10 simple tips for programming Beginners 🐣

⚡️| Practice coding every day.
⚡️| Learn the basic concepts well.
⚡️| Break problems into small steps.
⚡️| Read and learn from error messages.
⚡️| Start with simple beginner projects.
⚡️| Study other people's code.
⚡️| Try solving problems before asking for help.
⚡️| Write clear, well-commented code.
⚡️| Use free online learning resources.
⚡️| Be patient and persistent.

React "❤️" For More

Python - ❤️
Javascript - 😂
Java - 👍

How to Learn Java in 2025

1. Set Clear Goals:
   - Define your learning objectives. Do you want to build web applications, mobile apps, or work on enterprise-level software?


2. Choose a Structured Learning Path:
   - Follow a structured learning path that covers the fundamentals of Java, object-oriented programming principles, and essential libraries.


3. Start with the Basics:
   - Begin with the core concepts of Java, such as variables, data types, operators, and control flow statements.


4. Master Object-Oriented Programming:
   - Learn about classes, objects, inheritance, polymorphism, and encapsulation.


5. Explore Java Libraries:
   - Familiarize yourself with commonly used Java libraries, such as those for input/output, networking, and data structures.


6. Practice Regularly:
   - Write code regularly to reinforce your understanding and identify areas where you need more practice.


7. Leverage Online Resources:
   - Utilize online courses, tutorials, and documentation to supplement your learning.


8. Join a Coding Community:
   - Engage with online coding communities and forums to ask questions, share knowledge, and collaborate on projects.


9. Build Projects:
   - Create simple projects to apply your skills and gain practical experience.


10. Stay Updated with Java Releases:
    - Keep up with the latest Java releases and updates to ensure your knowledge remains current.


11. Explore Frameworks and Tools:
    - Learn about popular Java frameworks and tools, such as Spring Boot, Maven, and IntelliJ IDEA.


12. Contribute to Open Source Projects:
    - Contribute to open source Java projects to gain real-world experience and showcase your skills.


13. Seek Feedback and Mentoring:
    - Seek feedback from experienced Java developers and consider mentorship opportunities to accelerate your learning.


14. Prepare for Certifications:
    - Consider pursuing Java certifications, such as the Oracle Certified Java Programmer (OCJP), to validate your skills.


15. Network with Java Developers:
    - Attend Java meetups, conferences, and online events to connect with other Java developers and learn from their experiences.

Complete Python Roadmap 🐍👇

1. Introduction to Python
- Definition
- Purpose
- Python Installation
- Interpreter vs Compiler

2. Basic Python Syntax
- Print Statement
- Variables and Data Types
- Input and Output
- Operators

3. Control Flow
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Break and Continue Statements

4. Data Structures
- Lists
- Tuples
- Sets
- Dictionaries

5. Functions
- Function Definition
- Parameters and Return Values
- Lambda Functions

6. File Handling
- Reading from and Writing to Files
- Handling Exceptions

7. Modules and Packages
- Importing Modules
- Creating Packages

8. Object-Oriented Programming (OOP)
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction

9. Error Handling
- Try, Except Blocks
- Custom Exceptions

10. Advanced Data Structures
- List Comprehensions
- Generators
- Collections Module

11. Decorators and Generators
- Function Decorators
- Generator Functions

12. Working with APIs
- Making HTTP Requests
- JSON Handling

13. Database Interaction with Python
- Connecting to Databases
- CRUD Operations

14. Web Development with Flask/Django
- Flask/Django Setup
- Routing and Templates

15. Asynchronous Programming
- Async/Await
- Asyncio Library

16. Testing in Python
- Unit Testing
- Testing Frameworks (e.g., pytest)

17. Pythonic Code
- PEP 8 Style Guide
- Code Readability

18. Version Control (Git)
- Basic Commands
- Collaborative Development

19. Data Science Libraries
- NumPy
- Pandas
- Matplotlib

20. Machine Learning Basics
- Scikit-Learn
- Model Training and Evaluation

21. Web Scraping
- BeautifulSoup
- Scrapy

22. RESTful API Development
- Flask/Django Rest Framework

23. CI/CD Basics
- Continuous Integration
- Continuous Deployment

24. Deployment
- Deploying Python Applications
- Hosting Platforms (e.g., Heroku)

25. Security Best Practices
- Input Validation
- Handling Sensitive Data

26. Code Documentation
- Docstrings
- Generating Documentation

27. Community and Collaboration
- Open Source Contributions
- Forums and Conferences

Resources to Learn Python:

1. Free Course
- https://www.freecodecamp.org/learn/data-analysis-with-python/

2. Projects
- t.iss.one/pythonfreebootcamp/177
- t.iss.one/pythonspecialist/90

3. Books & Notes
- https://t.iss.one/dsabooks/99
- https://t.iss.one/dsabooks/101

4. Python Interview Preparation
- https://t.iss.one/PythonInterviews
- t.iss.one/DataAnalystInterview/63

Join @free4unow_backup for more Python resources.

