Steps to become a data analyst

Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://news.1rj.ru/str/learndataanalysis

Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:

SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst

Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst

Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst

Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst

Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).

Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.

Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.

Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.

Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.

Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss

Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.

Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.

Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.

Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.

ENJOY LEARNING 👍👍

Useful Cheatsheets for Programmers
👇👇

Data Science Cheatsheet

https://github.com/aaronwangy/Data-Science-Cheatsheet

SQL Cheatsheet

https://learnsql.com/blog/sql-basics-cheat-sheet/

https://news.1rj.ru/str/programming_guide/299

https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf

https://news.1rj.ru/str/learndataanalysis/442

Java Programming Cheatsheet

https://introcs.cs.princeton.edu/java/11cheatsheet/

PHP and Ruby Cheatsheets

https://news.1rj.ru/str/programming_guide/300
https://news.1rj.ru/str/programming_guide/301

Pandas in 5 minutes

https://bit.ly/3EZgNgF

Python Cheat sheet

https://news.1rj.ru/str/pythondevelopersindia/314

UML Cheat sheet

https://www.guru99.com/uml-cheatsheet-reference-guide.html.

GIT and Machine Learning Cheatsheet

https://news.1rj.ru/str/datasciencefun/714

Javanoscript Cheat sheet

https://news.1rj.ru/str/programming_guide/623

HTML Cheatsheet

https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf

ENJOY LEARNING 👍👍

FREE RESOURCES to Learn Data Visualization
👇👇

Data Visualization in Tableau Free Udacity Course

https://imp.i115008.net/2rMPVg

Tableau Tutorial for Beginners FREE UDEMY COURSE

https://bit.ly/3H3uhX5

Free Power BI course from Microsoft

https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em

Data Visualization with Python FREE COURSE

https://cognitiveclass.ai/courses/data-visualization-with-python

Free Training Videos for Tableau

https://www.tableau.com/learn/training/20201

Learn Power BI Basics for Free from Udemy

https://bit.ly/3sTa6Gj

Microsoft Excel - Basic Data Visualization in Excel
[4.4 star ratings out of 5]

https://bit.ly/3JDEs6A

Handbook of Data Visualization FREE BOOK

https://haralick.org/DV/Handbook_of_Data_Visualization.pdf

Tableau tutorial

https://news.1rj.ru/str/PowerBI_analyst/19

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ENJOY LEARNING 👍👍

Learning one of these Tech Skills can change your life:

1. Cloud Computing/ Amazon Web Services
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2. Data Engineering / Data Science
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3. Cybersecurity/ Ethical Hacking
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https://news.1rj.ru/str/EthicalHackingToday

4. Machine Learning/AI/Robotics
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5. Web/App Development
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6. Data Analysis
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https://news.1rj.ru/str/sqlspecialist/379

7. Data Visualization
https://news.1rj.ru/str/PowerBI_analyst

8. UI/UX Design
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9. Blockchain
https://news.1rj.ru/str/free4unow_backup/389

10. Android App Development
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Stepwise Guide on becoming a software engineer 😄👇

Choose a Programming Language: Start by picking a programming language to learn. Popular choices for beginners include Python, JavaScript, or Java.

Learn the Basics: Begin with the fundamentals of programming, including variables, data types, control structures (if-else, loops), and basic algorithms.

Data Structures and Algorithms: Gain a solid understanding of data structures (arrays, linked lists, stacks, queues) and algorithms. Telegram channels like @crackingthecodinginterview can be helpful.

Online Courses and Tutorials: Take advantage of online courses and tutorials. Platforms like Coursera, edX, and Codecademy offer a wide range of programming courses. Many free resources are shared in this channel. Just search for the desired skill/course based on your interest in this channel.

Build Projects: Practical experience is key. Create small software projects to apply what you've learned. Start with simple projects and gradually work your way up to more complex ones.

Version Control (Git): Learn how to use Git for version control. It's essential for collaborative software development.

Explore Different Fields: Software development is vast. Explore different areas like web development, mobile app development, data science, or game development to find your niche.

