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A-Z Guide to Python in Data Science ๐Ÿ‘‹๐Ÿ›’

Welcome to the A-Z Guide to Python in Data Science repository! This comprehensive supplement offers an exhaustive exploration of Python in the context of Data Science, covering topics from basic to advanced levels of proficiency.

Overview๐Ÿ‘‹๐Ÿ›’

The A-Z Guide to Python in Data Science is your go-to resource for mastering Python's applications in the field of Data Science. Whether you're a novice looking to build a solid foundation or an experienced practitioner aiming to enhance your skills, this repository caters to learners at all levels.

Features๐Ÿ‘‹๐Ÿ›’

1- Comprehensive Coverage: Delve into the vast landscape of Python in Data Science, encompassing fundamental concepts, libraries, tools, and advanced techniques essential for data analysis, visualization, machine learning, and more.

2-Progressive Learning Path: Follow a structured learning path that starts with foundational Python concepts and gradually progresses to advanced topics, ensuring a smooth and comprehensive learning experience.

3- Practical Implementations: Gain practical insights through hands-on implementation examples, exercises, and projects that reinforce theoretical knowledge and foster a deeper understanding of Python's role in Data Science.

4- Supplementary Resources: Access additional resources, including articles, tutorials, datasets, and recommended readings, to supplement your learning journey and stay updated with the latest developments in Python and Data Science.

Contents๐Ÿ‘‹๐Ÿ›’

1- Foundations: Explore basic Python syntax, data types, control structures, functions, and object-oriented programming principles.

2-Data Manipulation and Analysis: Learn how to work with data using popular Python libraries such as NumPy, pandas, and matplotlib for tasks like data cleaning, transformation, aggregation, and visualization.

3- Machine Learning with Python: Dive into the world of machine learning using libraries like scikit-learn and TensorFlow/Keras, covering topics such as regression, classification, clustering, and neural networks.

Contributing๐Ÿ™Œ

Contributions are welcome! Whether it's fixing a bug, enhancing existing content, or adding new material, your contributions can help improve the learning experience for others. Please contact me on skype:themushtaq48, email:[email protected] for guidelines on how to contribute.

Also please subscribe to my Youtube channel

๐Ÿ“ฌContact

If you want to contact me, you can reach me through following social handles.

๐Ÿ™ Special thanks ๐Ÿ™ to our Virtual University of Pakistan students (Mr Saad Abbasi), reviewers, and content contributors, notably Dr Said Nabi

                              Star this repo if you find it useful โญ
                              Also please subscribe to my [Youtube channel](https://www.youtube.com/@coursesteach-mv5si)

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Never miss a tutorial! Get weekly insights, updates, and bonus content straight to your inbox.
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๐Ÿ“• Getting Started-Course 01 - ๐ŸPython

##๐Ÿ“š Python Basics

Tutorial Videoโ–ถ๏ธ Code
โœ…1- Setup Environment for Python 1 Content 3
1- What is mean by programmingโญ๏ธ 1-2 Content 3
๐ŸŒ2- What is Pythonโญ๏ธ 1-2 Content 6
๐ŸŒ3- Python integrated development environment (IDE)โญ๏ธ --- ---
๐ŸŒ4- Best Free Resources to Learn Pythonโญ๏ธ --- ---
๐ŸŒ5- Understanding Variables and Types in Pythonโญ๏ธ Content 2 Colab icon
๐ŸŒ6-Understanding Operators in Python: A Comprehensive Guideโญ๏ธ 1 Colab icon
๐ŸŒ7-Understanding string in Pythonโญ๏ธ Content 2 Colab icon
๐ŸŒ8- Understanding Control Flow in Pythonโญ๏ธ Content 2 Colab icon
๐ŸŒ9- Loops and Iterablesโญ๏ธ 1-2 Colab icon
๐ŸŒ10-Functionโญ๏ธ 1-2 Colab icon
๐ŸŒ11- Dictionaries Content 2 Colab icon
๐ŸŒ12- List Content 2 Colab icon
๐ŸŒ13-Classes and Objects Content 2 Colab icon
๐ŸŒ14-Modules 1 Colab icon
๐ŸŒ15-Packages 1 Colab icon
๐ŸŒ16-File handling -- Colab icon

๐Ÿ“• Course 02 - ๐Ÿ› ๏ธMachine Learning Libraries

๐Ÿ“šChapter: 1 - NumPy

Tutorial Video Code
โœ…1- Exploring the Power of Machine Learning Libraries in Python-G 1 Colab icon
โœ…2- NumPy-Create Array 1 Colab icon
โœ…3- NumPy-Arithmetic Operation -- Colab icon
โœ…4- NumPy-Basics operations 1 Colab icon
โœ…5- NumPy for Statistical Analysis-s 1-2-3-4 Colab icon
โœ…5- NumPy for Linear Algebra-S -- Colab icon
โœ…6- NumPy for Data Cleaningโญ-S -- Colab icon

๐Ÿ“šChapter: 2 - Pandas

Tutorial Video Code
โœ…1- Introduction of Pandas-s 1-2 Colab icon
โœ…2-Pandas Data Structuresโญ๏ธ 1-2 Colab icon
โœ…3-Data Preparation using pandas 1-2 Colab icon
โœ…4-Data Visualization with Pandasโญ๏ธ 1 Colab icon
โœ…5-Data Visualization with Pandasโญ๏ธ -- Colab icon

