📘 Edureka Data Science DIY Projects

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🚀 Project Overview

This repository is a comprehensive collection of hands-on DIY projects completed as part of the Edureka Data Science Master Program. It covers the entire data science learning journey with practical assignments and projects designed to build skills from Python basics to advanced deep learning and data visualization.

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📚 About the Course

• Course Provider: Edureka
• Course Duration: Approximately 100 days of guided learning with daily DIY projects
• Focus Areas:
  • Python Programming
  • Probability and Statistics
  • Supervised and Unsupervised Machine Learning
  • Natural Language Processing (NLP)
  • Deep Learning (including Reinforcement Learning)
  • Model Evaluation and Optimization
  • Data Visualization and Tableau
  • SQL for data handling and querying

The course is structured to build your skills progressively through day-wise DIY projects, which are documented and organized in this repository.

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📂 Repository Structure

The repo is organized by topic and day-wise folders to align with the course timeline for easier navigation:

edureka-data-science-diy/

│

├── 01_Python_Basics/

│ ├── Day01/

│ │ ├── Day01_DIY.ipynb

│ │ └── dataset.csv

│ ├── Day02/

│ └── ...

│

├── 02_Probability_Statistics/

│ ├── Day19/

│ └── ...

│ ├── Day30/

├── 03_Supervised_Learning/

│

├── 04_NLP/

│

├── 05_Unsupervised_Learning/

│

├── 06_Model_Evaluation & Optimization/

│

├── 07_Deep_Learning/

│

└── 08_Data_Visualization_Tableau/

• Each DayXX folder contains:
  • Jupyter Notebook file (DayXX_DIY.ipynb) with detailed solutions
  • Corresponding dataset (dataset.csv) or text files used in the project

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🗓️ Learning Timeline and Topics Covered

Days	Topics Covered
Day 01–18	Python Basics
Day 19–30	Probability & Statistics
Day 32–43	Supervised Machine Learning
Day 45–48	Natural Language Processing (NLP)
Day 50–61	Unsupervised Machine Learning
Day 63–73	Model Evaluation & Optimization
Day 75–84	Deep Learning (including Reinforcement Learning)
Day 86–96	Data Visualization & Tableau

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🧰 Tools and Technologies

• Languages: Python
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, NLTK, SpaCy
• Visualization: Matplotlib, Seaborn, Tableau
• Others: Jupyter Notebook, VS Code, SQL (basic queries and data handling)

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👩‍💻 About the Author

Sravya Togarla
Aspiring Data Scientist | Data Analytics Enthusiast

• Passionate about transforming raw data into actionable insights
• Continuously learning and building hands-on projects
• Connect with me on LinkedIn: (https://www.linkedin.com/in/sravya-togarla)
• Check out my GitHub: https://github.com/Sravyatogarla

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📖 How to Use This Repository

1. Clone the repository:

   git clone https://github.com/YOUR_USERNAME/edureka-data-science-diy.git
   

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📈 Why This Repository? Structured day-wise learning aligned with a recognized data science course

Real hands-on practice with datasets and problem-solving

Covers all key topics from beginner to advanced levels

Ideal for learners who want a guided DIY project-based approach to mastering data science

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📢 Additional Information Datasets included are samples for learning purposes. For large or sensitive data, please refer to original course material.

This repository is intended for personal learning and portfolio showcase.

Feel free to fork and customize your own learning version!

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

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🚀 100 Days of Data Science DIY Projects – My journey through the Edureka Master Program 🔍📊🤖