclusion:
Don’t fear AI. Use it. In a world of tight deadlines and fast-changing tech, the smartest devs aren’t the ones who work harder—but the ones who build smarter.