DEV Community: EddyFrank

Why Building RESTful APIs Is the Backbone of Modern Web Development

EddyFrank — Tue, 15 Jul 2025 18:54:30 +0000

A Dev's Tale of Demo Doom

EddyFrank — Tue, 15 Jul 2025 15:50:07 +0000

"Anything that can go wrong, will go wrong."
Especially when you're 5 minutes away from presenting to your team 😩
I spent the whole weekend coding EventHub like my career depended on it:
✅ Backend routes running flawlessly
✅ Frontend syncing beautifully
✅ Database behaving itself
✅ Console clean - not a single error
✅ Confidence: peaking
By Sunday night, I closed VS Code with a satisfied sigh, whispered "I'm done," and shut the laptop like I just dropped a mic.
But clearly, I also dropped a curse 😅
Fast forward to Monday's demo…
I launch the app and suddenly:
🚨 Server freezes
🚨 Login page won't even load
🚨 Frontend blank like a new canvas
🚨 Database connection lost to the spirit world
🚨 Me: stuck staring at the screen, blinking like a broken chatbot
At one point, I just glanced at my TM, Madam Sonia 👉 https://lnkd.in/gMqmEBg3, silently pleading for divine intervention. She looked back, confused… I looked back at the screen.
Nothing was making sense.
I checked the code. Then I checked it again. Then again.
Even whispered to my laptop like it owed me an explanation.
Still… silence.
It was as if someone secretly ran npm ruin-my-day.
And honestly, if someone told me my ancestors had pushed a hidden bug from the cloud, I would've believed them.
But that's the dev life and I learnt that:
Never trust a feature that was "perfect last night"
Always test right before showtime
Don't panic - code chaos is part of the journey
Laugh at the mess, learn from it, and bounce back
To every dev who's been humbled by a live bug, a missing login, or a frozen UI during presentations - I feel your pain.
We crash, we recover, and we deploy again. 💻🔥

React Hooks(useState & useEffect)

EddyFrank — Wed, 04 Jun 2025 11:00:32 +0000

Ever wondered how top React developers build apps that feel alive—updating instantly with every user action—without drowning in complex code? The secret lies in two powerful React Hooks: useState and useEffect. But how do these hooks work together to manage state and side effects like a pro, and why are they a must-know for every modern React developer? Walk with me through this journey to understand how these hooks transform functional components into dynamic, responsive powerhouses, making your code cleaner and your apps more engaging.

The Dawn of Hooks: Why useState and useEffect Matter
Before Hooks, React developers relied on class components to manage state and lifecycle methods. This often led to bloated code, complex logic, and headaches when sharing stateful logic between components. Introduced in React 16.8, Hooks changed the game by allowing functional components to handle state and side effects. Among them, useState and useEffect stand out as the dynamic duo that every React developer needs in their toolkit.

useState: This hook lets you add state to functional components, enabling them to "remember" data between renders.
useEffect: This hook handles side effects—think API calls, DOM updates, or subscriptions—ensuring your app stays in sync with the outside world.
Together, they enable you to build interactive features like real-time form updates, dynamic data fetching, or animated UI elements with minimal fuss. Let’s explore each hook and see how they work in harmony.

Step 1: Mastering useState – The Heart of Reactivity
Imagine you’re building a simple counter app. The user clicks a button, and the number increments. Without state, the number would reset on every render. Enter useState, which gives your component a memory.

Here’s how it works:

Data Science for Beginners: 2023 - 2024 Complete Roadmap

EddyFrank — Tue, 03 Oct 2023 20:19:30 +0000

Data science is a solid, rapidly growing field with plenty of untapped potential. It has become a major part of the modern-day business world, playing an important role in helping businesses make critical business decisions through logic rather than intuition alone. The journey to being a data scientist isn’t an easy road, but with focus, determination, and consistency, one is able to thrive in this field.

Why Data Science?

The world is constantly changing, with new technologies being introduced on a daily basis, and for organizations and businesses to remain competitive enough, there is a need to ensure that their data plays a useful role in driving decisions. Data science calls for analyzing big data to find patterns and insights that can drive business decisions and enable organizations to increase profits.

To a beginner, it may seem challenging and impossible to become a data scientist. However, with a great roadmap, the journey can be quite smooth for an aspiring data scientist to put in the work to become one.

Data Science Roadmap for a Beginner

As a beginner, the learning process involves several tools and concepts and practicing and implementing what one has learned. Below is a stepwise roadmap that also requires persistence and determination to help you get started in data science.

1. Learn SQL

SQL is a query language used for managing and manipulating data stored in databases. SQL allows a data scientist to query data from various sources and retrieve, filter, and aggregate the data. A beginner may use tutorials, boot camps, and SQL challenges to gain a strong foundation in SQL programming language before embarking on the next step.

2. Learn Python/R Programming Languages

Python and R are the widely used programming languages for data science, and a good foundation on this is important for data manipulation, visualization, and machine learning models. Knowing the use of variables, tuples, functions, loops, and other frameworks such as Numpy, Pandas, and sci-kit-learn is really helpful.

3. Visualization Tools

The next step after having learned and gained a good foundation in SQL and Python/R is learning how to use visualization tools to create interactive and visually appealing dashboards, graphs, and charts to communicate your data. Tolls such as PowerBI, Tableau, and QlikView can be of great help for visualization. As a beginner, it’s better to choose one tool and focus on it first before exploring other visualization tools.

4. Basic Statistics for Machine Learning

Basic statistics concepts in statistics, probability, and linear regression are crucial for machine learning and data science in general. As a beginner, you are required to have a strong background in these concepts. Machine learning is a subset of data science that uses algorithms to learn from and make predictions from data. To get started, one can use online resources and tutorials.

5. Machine Learning Algorithms

Machine learning algorithms play a major part in the entire data science process, and upon getting the needed skills in statistics as a beginner, you’ll be required to learn machine learning algorithms such as linear regression, decision trees and K-means clustering, to begin with, and online tutorials and courses can be of great advantage to start.

6. Practice and Implementation

As it is commonly known, practice makes perfect, and as a beginner, this phrase is of great importance. A beginner is required to work on several projects and exercises to apply their learned skills. Participating in hackathons and online communities can assist in learning from others and getting feedback for projects worked on. One is required to work on real-world data sets to help solve a real-world problem and to apply their skills to analyze data effectively. Building models and testing them on unseen data can help a beginner gain practical experience and build a good portfolio to kick start their career in data science.

For individuals interested in data to solve complex problems and make good decisions, data science is the path to follow. And with good communication skills, knowledge, and experience, data science can prove to be a rewarding career.