https://github.com/sidraanl-08/completedatascience
this is complete data science repo for beginners
https://github.com/sidraanl-08/completedatascience
anaconda-environment anaconda3 data-science jupyter-notebook jupyterlab
Last synced: 24 days ago
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this is complete data science repo for beginners
- Host: GitHub
- URL: https://github.com/sidraanl-08/completedatascience
- Owner: sidraanl-08
- Created: 2025-10-24T20:19:43.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-12-11T17:19:01.000Z (7 months ago)
- Last Synced: 2025-12-12T22:08:12.495Z (7 months ago)
- Topics: anaconda-environment, anaconda3, data-science, jupyter-notebook, jupyterlab
- Language: Jupyter Notebook
- Homepage:
- Size: 5.33 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🧠 Complete Data Science Repository
Welcome to my **Complete Data Science Repository** — a structured collection of notebooks, scripts, and projects documenting my journey into **Data Science**, **AI**, and **Machine Learning**.
This repository showcases my learning, from Python foundations to complete data analysis workflows, guided by **CodeWithHarry’s Data Science Course**.
---
## 🎯 Learning Objectives
- Strengthen **Python fundamentals** 🐍
- Explore the **Data Science pipeline** step by step
- Apply real-world datasets for **analysis and visualization**
- Build foundational **Machine Learning models**
- Understand **statistics, probability, and linear algebra basics**
- Learn **version control** with Git & GitHub
---
## 📘 Course Reference – CodeWithHarry
This repository is guided by **CodeWithHarry’s online Data Science course**.
His teaching has provided a **clear, practical, and structured approach** to learning Data Science.
### 🧩 Topics Covered
1. Introduction to Data Science & workflow overview
2. Setting up environment (Anaconda, Jupyter, VS Code)
3. Python essentials (variables, loops, conditionals, functions, OOP)
4. Working with data structures — lists, dicts, tuples, sets
5. File handling (CSV, JSON, text files)
6. Libraries:
- **NumPy** → numerical operations
- **Pandas** → data manipulation & cleaning
- **Matplotlib / Seaborn** → visualization
7. Handling missing values & preprocessing data
8. Exploratory Data Analysis (EDA)
9. SQL for data analysis
10. Probability, statistics, and hypothesis testing
11. Intro to Machine Learning — regression & classification
12. Model evaluation metrics
13. Git & GitHub for version control
14. Real-world case studies & project work
> ⚙️ *Note: Course structure may evolve; this list is based on currently advertised content.*
---
## 🛠️ Technologies & Tools Explored
- **Programming Languages:** 
- **Anaconda:** 
- **Data Analysis & Manipulation:** , 
- **Visualization:** Matplotlib, Seaborn 
- **Machine Learning:** Scikit-learn 
- **Environment:** Jupyter Notebook, Anaconda 
- **Version Control:** Git & GitHub , 
---
## 🚀 How to Use This Repository
1. **Clone the repo**
```bash
git clone https://github.com/sidraanl-08/completedatascience.git
## 🙏 Credits & Acknowledgments
Massive thanks to CodeWithHarry for his exceptional teaching and clear explanation!
This repository and my learning journey would not have been possible without his guidance.
🔗 Official Links
Website: https://www.codewithharry.com
YouTube: CodeWithHarry
Instagram: @codewithharry
🎓 All course content, guidance, and teaching methodology belong to CodeWithHarry.
📘 This repository is part of my personal learning journey.
Feel free to explore, fork, and learn along!
Made with ❤️ by Sidraa