This is a collection of data science Courses, notebooks, projects, and resources that document our journey in the world of data science and machine learning.
In this repository, we share our data science explorations, including datasets, Jupyter notebooks, scripts, and documentation. Our goal is to learn, experiment, and contribute to the data science community. Whether you're a beginner or an experienced data scientist, you can find something here to enhance your knowledge and skills.
We organize our Modules in separate directories, each dedicated to a specific topic or project. Here are some of the Modules you'll find in this repository:
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Git & GitHub : Git: Git is a version control system that lets you track changes to your code, collaborate with others, and revert to earlier versions if needed. It’s essential for managing code efficiently, especially in projects where multiple people are working together. GitHub: GitHub is a platform that hosts Git repositories online, making it easy to share code, collaborate on projects, and showcase your work. It also provides tools like issue tracking, pull requests, and project management to streamline teamwork..
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Basics-of-Python-Programming: Python is a versatile, beginner-friendly programming language widely used in Data Science for tasks like data manipulation, analysis, and visualization. Its libraries, such as Pandas, NumPy, and Matplotlib, simplify complex computations and make data handling efficient and accessible.
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Python-for-Data-Scientists:Python is a core tool for Data Scientists due to its powerful libraries like Pandas, NumPy, and Scikit-learn, which streamline data cleaning, analysis, and model building. Its simplicity and community support make it ideal for handling large datasets, visualizations, and machine learning tasks..
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Semester Project: Description of project.
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To get started with the projects and resources in this repository, follow these steps:
Make sure you have the following prerequisites installed:
- Python (3.11 recommended)
- Jupyter Notebook
- Libraries listed in the
requirements.txtfile
You can install the required libraries using pip:
pip install -r requirements.txtClone this repository to your local machine:
git clone https://github.com/nadeem-majeedch/IntroductionToDataScienceF22.git- Navigate to the project or resource you're interested in.
- Open and run Jupyter notebooks to explore the data and models.
- Feel free to modify and experiment with the code to learn and improve your skills.
We welcome contributions from the data science community. If you have an interesting project, improvement, or resource to share, please follow our Contribution Guidelines.
This repository is licensed under the MIT License.