📘 Data Science Lecture Notes

Overview

This repository contains data science lecture notes curated from classes that I have taught to students at an academy. This repository contains curated data science notes compiled over time while exploring, explaining, and organizing concepts during a series of learning sessions. The material reflects how topics were broken down, discussed, and refined through real conversations and problem-solving.

I’ve put this together primarily as:

A way to consolidate my own understanding
A personal learning project
A resource that might be useful to anyone learning data science, just as it has been useful for me

🎯 Purpose of This Repository

To capture data science concepts in a clear, structured way
To turn scattered learning into reusable notes
To document explanations that worked well while reasoning through topics
To create a reference that supports continuous learning and revision

This repository is meant to be practical and approachable, not a formal course or academic reference.

📚 Content Philosophy

This is a living document
Content is continuously updated, refined, and reorganized
Explanations evolve as understanding improves
Some topics are brief, others more detailed — depending on what felt useful to document

The focus is on learning by clarity, not completeness.

🔍 Sources & Attribution

The content here is not purely original.

It has been:

Curated from multiple sources including documentation, articles, tutorials, and notes
Shaped by hands-on exploration and discussion
Enhanced using AI-assisted tools (including ChatGPT) for explanations, structure, and clarity

This repository represents a curation and synthesis effort, not ownership of all ideas presented.

🤝 Open & Collaborative

This is intended to be an open, collaborative learning repository.

If you’d like to:

Improve explanations
Fix errors or inconsistencies
Add examples or references
Expand on existing topics

👉 Feel free to open a Pull Request (PR).

All constructive contributions are welcome.

👥 Who Might Find This Useful?

Anyone learning data science
Self-learners who prefer structured notes
Practitioners revisiting fundamentals
Learners who like concept-first explanations

⚠️ Disclaimer

These notes reflect learning in progress
Some explanations may be simplified
Always cross-check with official or authoritative sources for production or research use

🌱 Final Note

Learning works best when ideas are revisited, questioned, and shared. This repository exists as a space to do exactly that — for personal growth and, hopefully, for others as well.

Happy learning 🚀

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📘 Data Science Lecture Notes

Overview

🎯 Purpose of This Repository

📚 Content Philosophy

🔍 Sources & Attribution

🤝 Open & Collaborative

👥 Who Might Find This Useful?