Contribute to Open Source: Consider contributing to open-source projects. It's a great way to gain real-world experience, collaborate with others, and build a portfolio.

Build a Portfolio: Create a portfolio of your projects on platforms like GitHub or a personal website. Showcase your skills and projects to potential employers.

Internships and Job Search: Look for internships or entry-level positions to gain professional experience. Tailor your resume and cover letter to highlight your skills and projects. Many telegram channels like @getjobss or linkedin platform might be useful to find your desired job/internship.

Interview Preparation: Practice coding interviews. Use resources like LeetCode, HackerRank, or InterviewBit to improve your problem-solving skills.

Soft Skills: Develop soft skills like communication, teamwork, and time management. These are essential in a professional environment.

Continuous Learning: Technology evolves rapidly. Stay updated by reading blogs, books, and taking advanced courses to deepen your knowledge.

Build a Strong Online Presence: Engage in tech communities, write blog posts, or share your insights on platforms like LinkedIn to showcase your expertise.

Be Persistent: Landing your first job can be challenging. Keep applying, learning, and improving your skills. Don't get discouraged by rejections.
Remember that becoming a software engineer is a journey, and it may take time. Stay committed to learning and adapting to new technologies, and you'll progress in your career.

ENJOY LEARNING 👍👍

Free Courses/ Certifications for a full-stack career in 2023:

1. Web design https://freecodecamp.org/learn/2022/responsive-web-design/

2. JavaScript https://freecodecamp.org/learn/javanoscript-algorithms-and-data-structures/

https://bit.ly/3PI8JWB

3. Front-end libraries https://freecodecamp.org/learn/front-end-development-libraries/

5. Back-end and APIs
https://freecodecamp.org/learn/back-end-development-and-apis/

6. Databases
https://freecodecamp.org/learn/relational-database/

7. Full Stack Web Development Bootcamp
https://news.1rj.ru/str/webdevcoursefree/461

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ENJOY LEARNING 👍👍

Free Online courses with certificate from Microsoft

Python for beginners
https://learn.microsoft.com/en-us/training/paths/beginner-python/

Get started with Azure Cosmos DB for NoSQL
https://learn.microsoft.com/en-us/training/paths/get-started-azure-cosmos-db-sql-api/

Introduction to machine learning with Python and Azure Notebooks
https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/

Automate development tasks by using GitHub Actions
https://bit.ly/48E75xT

SQL, Power BI & AI Fundamentals
https://bit.ly/3ZL2820

Write your first code using C#
https://learn.microsoft.com/en-us/training/paths/get-started-c-sharp-part-1/

Join @free4unow_backup for more free resources

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Websites and platforms where you can practice Python projects, do hands-on coding, and gain valuable experience

1. w3schools
(https://www.w3schools.com/python): Offers interactive Python courses and coding exercises. Great for beginners.

2. Learn Python (https://learnpython.org/): Good resource for beginners

3. Freecodecamp (https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course): Offers Python courses, including those from top universities. You can earn certificates upon completion.

4. Hackerrank (https://www.hackerrank.com/domains/tutorials/10-days-of-python): Offers Python tutorials and coding challenges to practice your skills.

5. Google (https://developers.google.com/edu/python): Free resource to learn python from Google.

6. Project Euler (https://projecteuler.net/): Provides mathematical and computational problems that can be solved with Python. Great for honing your programming skills.

7. Python.org (https://docs.python.org/3/tutorial/index.html): The official Python website has a tutorial section that includes exercises and examples to practice Python concepts.

8. GitHub (https://github.com/): Explore Python repositories, contribute to open-source projects, or start your own Python project to build a portfolio.

9. Kaggle (https://www.kaggle.com/): Offers Python datasets, competitions, and notebooks for data science and machine learning projects.

10. Udemy (https://bit.ly/45q7pxh): Amazing course to master Python in 100 days with coding challenges with certificate. Learn data science, automation, build websites, games and apps!

