🎓 IBM Data Science Track Courses Materials Repository

Welcome to my Courses Materials Repository! This repository contains teaching materials for the IBM Data Science Track that I used to teach. The track consists of 11 courses, each with its own folder containing slides, Jupyter notebooks, and other supplementary resources, as applicable.

Whether you're a student, educator, or professional, feel free to explore the resources and use them for learning or teaching purposes. 🎉

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📺 YouTube Playlist

As part of this track, I’ve curated a YouTube playlist containing online lectures and resources that complement the course materials. These videos provide additional insights and explanations to help you better understand the concepts covered in this track.

🎥 Access the playlist here: DEPI - IBM TRACK

Feel free to explore the videos and use them alongside the materials in this repository for a more comprehensive learning experience.

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📂 Repository Structure

The repository is organized into folders, one for each course. Inside each course folder, you'll find slides, Jupyter notebooks, and other materials, as applicable.

Here’s an overview of the courses:

1. What is Data Science
   Introduction to data science, its applications, and its importance in solving real-world problems.

2. Tools for Data Science
   Overview of the tools and platforms commonly used in data science, including Jupyter, RStudio, and more.

3. Data Science Methodology
   Learn the structured approach to solving data science problems, including the CRISP-DM methodology.

4. Python for Data Science, AI & Development
   Introduction to Python programming for data science, artificial intelligence, and software development.

5. Python Project for Data Science
   A hands-on project to apply Python skills in a real-world data science scenario.

6. Databases and SQL for Data Science with Python
   Learn how to use SQL and Python to work with databases and extract meaningful insights.

7. Data Analysis with Python
   Explore data analysis techniques using Python libraries like Pandas and NumPy.

8. Data Visualization with Python
   Learn how to create compelling visualizations using Python libraries like Matplotlib, Seaborn, and Dash.

9. Machine Learning with Python
   Introduction to classical machine learning concepts and techniques using Python's Scikit-learn library.

10. Applied Data Science Capstone
    A capstone project to apply all the skills learned in the previous courses to solve a real-world problem.

11. MLOps (DataCamp)
    Learn about MLOps (Machine Learning Operations) and how to deploy and manage machine learning models in production.

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🚀 How to Use

1. Browse the Repository: Navigate to the folder of the course you're interested in.
2. Download Resources: You can download individual files or the entire repository.
3. View PDFs: Use any PDF viewer to access the slide decks.
4. Run Jupyter Notebooks: If you're using the notebooks, make sure you have Python and Jupyter installed. Launch the notebooks locally to run the examples. You can also play with them online via Google Colab ☺️.

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📜 License

This repository is licensed under the MIT License. You are free to use, modify, and distribute these materials, but please credit the original author.

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📝 Feedback and Contributions

I’d love to hear from you! If you have any feedback, suggestions, or questions:

• Open an issue on this repository.
• Feel free to fork the repository and submit a pull request if you’d like to contribute.

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📄 Notebook Sources

This repository contains Jupyter Notebooks that are used as part of the course materials. The sources of the notebooks are as follows:

• Notebooks copied from Coursera: Some Jupyter Notebooks in this repository were downloaded from the Coursera platform as part of the courses I audited or completed. These notebooks are provided for educational purposes and remain the intellectual property of Coursera and its respective instructors.
• Notebooks created by me: Other Jupyter Notebooks were created by me to supplement the course materials or provide additional examples and exercises.

If you have any questions about the sources of specific notebooks, feel free to reach out.

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🌟 Acknowledgments

Thank you for checking out my course materials! These resources represent my efforts to help others learn and grow in the domain of data science. I hope you find them useful.

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📣 Connect With Me

If you’d like to connect or discuss these materials further:

• LinkedIn

Happy learning! 🚀