⚠️ Disclaimer

🌱 Final Note

About

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Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
.vscode		.vscode
data		data
0.01.Python Basics.ipynb		0.01.Python Basics.ipynb
0.02.Python Essentials.ipynb		0.02.Python Essentials.ipynb
0.03.Python Lists.ipynb		0.03.Python Lists.ipynb
0.04.Python Fundamentals - Dict, Set, Functions.ipynb		0.04.Python Fundamentals - Dict, Set, Functions.ipynb
0.05.Python Exception Handling.ipynb		0.05.Python Exception Handling.ipynb
0.06.Python Exception Tutorial.ipynb		0.06.Python Exception Tutorial.ipynb
0.07.Python Advanced - Default Dict and Named Tuple.ipynb		0.07.Python Advanced - Default Dict and Named Tuple.ipynb
0.08.Python Debugging Tutorials.ipynb		0.08.Python Debugging Tutorials.ipynb
0.09.Python Regular Expression.ipynb		0.09.Python Regular Expression.ipynb
0.10.Python Classes.ipynb		0.10.Python Classes.ipynb
1.01.Data - Overview.ipynb		1.01.Data - Overview.ipynb
1.02.Data - Manipulation using Python.ipynb		1.02.Data - Manipulation using Python.ipynb
2.0.00.Simple Data Science Demo.ipynb		2.0.00.Simple Data Science Demo.ipynb
2.0.01.Pattern Recognition.ipynb		2.0.01.Pattern Recognition.ipynb
2.0.02.Parametric vs Non Parametric Models.ipynb		2.0.02.Parametric vs Non Parametric Models.ipynb
2.1.00.Simple Linear Regression - Synthetic Data.ipynb		2.1.00.Simple Linear Regression - Synthetic Data.ipynb
2.1.01.Simple Linear Regression.ipynb		2.1.01.Simple Linear Regression.ipynb
2.1.02.Multiple Linear Regression.ipynb		2.1.02.Multiple Linear Regression.ipynb
2.1.03.Polynomial Regression.ipynb		2.1.03.Polynomial Regression.ipynb
2.1.04.Introduction to Gradient Descent.ipynb		2.1.04.Introduction to Gradient Descent.ipynb
2.2.00.Logistic Regression.ipynb		2.2.00.Logistic Regression.ipynb
2.3.00.Beyond Linearity.ipynb		2.3.00.Beyond Linearity.ipynb
2.3.01.Multilevel Models.ipynb		2.3.01.Multilevel Models.ipynb
2.3.02.Naive Bayes Classifier.ipynb		2.3.02.Naive Bayes Classifier.ipynb
2.4.00.Decision Trees.ipynb		2.4.00.Decision Trees.ipynb
2.5.00.Ensemble Learning.ipynb		2.5.00.Ensemble Learning.ipynb
2.5.01.Bagging Technique.ipynb		2.5.01.Bagging Technique.ipynb
2.5.02.Customer Churn using Bagging(Ex).ipynb		2.5.02.Customer Churn using Bagging(Ex).ipynb
2.5.03.Boosting Technique.ipynb		2.5.03.Boosting Technique.ipynb
2.5.04.Boosting Technique - XGBoost.ipynb		2.5.04.Boosting Technique - XGBoost.ipynb
2.5.05.Boosting Technique - Boosting Models Comparison.ipynb		2.5.05.Boosting Technique - Boosting Models Comparison.ipynb
2.5.06.Stacking Technique.ipynb		2.5.06.Stacking Technique.ipynb
2.6.00.KNN - K-Nearest Neighbor.ipynb		2.6.00.KNN - K-Nearest Neighbor.ipynb
2.7.00.Support Vector Machines.ipynb		2.7.00.Support Vector Machines.ipynb
3.0.00.Unsupervised Machine Learning.ipynb		3.0.00.Unsupervised Machine Learning.ipynb
3.0.01.Clustering Techinques.ipynb		3.0.01.Clustering Techinques.ipynb
3.0.02.Dimensionality Reduction - PCA, LDA.ipynb		3.0.02.Dimensionality Reduction - PCA, LDA.ipynb
4.0.00.Time Series Analysis.ipynb		4.0.00.Time Series Analysis.ipynb
4.0.01.Time Series Analysis - SML vs UML.ipynb		4.0.01.Time Series Analysis - SML vs UML.ipynb
4.0.02.Time Series Analysis - Sales Data.ipynb		4.0.02.Time Series Analysis - Sales Data.ipynb
4.0.03.Time Series Analysis - Sales Data - Adv.ipynb		4.0.03.Time Series Analysis - Sales Data - Adv.ipynb
4.0.04.Time Series Analysis - NTK.ipynb		4.0.04.Time Series Analysis - NTK.ipynb
5.0.00.Neural Network Basics.ipynb		5.0.00.Neural Network Basics.ipynb
5.0.01.Neural Network - Mathematical Explanation.ipynb		5.0.01.Neural Network - Mathematical Explanation.ipynb
5.1.00.Introduction to Deep Learning.ipynb		5.1.00.Introduction to Deep Learning.ipynb
5.1.01.Deep Neural Networks.ipynb		5.1.01.Deep Neural Networks.ipynb
5.2.00.Deep Learning in Computer Vision.ipynb		5.2.00.Deep Learning in Computer Vision.ipynb
5.2.01.Convolutional Neural Networks.ipynb		5.2.01.Convolutional Neural Networks.ipynb
5.2.02.CNN for CIFAR10.ipynb		5.2.02.CNN for CIFAR10.ipynb
5.2.03.Evolution of CNN architecture.ipynb		5.2.03.Evolution of CNN architecture.ipynb
5.2.04.Visualizing and Understanding CNNs.ipynb		5.2.04.Visualizing and Understanding CNNs.ipynb
5.2.05.CNNs for Recognition, Detection, and Segmentation.ipynb		5.2.05.CNNs for Recognition, Detection, and Segmentation.ipynb
5.2.06.Recent Trends in Computer Vision.ipynb		5.2.06.Recent Trends in Computer Vision.ipynb
5.2.07.CNN + RNN Models for Video Understanding.ipynb		5.2.07.CNN + RNN Models for Video Understanding.ipynb
5.2.08.Attention Models in Computer Vision.ipynb		5.2.08.Attention Models in Computer Vision.ipynb
5.2.09.Variants and Applications of Generative Models in Vision.ipynb		5.2.09.Variants and Applications of Generative Models in Vision.ipynb
5.2.10.VGG16 convolutional neural network (CNN).ipynb		5.2.10.VGG16 convolutional neural network (CNN).ipynb
5.3.00.Natural Language Processing (NLP) - Part 1.ipynb		5.3.00.Natural Language Processing (NLP) - Part 1.ipynb
5.3.01.Natural Language Processing (NLP) - Part 2.ipynb		5.3.01.Natural Language Processing (NLP) - Part 2.ipynb
5.3.02.NLP - Techniques and Algorithms.ipynb		5.3.02.NLP - Techniques and Algorithms.ipynb
5.3.04.NLP - Sentiment Analysis and Opinion Mining.ipynb		5.3.04.NLP - Sentiment Analysis and Opinion Mining.ipynb
5.3.05.NLP - Emotion Detection.ipynb		5.3.05.NLP - Emotion Detection.ipynb
5.3.06.NLP - Case Studies and Practical Applications.ipynb		5.3.06.NLP - Case Studies and Practical Applications.ipynb
5.4.00.Sequence Data - ANN vs RNN.ipynb		5.4.00.Sequence Data - ANN vs RNN.ipynb
5.4.01.Recurrent Neural Networks.ipynb		5.4.01.Recurrent Neural Networks.ipynb
5.5.00.Long Short-Term Memory.ipynb		5.5.00.Long Short-Term Memory.ipynb
5.6.00.Attention Models.ipynb		5.6.00.Attention Models.ipynb
5.7.00.Deep Generative Models.ipynb		5.7.00.Deep Generative Models.ipynb
5.7.01.Recent Trends in Deep Generative Models.ipynb		5.7.01.Recent Trends in Deep Generative Models.ipynb
6.0.00.Reinforcement Learning Algorithm.ipynb		6.0.00.Reinforcement Learning Algorithm.ipynb
7.0.00.Recommendation Systems.ipynb		7.0.00.Recommendation Systems.ipynb
8.0.00.Understanding Hypothesis Testing.ipynb		8.0.00.Understanding Hypothesis Testing.ipynb
Interview Prep Guide.ipynb		Interview Prep Guide.ipynb
LICENSE		LICENSE
PCA - Clustering.ipynb		PCA - Clustering.ipynb
README.md		README.md

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📘 Data Science Lecture Notes

Overview

🎯 Purpose of This Repository

📚 Content Philosophy

🔍 Sources & Attribution

🤝 Open & Collaborative

👥 Who Might Find This Useful?

⚠️ Disclaimer

🌱 Final Note

About

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