https://github.com/rahul-404/full_stack_data_science_masters
Welcome to the repository for the course "Full Stack Data Science Masters". This repository is designed to accompany the course and provide resources, exercises, and projects related to the study of data science techniques.
https://github.com/rahul-404/full_stack_data_science_masters
computer-vision data-science database deep-learning exploratory-data-analysis flask machine-learning natural-language-processing numpy pandas python statistics time-series visualization
Last synced: about 2 months ago
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Welcome to the repository for the course "Full Stack Data Science Masters". This repository is designed to accompany the course and provide resources, exercises, and projects related to the study of data science techniques.
- Host: GitHub
- URL: https://github.com/rahul-404/full_stack_data_science_masters
- Owner: Rahul-404
- Created: 2024-07-21T14:16:43.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-12-21T13:35:27.000Z (5 months ago)
- Last Synced: 2025-02-01T22:13:56.014Z (4 months ago)
- Topics: computer-vision, data-science, database, deep-learning, exploratory-data-analysis, flask, machine-learning, natural-language-processing, numpy, pandas, python, statistics, time-series, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 21.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Full Stack Data Science Masters
Welcome to the repository for the course "Full Stack Data Science Masters". This repository is designed to accompany the course and provide resources, exercises, and projects related to the study of data science techniques.
## Course Curriculum
This course covers a comprehensive range of topics essential for mastering full stack data science. Below is an outline of the curriculum:
1. **Python**
- Basics of Python programming language
- Python projects and applications
2. **Data Manipulation and Analysis**
- Pandas for data manipulation
- NumPy for numerical computations
3. **Data Visualization**
- Visualization libraries (e.g., Matplotlib, Seaborn)
4. **Databases**
- SQL fundamentals
- NoSQL with MongoDB
5. **Statistics**
- Basic statistics
- Advanced statistics
6. **Feature Engineering**
- Techniques for feature extraction and selection
7. **Exploratory Data Analysis (EDA)**
- Methods for data exploration and insights
8. **Machine Learning**
- Introduction to machine learning concepts
- Supervised learning algorithms
- Unsupervised learning techniques
- Time series analysis
9. **Natural Language Processing (NLP)**
- NLP fundamentals for machine learning applications
10. **End-to-End Machine Learning Projects**
- Integration of data preprocessing, model building, and deployment
11. **Interview Preparation**
- Tips and resources for preparing for data science and ML interviews
12. **Deep Learning**
- Introduction to deep learning concepts
- Deep learning for computer vision
- Deep learning for NLP
13. **PowerBI**
- Introduction to Power BI for data visualization and analytics
## Repository Structure
- **Lectures**: Contains lecture notes, slides, and supplementary materials.
- **Exercises**: Hands-on exercises and assignments to reinforce learning.
- **Projects**: Capstone projects and real-world applications using data science techniques.
- **Resources**: Additional resources, references, and links for further exploration.
## Getting Started
To get started with the course, clone this repository to your local machine:
```bash
git clone https://github.com/Rahul-404/Full_Stack_Data_Science_Masters.git
```
Make sure to install any necessary dependencies outlined in the course materials and follow along with the provided exercises and projects.
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvement, please submit an issue or a pull request. For major changes, please open an issue first to discuss what you would like to change.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Mention any acknowledgments or credits to individuals or organizations whose work or tools you are using in this course.