11. Real Python (https://realpython.com/): Offers tutorials and articles on various Python topics, including practical projects.

Remember that practice is key to mastering Python. Choose projects and exercises that align with your interests and goals, and don't hesitate to explore multiple platforms to diversify your learning experience.

ENJOY LEARNING 👍👍

Master Artificial Intelligence in 10 days with free resources 😄👇

Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.

Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.

Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.

Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.

Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.

Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.

Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.

Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

Here are 5 amazing AI projects with free datasets: https://bit.ly/3ZVDjR1

Throughout the 10 days, it's important to practice what you learn through coding and practical exercises. Additionally, consider reading AI-related books and articles, watching online courses, and participating in AI communities and forums to enhance your learning experience.

Free Books and Courses to Learn Artificial Intelligence
👇👇

Introduction to AI Free Udacity Course

Introduction to Prolog programming for artificial intelligence Free Book

Introduction to AI for Business Free Course

Artificial Intelligence: Foundations of Computational Agents Free Book

Learn Basics about AI Free Udemy Course
(4.4 Star ratings out of 5)

Amazing AI Reverse Image Search
(4.7 Star ratings out of 5)

13 AI Tools to improve your productivity: https://news.1rj.ru/str/crackingthecodinginterview/619

4 AI Certifications for Developers: https://news.1rj.ru/str/datasciencefun/1375

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

Complete Roadmap to become a data scientist in 5 months

Free Resources to learn Data Science: https://news.1rj.ru/str/datasciencefun

Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.

Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.

Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.

Work on Data Science Projects: https://news.1rj.ru/str/pythonspecialist/29

Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.

Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.

Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).

Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).

Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).

Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.

Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.

Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.

Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.

Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.

Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.

Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.

Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.

Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.

ENJOY LEARNING 👍👍

30-day roadmap to get started with web development

Week 1: HTML and CSS
- Day 1-3: Learn HTML basics (structure, tags, elements).
- Day 4-7: Dive into CSS (styling, selectors, layouts).

Week 2: Advanced CSS and Responsive Design
- Day 8-11: Explore advanced CSS concepts (flexbox, grid).
- Day 12-14: Learn about responsive web design.

Week 3: JavaScript Fundamentals
- Day 15-18: Get started with JavaScript (variables, data types, operators).
- Day 19-21: Study JavaScript functions and control structures.

Week 4: JavaScript DOM Manipulation and Frameworks
- Day 22-25: Understand the Document Object Model (DOM) and how to manipulate it with JavaScript.
- Day 26-28: Explore JavaScript frameworks/libraries (e.g., React, Vue, Angular).
- Day 29-30: Build a simple project combining HTML, CSS, and JavaScript.

Complete Web development bootcamp: https://news.1rj.ru/str/webdevcoursefree/3

Here are 5 beginner-friendly web development projects: https://news.1rj.ru/str/Programming_experts/455

Remember, practice and building projects are crucial. Adjust the pace based on your learning speed, and feel free to delve deeper into areas that interest you. Web development is an ongoing learning process. Good luck!

ENJOY LEARNING 👍👍

Steps to learn Data Structures and Algorithms (DSA) with Python

1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.

2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.

3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview

4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.

5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.

6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.

7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.

8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.

9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.

10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.

11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.

12. Competitive Programming: Participate in competitive programming contests. They are a great way to test your skills and improve your problem-solving abilities.

13. Stay Updated: DSA is an ever-evolving field. Stay updated with the latest trends and algorithms.

14. Contribute to Open Source: Consider contributing to open source projects. It's a great way to apply your knowledge and work on real-world code.

15. Teach Others: Teaching what you've learned to others can deepen your understanding. You can create tutorials or mentor someone.

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ENJOY LEARNING 👍👍

How to Create Resume using ChatGPT

1. Gather Information: Before you start, collect all the necessary information for your resume, including your contact details, work experience, education, skills, and achievements.