๐Ÿ“šChapter: 3 - Exploratory Data Analysis(EDA)

Tutorial Video Code
โœ…1-Skimpy 1 Colab icon
โœ…2-Sweetviz 1 Colab icon
โœ…3-Cufflinks --- Colab icon
โœ…5-Pandas Profiling --- Colab icon
*โœ…6-Chartify --- Colab icon
โœ…7-pygwalker 1 Colab icon
โœ…8-Vizard --- Colab icon
๐ŸŒ9-Matplotlib --- Colab icon
๐ŸŒ10-seaborn --- Colab icon
๐ŸŒ11-plotly.express --- Colab icon
๐ŸŒ12-ydata-profiling --- Colab icon
๐ŸŒ13-Vega-Altair --- Colab icon
๐ŸŒ14-PyVista --- Colab icon
๐ŸŒ15-Wordcloud -1 Colab icon
๐ŸŒ16-hvPlot -1 Colab icon

๐Ÿ“šChapter: 5 - Explainable AI (XAI) techniques

Tutorial Video Code Resources
๐ŸŒ1-ExplainerDashboard 1 Colab icon
๐ŸŒ2-SHAP 1 Colab icon 1
๐ŸŒ3-InterpretML 1 Colab icon
๐ŸŒ4-Dalex 1 Colab icon
๐ŸŒ5-LINE 1 Colab icon
๐ŸŒ6-eli5 library 1 Colab icon

๐Ÿ“šChapter: 6 - Auto Machine Learning

Tutorial Video Code Status
โœ…1-Pycaret Introduction 1 Colab icon
โœ…2-Classification 1-2 Colab icon
โœ…3-Regression 1-2 Colab icon
โœ…4- Clustering 1 Colab icon
โœ…5- Anomaly Detection 1 Colab icon
โœ…6-Time Series Forecasting 1 Colab icon
โœ…7-Automated Feature Engineering in PyCaret 1 Colab icon
7-Auto-Sklearn2 --- Colab icon blog need to developed

๐Ÿ“šChapter: 7 - Building Interactive Web Applications with Streamlit

This Chapter introduces students to Streamlit, a powerful open-source app framework designed to create beautiful, interactive, and data-driven web applications in Python with minimal effort. By the end of the course, students will be able to build, deploy, and share fully functional Streamlit apps for use in data science, analytics, and machine learning projects.

Tutorial Video Code Status
โœ…1-Introduction 1 Colab icon
โœ…2-Streamlit vs Flask/Django for ML apps 1 Colab icon

๐Ÿ“• Resources - Other Best Free Resources to Learn Python

๐Ÿ‘๏ธ Chapter1: - Free Courses

Title/link Description Reading Status
โœ… 1-Harvard CS50โ€™s Introduction to Programming with Python by Eddy Shyu,Cousera,Goog InProgress
โœ… 2-Python Cheat Sheet by Eddy Shyu,Cousera,Goog Pending
โœ… 3-Getting Started with Streamlit Stramlite, Other GUI library Pending

๐Ÿ‘๏ธ Chapter1: - Books

Title/link Description Reading Status
โœ… 1-Interpretable Machine Learning by Christoph Molnar A Guide for Making Black Box Models Explainable InProgress

๐Ÿ”นPython Projects**

Title Description Code Status Completed On Author
โœ…1-Create a Simple Voice Assistant --- Colab icon Completed
โœ…2-Autocorrect --- Colab icon Completed
โœ…3-Audio_book --- Colab icon Completed
โœ…4-Chatboot Chat Bot GUI Using Python Colab icon Pending
**โœ…5-Build a Python Module and Share it with Pip Install --- Colab icon Pending

๐Ÿ”นImportant Webites

Title Description Code Status
โœ…1-PyCaret offical website Offical website of Pycaret for learning Colab icon Completed
2-Autocorrect --- Colab icon Completed
3-Audio_book --- Colab icon Completed
4-Chatboot Chat Bot GUI Using Python Colab icon Pending

๐Ÿ‘๏ธ Chapter5: - Github Repository

Title/link Description Status Feedback
โœ… 1- Best-of Machine Learning with Python This curated list contains 920 awesome open-source projects with a total of 4.9M stars grouped into 34 categorie Pending โญ๏ธโญ๏ธโญ๏ธ

Module 01: Basics

================================================================

  1. Basic

  2. Projects

Module 02: Projects that needs to be solved

================================================================

  1. Python Projects need to be solve.

Module 03: Important Python Language Resources

================================================================

  1. Impotant Python resourses

๐Ÿ’ป Workflow:

  • Fork the repository

  • Clone your forked repository using terminal or gitbash.

  • Make changes to the cloned repository

  • Add, Commit and Push

  • Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Python")

๐Ÿ”Explore more๐Ÿ‘‹๐Ÿ›’

Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Donโ€™t wait โ€” enroll now and unleash your.

โœจTop Contributors

We would love your help in making this repository even better! If you know of an amazing Python course that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.

                   Together, let's make this the best AI learning hub website! ๐Ÿš€

Thanks goes to these Wonderful People. Contributions of any kind are welcome!๐Ÿš€

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This repository is related to all about Python - an A-Z guide to the world of Data Science. This supplement contains the implementation of Python from Basic to advance level. Follow Coursesteach for more content ๐Ÿ˜Š

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