🎒 Lecture Backpack

Welcome to the Lecture Backpack 🚀, a comprehensive resource repository designed for students and enthusiasts in software engineering, data science, full stack web development, and anyone looking to sharpen their coding interview skills. Our goal is to provide an all-encompassing learning experience that caters to a wide range of educational needs, from basic programming concepts to specialized topics in various tech fields.

Overview 📚

This repository is structured into five main folders, each targeting a specific learning track:

• Python : A foundational phase for Software Engineering and Data Science 🐍 that introduces basic programming concepts, including the terminal, setting up development environments and basic procedural programming in Python.

• Software Engineering (SE) : Building on Python phase, the SE track delves into advanced software development topics. It covers:

  • Version control with GitHub
  • Debugging techniques and tools
  • Software design patterns and principles
  • Agile methodologies and unit testing
  • Application development with Django and SQL

• Data Science (DS) : Following the Python introduction, the DS track focuses on data manipulation and analysis. Key topics include:

  • Data cleaning and preprocessing
  • Exploratory data analysis (EDA) with Pandas
  • Visualization with Matplotlib and Seaborn
  • Statistical analysis and hypothesis testing
  • Introduction to machine learning with scikit-learn

• Full Stack Web Development (WD) : Starting with web basics, the terminal and GitHub, this track progresses to comprehensive web development skills, encompassing:

  • HTML, CSS, and introductory programming in JavaScript.
  • Responsive design and CSS frameworks like Bootstrap
  • Front-end development with React.js
  • RESTful API development with Node.js, Express.js and MongoDB
  • Application security with JWT

• Coding Interview Workshops : A series of workshops 🧩 focusing on fundamental computer science concepts, common interview questions, and problem-solving strategies. Topics include:

  • Data structures (arrays, lists, stacks, queues, hashtables trees, graphs)
  • Algorithms (sorting, searching, recursion)
  • Complexity analysis and space-time trade-offs
  • Coding exercises targeting specific interview scenarios

Getting Started 🚦

To get started with the Lecture Backpack, follow these steps:

1. Clone the repository: Use git clone https://github.com/skills-cogrammar/C7-Lecture-Backpack.git to clone this repository to your local machine.
2. Choose your track: Begin with the Python folder if you're in weeks 1 - 4 of Software Engineering or Data Science, and move directly to your either Data Science or Software Engineering for weeks 5 - 16. If you're Full Stack Web Development, simply jump straight to the Full Stack Web Development folder. For coding interview preparation, please jump to the Coding Interview Workshop folder.

License 📄

This project is licensed under the MIT License - see the LICENSE.md file for details.

Happy Learning! 🎓