2. Use ChatGPT for Content Generation:
- Begin by specifying the role or field you're targeting. For example, "I am seeking a [job noscript] position."
- Ask ChatGPT for help in creating content for your resume. For instance, "Can you provide a summary of my work experience?" or "Please list my skills and achievements related to [specific skill or project]."

You can also use ChatGPT for your next interview 👉 https://news.1rj.ru/str/getjobss/1483

3. Proofread and Edit: ChatGPT can generate content, but it might not always be perfect. Make sure to carefully review and edit the generated text for accuracy, clarity, and conciseness.

4. Resume Structure:
- Ensure your resume follows a standard structure, with sections for Contact Information, Summary or Objective, Work Experience, Education, Skills, and Additional Sections (e.g., certifications, awards, projects).
- Use bullet points to make information concise and easy to read.

5. Contact Information:
- Include your full name, phone number, email address, and LinkedIn profile (if applicable).

6. Summary or Objective:
- Craft a brief, impactful summary or objective statement highlighting your career goals and what you can bring to the role.

7. Work Experience:
- List your work experience in reverse chronological order (most recent job first).
- For each position, include the job noscript, company name, location, dates of employment, and a concise denoscription of your key responsibilities and accomplishments.

8. Education:
- Include your educational background, listing degrees, institutions, dates, and any relevant honors.

9. Skills:
- Enumerate your skills, such as technical, soft skills, or certifications.

10. Additional Sections:
- Depending on your background, you might add sections for certifications, awards, volunteer work, or projects.

11. Formatting:
- Ensure consistent font, size, and formatting throughout the document.
- Use a professional and easily readable font.

12. Save and Share: Save your resume in a common format like PDF, which preserves formatting. You can then share it with potential employers.

Don't limit yourself to completing it solely through automated tools. Feel free to infuse your own variations and personal touch. Your individuality and creativity are what will truly make you stand out in the job market. Additionally, consider seeking feedback from professionals or a career counselor to further refine your resume.

Join @free4unow_backup for more free tips

ENJOY LEARNING 👍👍

Master Python programming in 15 days with Free Resources 😄👇

Days 1-3: Introduction to Python
- Day 1: Start by installing Python on your computer.
- Day 2: Learn the basic syntax and data types in Python (variables, numbers, strings).
- Day 3: Explore Python's built-in functions and operators.

Days 4-6: Control Structures
- Day 4: Understand conditional statements (if, elif, else).
- Day 5: Learn about loops (for and while) and iterators.
- Day 6: Work on small projects to practice using conditionals and loops.

Days 7-9: Data Structures
- Day 7: Learn about lists and how to manipulate them.
- Day 8: Explore dictionaries and sets.
- Day 9: Understand tuples and lists comprehensions.

Days 10-12: Functions and Modules
- Day 10: Learn how to define functions in Python.
- Day 11: Understand scope and global vs. local variables.
- Day 12: Explore Python's module system and create your own modules.

Days 13-15: Intermediate Concepts
- Day 13: Work with file handling and I/O operations.
- Day 14: Learn about exceptions and error handling.
- Day 15: Explore more advanced topics like object-oriented programming and libraries such as NumPy, pandas, and Matplotlib.

FREE RESOURCES TO LEARN PYTHON 👇

Microsoft course for Python: https://learn.microsoft.com/en-us/training/paths/beginner-python/

Python for data Science and Machine Learning: https://news.1rj.ru/str/datasciencefree/69

Python Interview Questions & Answers: https://news.1rj.ru/str/dsabooks/96

Harvard course for Python: http://cs50.harvard.edu/python/2022/

Freecodecamp Python course with certificate: https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

Master Java programming in 15 days with Free Resources 😄👇

Days 1-3: Getting Started
1. Day 1: Install Java Development Kit (JDK) on your computer and set up your development environment.
2. Day 2: Learn the basics of Java syntax, variables, data types, and how to write a simple "Hello, World!" program.
3. Day 3: Dive into Java's Object-Oriented Programming (OOP) concepts, including classes and objects.

