#uplatz

📖 Your step-by-step journey to becoming a Software Developer in 2025.

1️⃣ Foundations

Computer Science fundamentals: Data Structures & Algorithms

Operating Systems & Networking basics

Problem-solving and logic building

2️⃣ Programming Languages

Core: Python, Java, C++, or JavaScript

Learn multiple paradigms: OOP, Functional, Procedural

Practice with coding platforms (LeetCode, HackerRank)

3️⃣ Software Development Tools

Version Control: Git & GitHub/GitLab/Bitbucket

IDEs: VS Code, IntelliJ, Eclipse

Package managers: npm, pip, Maven, Gradle

4️⃣ Web Development Basics

Frontend: HTML, CSS, JavaScript

Backend: Node.js, Django, Spring Boot

Databases: SQL (MySQL, PostgreSQL) & NoSQL (MongoDB, Firebase)

5️⃣ Application Architecture

Client-server model

APIs: REST & GraphQL

Microservices fundamentals

6️⃣ Testing & Debugging

Unit testing (JUnit, PyTest, Mocha)

Integration testing & debugging tools

Test automation frameworks (Selenium, Cypress)

7️⃣ DevOps & Deployment

CI/CD basics (Jenkins, GitHub Actions, GitLab CI)

Containerisation with Docker

Deployment on AWS, Azure, or GCP

8️⃣ Advanced Concepts

Design Patterns & SOLID Principles

Secure coding practices

Scalability and performance optimisation

9️⃣ Portfolio & Career

Build real-world projects (web apps, mobile apps, APIs)

Contribute to open-source projects

Certifications: Oracle Java, Microsoft, AWS Developer Associate

Choose a specialisation (Frontend, Backend, Full-Stack, Mobile, etc.)

💡 Final Note A Software Developer is a problem-solver at heart. By mastering coding, architecture, and deployment, you’ll build impactful solutions that shape the digital world.

Are you new to data science? Or maybe you just want a strong refresher? Here’s your quick guide to the Top 10 most important formulas — explained simply and clearly.

📘 Why These Formulas Matter

These formulas are used in:

📊 Data analysis

📈 Machine learning

🧠 Pattern recognition

🤖 Predictive modeling

🔢 The 10 Essential Data Science Formulas

1. Mean (Average) Mean = Sum of all values / Number of values

2. Median Odd-length dataset → Middle value Even-length dataset → Average of two middle values

3. Mode Mode = Most frequently occurring value

4. Standard Deviation (σ) σ = sqrt( Σ (xᵢ - μ)² / N )

5. Variance (σ²) σ² = Σ (xᵢ - μ)² / N

6. Correlation Coefficient (r) r = Σ[(xᵢ - x̄)(yᵢ - ȳ)] / √(Σ(xᵢ - x̄)² * Σ(yᵢ - ȳ)²)

7. Logistic Regression P(Y=1) = 1 / (1 + e^-(β₀ + β₁x))

8. Euclidean Distance Distance = √[(x₂ - x₁)² + (y₂ - y₁)²]

9. Cosine Similarity cos(θ) = A · B / (||A|| * ||B||)

10. Probability P(E) = Favorable outcomes / Total outcomes

📌 Bonus Tip

These formulas aren’t just math — they help machines learn, detect fraud, recommend content, and make predictions.