Like this post if you want more content like this 😄❤️

ENJOY LEARNING 👍👍

How to send follow up email to a recruiter 👇👇

Dear [Recruiter’s Name],

I hope this email finds you doing well. I wanted to take a moment to express my sincere gratitude for the time and consideration you have given me throughout the recruitment process for the [position] role at [company].

I understand that you must be extremely busy and receive countless applications, so I wanted to reach out and follow up on the status of my application. If it’s not too much trouble, could you kindly provide me with any updates or feedback you may have?

I want to assure you that I remain genuinely interested in the opportunity to join the team at [company] and I would be honored to discuss my qualifications further. If there are any additional materials or information you require from me, please don’t hesitate to let me know.

Thank you for your time and consideration. I appreciate the effort you put into recruiting and look forward to hearing from you soon.

Warmest regards,

(Tap to copy)

If you want to Excel as a Data Analyst, master these powerful skills:

• SQL Queries – SELECT, JOINs, GROUP BY, CTEs, Window Functions
• Excel Functions – VLOOKUP, XLOOKUP, PIVOT TABLES, POWER QUERY
• Data Cleaning – Handle missing values, duplicates, and inconsistencies
• Python for Data Analysis – Pandas, NumPy, Matplotlib, Seaborn
• Data Visualization – Create dashboards in Power BI/Tableau
• Statistical Analysis – Hypothesis testing, correlation, regression
• ETL Process – Extract, Transform, Load data efficiently
• Business Acumen – Understand industry-specific KPIs
• A/B Testing – Data-driven decision-making
• Storytelling with Data – Present insights effectively

Like it if you need a complete tutorial on all these topics! 👍❤️

Clean code advice for Python:

Do not add redundant context.
Avoid adding unnecessary data to variable names, especially when working with classes.

Example:

This is bad:

class Person:
    def __init__(self, person_first_name, person_last_name, person_age):
        self.person_first_name = person_first_name
        self.person_last_name = person_last_name
        self.person_age = person_age

This is good:

class Person:
    def __init__(self, first_name, last_name, age):
        self.first_name = first_name
        self.last_name = last_name
        self.age = age

Roadmap to become a Programmer:

📂 Learn Programming Fundamentals (Logic, Syntax, Flow)
∟📂 Choose a Language (Python / Java / C++)
∟📂 Learn Data Structures & Algorithms
∟📂 Learn Problem Solving (LeetCode / HackerRank)
∟📂 Learn OOPs & Design Patterns
∟📂 Learn Version Control (Git & GitHub)
∟📂 Learn Debugging & Testing
∟📂 Work on Real-World Projects
∟📂 Contribute to Open Source
∟✅ Apply for Job / Internship

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Oldest Programming Languages Still in Use Today 🕰️

📜 Fortran (1957) – Still used in scientific computing
🤖 Lisp (1958) – Powering AI since the start
💼 COBOL (1959) – Running banks & ATMs
🖥 C (1972) – The godfather of modern languages
🗣 Prolog (1972) – Logic programming OG

React ❤️ For More!

7 Most Popular Programming Languages in 2025

1. Python

The Jack of All Trades

Why it's loved: Simple syntax, huge community, beginner-friendly.

Used for: Data Science, Machine Learning, Web Development, Automation.

Who uses it: Data analysts, backend developers, researchers, even kids learning to code.


2. JavaScript

The Language of the Web

Why it's everywhere: Runs in every browser, now also on servers (Node.js).

Used for: Frontend & backend web apps, interactive UI, full-stack apps.

Who uses it: Web developers, app developers, UI/UX enthusiasts.


3. Java

The Enterprise Backbone

Why it stands strong: Portable, secure, scalable — runs on everything from desktops to Android devices.

Used for: Android apps, enterprise software, backend systems.

Who uses it: Large corporations, Android developers, system architects.


4. C/C++

The Power Players

Why they matter: Super fast, close to the hardware, great for performance-critical apps.

Used for: Game engines, operating systems, embedded systems.

Who uses it: System programmers, game developers, performance-focused engineers.


5. C#

Microsoft’s Darling

Why it's growing: Built into the .NET ecosystem, great for Windows apps and games.

Used for: Desktop applications, Unity game development, enterprise tools.

Who uses it: Game developers, enterprise app developers, Windows lovers.


6. SQL

The Language of Data

Why it’s essential: Every application needs a database — SQL helps you talk to it.

Used for: Querying databases, reporting, analytics.

Who uses it: Data analysts, backend devs, business intelligence professionals.


7. Go (Golang)

The Modern Minimalist

Why it’s rising: Simple, fast, and built for scale — ideal for cloud-native apps.

Used for: Web servers, microservices, distributed systems.

Who uses it: Backend engineers, DevOps, cloud developers.

Free Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17

👨‍💻 FREE Resources to Learn & Practice Python

1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://learnpython.org/
5. https://www.w3schools.com/python/python_exercises.asp
6. https://t.iss.one/pythonfreebootcamp/134
7. https://t.iss.one/pythonanalyst/26
8. https://pythonbasics.org/exercises/
9. https://t.iss.one/pythondevelopersindia/300
10. https://docs.python.org/3/
11. https://t.iss.one/pythonspecialist/33

Join @free4unow_backup for more free resources

ENJOY LEARNING 👍👍