Days 4-6: Control Flow and Data Structures
4. Day 4: Study control flow structures like if statements, loops (for, while), and switch statements.
5. Day 5: Learn about data structures such as arrays and ArrayLists for handling collections of data.
6. Day 6: Explore more advanced data structures like HashMaps and Sets.

Days 7-9: Methods and Functions
7. Day 7: Understand methods and functions in Java, including method parameters and return values.
8. Day 8: Learn about method overloading and overriding, as well as access modifiers.
9. Day 9: Practice creating and using methods in your Java programs.

Days 10-12: Exception Handling and File I/O
10. Day 10: Study exception handling to deal with runtime errors.
11. Day 11: Explore file input/output to read and write data to files.
12. Day 12: Combine exception handling and file I/O in practical applications.

Days 13-15: Advanced Topics and Projects
13. Day 13: Learn about Java's built-in libraries, such as the Collections framework and the java.util package.
14. Day 14: Explore graphical user interfaces (GUI) using Java Swing or JavaFX.
15. Day 15: Work on a Java project to apply what you've learned. Build a simple application or program of your choice.

FREE RESOURCES TO LEARN JAVA 👇👇

Introduction to Programming in Java: https://ocw.mit.edu/courses/6-092-introduction-to-programming-in-java-january-iap-2010/

Java Tutorial for complete beginners: https://bit.ly/3MkvQWf

Introduction to Java Programming and Data Structures: https://news.1rj.ru/str/programming_guide/573

Project Ideas for Java: https://news.1rj.ru/str/Programming_experts/457

Free Website to Practice Java https://www.hackerrank.com/domains/java

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

Master C programming in 30 days with free resources

Week 1: Basics
1. Days 1-3: Learn the basics of C syntax, data types, and variables.
2. Days 4-7: Study control structures like loops (for, while) and conditional statements (if, switch).

Week 2: Functions and Arrays
3. Days 8-10: Understand functions, how to create them, and pass parameters.
4. Days 11-14: Dive into arrays and how to manipulate them.

Week 3: Pointers and Memory Management
5. Days 15-17: Learn about pointers and their role in C programming.
6. Days 18-21: Study memory management, dynamic memory allocation, and deallocation (malloc, free).

Week 4: File Handling and Advanced Topics
7. Days 22-24: Explore file handling and I/O operations in C.
8. Days 25-28: Learn about more advanced topics like structures, unions, and advanced data structures.
9. Days 29-30: Practice and review what you've learned. Work on small projects to apply your knowledge.

Throughout the 30 days, make sure to:
- Code every day to reinforce your learning.
- Use online resources, tutorials, and textbooks.
- Join C programming communities and forums for help and discussions.
- Solve coding challenges and exercises to test your skills (e.g., HackerRank, LeetCode).
- Document your progress and make notes.

Free Resources to learn C Programming
👇👇

Introduction to C Programming

CS50 Course by Harvard

Master the basics of C Programming

C Programming Project

Let Us C Free Book

Free Interactive C Tutorial

Join @free4unow_backup for more free courses

ENJOY LEARNING 👍👍

🔟 Free Certification Courses to learn data analytics in 2023:

1. Get started with Microsoft data analytics

2. Microsoft Excel for Professionalsl

3. Model data with Power BI

4. Build Power BI visuals and reports

5. Excel & Power BI

6. 𝗗𝗮𝘁𝗮 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀

7. SQL Roadmap for Data Analytics

8. Python for Data Analysis & Visualization

9. 𝗦𝗾𝗹 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀

10. Data Analysis with Python

Join @free4unow_backup for more free resources

ENJOY LEARNING 👍👍

Hi guys, this is a group for people who want to discuss, solve, and work on a kaggle competitions & projects.
👇👇
https://news.1rj.ru/str/Kaggle_Group

Quick Roadmaps to start learning something new before 2024 😄

👉 Java

👉 Python

👉 Javanoscript

👉 Data Analysis

👉 Data Science

👉 Frontend development

👉 AI/ML

👉 SQL

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