Essentials of Data Science Tutorial

This is a comprehensive 5-day course covering essentials in data science as of 2025, from basic Python programming to deploying machine learning applications. The course emphasizes practical applications over theoretical concepts, focusing on working with real-world datasets.

Course Overview

• 🐍 Python fundamentals and data manipulation
• 🔍 Regression analysis and classification techniques
• 🤖 Clustering and neural networks
• 📝 Natural Language Processing (NLP) and transformers
• 🖼️ Computer Vision and model deployment

Prerequisites

Before starting the course, ensure you have:

• 🐍 Basic programming knowledge
• 📦 Git installed on your system
• ☁️ Google Colab account (free)

Getting Started

1. Clone the repository:

git clone https://github.com/adi2907/essentials-of-data-science
cd essentials-of-data-science

Copy to Google colab and run. We are using the standard libraries which are available off the shelf in Colab

OR set up your own environment and run these Jupyter notebooks. The pip installs are there in the code lines itself

3. Run the animations:

cd animations
npm run dev

Course Structure

Day 0: Optional Coding Basics

• 🔰 Python programming fundamentals
• 💻 Basic data structures and algorithms
• 🔄 Control flow and functions

Day 1: Python Data Science Libraries

• 🐼 Pandas for data manipulation
• 🔢 NumPy for numerical computing
• 📊 Python data analysis techniques

Day 2: Regression and Classification

• 📈 Linear regression fundamentals
• 🎯 Classification techniques:
  • Logistic regression
  • Decision trees
  • Random forests

Day 3: Advanced Machine Learning

• 📊 Clustering techniques:
  • K-means clustering
  • Hierarchical clustering
• 🧠 Neural Networks:
  • Introduction to neural networks
  • Building simple neural network models

Day 4: Natural Language Processing

• 📝 NLP fundamentals using Spacy:
  • Sentiment analysis
  • Parts of Speech (POS) tagging
  • Named Entity Recognition (NER)
• 🔤 Vector embeddings and arithmetic
• 🤖 Transformers:
  • Basic concepts
  • Question-answering
  • Language generation
  • Text summarization

Day 5: Computer Vision and Deployment

• 🖼️ Computer Vision:
  • Convolutional Neural Networks (CNN)
  • Training on CIFAR dataset
  • OpenCV basics
  • Document scanning
• 🚀 Model Deployment:
  • FastAPI implementation
  • Streamlit web applications

Learning Materials

The repository includes:

• 📚 Detailed Jupyter notebooks for each topic
• 🎬 Interactive animations for complex concepts
• 📊 Real-world datasets for practice

Contributing

If you find any issues or have suggestions for improvements:

1. 🍴 Fork the repository
2. 🌿 Create a new branch
3. 📝 Make your changes
4. 🚀 Submit a pull request

Note: This course is designed to be hands-on and practical, with emphasis on real-world applications rather than theoretical concepts.

• Consider starring it if you find it useful for greater reach