ArewaDS Official Website: https://arewadatascience.github.io

Welcome to the Arewa Data Science Academy Python Programming Fellowship. This comprehensive, free program aims to equip aspiring data scientists and machine learning engineers with essential Python skills. A strong foundation in Python programming will significantly enhance your ability to learn data science and machine learning effectively.

This course is designed around hands-on, bite-sized exercises inspired by "Atomic Habits" by James Clear, to help you build strong Python habits. By completing daily tasks, you'll develop a deeper understanding of Python and apply your knowledge to real-world problems. Remember, consistency is key!

• 📘 ArewaDS Python for Beginners - Cohort 3.0
• 🗂 Table of Contents
  • 💡 Interested in Joining the Fellowship?
  • 🌐 Contact & Community
• 🎉 Welcome to Cohort 3.0 ArewaDS Fellowship
  • 🎓 Graduation Requirements
  • 🎥 Fellowship Kickoff
• 🛠 Setup and Installation
  • Setup Guide
• 🐍 Python Programming
  • 📑 Python Basics & Data Structures
  • 🔄 Control Flow & Functions
  • ⚙️ Advanced Python Concepts
  • 📊 Python for Data Science

Applications for Cohort 3.0 have now closed, but you can still access our materials for self-study. Stay updated on future cohorts by following us on social media and joining our Telegram group for regular updates and fellowship insights.

• 📺 Python Programming YouTube Playlist
• 🌐 Website: Arewa Data Science Official Website
• 📧 Email: [email protected]
• Follow us on:
  • Twitter
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  • LinkedIn
  • YouTube
  • Telegram

Whether you're just starting or deepening your skills, our fellowship offers a structured path to master Python fundamentals and beyond. The fellowship has three main stages:

• Stage 1: Python Programming - Essential Python skills for data science and machine learning (this course).
• Stage 2: Data Science - Data handling, from cleaning to analyzing.
• Stage 3: Machine Learning - Introduction to machine learning techniques and Scikit-learn.

To graduate from the Arewa Data Science and Machine Learning Fellowship, fellows must meet the following criteria:

• Completion of all three stages: Fellows must complete each stage to receive the ArewaDS Certificate.
• Assignments and Blog Posts: Submit all required assignments and a blog post on Medium. Posts must meet quality standards set by mentors.
• Attendance: Maintain a 90% attendance rate for weekly office hours (Saturday and Sunday).
• Capstone Project: Complete a capstone project that demonstrates your ability to apply learned skills to a real-world problem, approved by the ArewaDS Team.

However, for each stage we will provide certificate of completion.

Find the list of accepted fellows, mentor details, recording of the kickoff event, and the slides used during the presentation below.

In this initial part, we’ll guide you through the essential tools needed for data science and machine learning, including installing VSCode, Jupyter Notebooks, Python virtual environments, Git for version control, GitHub for collaboration, Markdown, and creating a Medium blog post.

• 🔗 Introduction
• 🔗 Variables & Built-in Functions
• 🔗 Operators
• 🔗 Strings
• 🔗 Lists, Tuples, and Sets
• 🔗 Dictionaries

• 🔗 Conditionals
• 🔗 Loops
• 🔗 Functions & List Comprehension
• 🔗 Higher-Order Functions

• 🔗 Type Errors & Exception Handling
• 🔗 Regular Expressions
• 🔗 DateTime & File Handling
• 🔗 Modules & Virtual Environment

• 🔗 Package Management
• 🔗 Classes and Objects
• 🔗 Web Scraping & APIs
• 🔗 Statistics, NumPy, and Pandas

We’re excited to have you on board and can’t wait to see all the amazing things you’ll accomplish!

-- A fork on GitHub is essentially a personal copy of someone else's repository that you can modify independently without affecting the original repository. Forking a repository creates a distinct copy under your GitHub account, allowing you to experiment, make changes, and even contribute back to the original repository.

-- Forking vs. Cloning

-- 🎗️🎗️ Forking:

Creates a separate copy of the entire repository (including branches, issues, pull requests) under your GitHub account. You have full control over your fork, including the ability to make changes and create new branches without impacting the original repository. You can submit pull requests to suggest changes to the original project. Cloning:

Downloads a copy of the repository to your local machine but doesn’t create a new repository on GitHub. You can push changes to the original repository if you have write access, but if you don’t, you would only be able to suggest changes by creating a fork. When and Why Should You Use a Fork? Forking is beneficial in the following scenarios:

-- Contributing to Open-Source Projects

-- 🎗️🎗️ When contributing to open-source repositories, forking is the standard approach. It provides a way to make contributions without affecting the main repository. After making changes, you can submit a pull request to propose your contributions for review. Experimenting with a Project:

-- 🎗️🎗️ If you want to experiment with code without impacting the original repository, a fork allows you to test ideas and make changes freely in your personal copy. Collaborating on Large Projects:

-- 🎗️🎗️ In large projects with many contributors, forking helps maintain organization. Contributors can work independently on forks, and maintainers can review and accept pull requests as they see fit. Avoiding Conflicts with Other Contributors:

-- 🎗️🎗️ Forking is helpful in avoiding direct conflicts with other contributors, as everyone works on their separate copy. This structure reduces the risk of overwriting others' work on the original repository. Why Forking is Useful in Open-Source and Collaborative Coding Maintains Project Integrity: Forking allows contributors to make changes without altering the main codebase directly. Only approved changes are merged back, helping maintain the integrity of the original project.

-- Encourages Community Contributions

-- 🎗️🎗️ Forking provides a pathway for open-source contributors to easily make improvements, add features, or fix bugs. They can work on their version and submit a pull request when ready.

Allows Independent Feature Development: Developers can work on new features or bug fixes in their forks. This isolates their work from the main project until it is reviewed and approved, making collaboration more manageable and organized.

-- 🎗️🎗️ In summary, forking is a vital tool in GitHub's collaborative ecosystem, promoting community involvement while safeguarding the original codebase’s stability and quality.