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📘 Data Analytics Using Python

TYBCA – Course Code 602

A complete interactive learning repository for students of Data Analytics & Python

This course aims to introduce students to data analytics techniques using Python, with a focus on Exploratory Data Analysis (EDA), regression, and supervised learning. It equips learners with practical skills in handling data, automating EDA, and applying machine learning concepts in real-world scenarios.

🌟 Overview

Welcome to the official repository for the TYBCA – Data Analytics Using Python course under VNSGU. This repository is designed to provide students with:

  • Interactive Google Colab notebooks
  • High-quality teaching materials
  • Practical assignments & lab exercises
  • Real-world datasets
  • Step-by-step EDA & Machine Learning basics
  • Student-friendly explanations + hands-on examples

This course emphasizes learning-by-doing, enabling students to explore data, visualize patterns, clean datasets, and understand foundational ML concepts.

🚀 Features of This Repository

✔ Well-structured unit-wise content ✔ Colab-ready notebooks with “Open in Colab” support ✔ Beginner-friendly explanations & visualizations ✔ Assignments + practice tasks for each unit ✔ Real datasets for hands-on learning ✔ Mini-project templates for student submissions ✔ Vedic Mathematics Sutra implementations (Unit 4) ✔ Continuously updated with new notebooks and improvements

📂 Repository Structure

data-analytics-using-python/
│
├── 1_Syllabus/
│   ├── 602_Data_Analytics_using_Python.pdf         # official syllabus (uploaded)
│   
│   
│
├── 2_Lecture_Notes/
│   ├── Unit1_Fundamentals
│   ├── Unit2_Automated_EDA/
│   ├── Unit3_Supervised_Learning/
│   └── Unit4_Vedic_Math_Sutras/
│
├── 3_Projects_Presentations/
│   ├── Mini_Project_Template.ipynb
│   ├── Student_Submissions/          # (one folder per student/group or zipped uploads)
│   └── Project_Evaluation_Rubric.md
│
├── 4_Assignments/
│   ├── Unit1_Assignment/
│   ├── Unit2_Assignment/
│   └── Unit3_Assignment/
│
├── 5_QuestionBank/
│   ├── Unit1_MCQ.md
│   ├── Unit1_Short_Long_Questions.md
│   └── Practical_Exam_Questions.md
│
├── 6_eBooks_ExtraResources/
│   ├── Reema_Thareja_Python_for_Data_Analysis.pdf   # if allowed by license / links
│   ├── References.md                                # canonical reading list + links
│   └── Tutorials/                                   # curated external links
│
├── 7_Previous_Year_Papers/
│
├── resources/
│   ├── datasets/
│   │   ├── students_performance.csv
│   │   ├── iris.csv
│   │   └── house_prices.csv
│   ├── notebooks/
│   │   ├── notebooks_list.md        # index of notebooks + "Open in Colab" links
│   │   ├── Unit1_Fundamentals.ipynb
│   │   ├── Unit1_Student_Workbook.ipynb
│   │   └── Unit2_Automated_EDA.ipynb
│   ├── assets/
│   │   ├── github_banner.png
│   │   └── logos/
│   └── data_dictionary.md
│
├── README.md
├── LICENSE
└── .gitignore

📘 Course Units

📍 Unit 1 – Fundamentals of Data Analytics

  • EDA introduction
  • Types of analysis (Univariate, Bivariate, Multivariate)
  • Missing values, outliers
  • Normal & skewed distributions
  • Skewness & kurtosis

👉 Notebook: /notebooks/Unit1_Fundamentals.ipynb

📍 Unit 2 – Automated EDA & Regression

  • Pandas & NumPy techniques
  • Automated EDA tools
  • Regression basics
  • Covariance & correlation
  • Machine Learning introduction

👉 Notebook: /notebooks/Unit2_Automated_EDA.ipynb

📍 Unit 3 – Supervised Learning

  • Classification vs Regression
  • Dataset splitting
  • Overfitting & Underfitting
  • Evaluation metrics: MSE, MAE, R²

👉 Notebook: /notebooks/Unit3_Supervised_Learning.ipynb

📍 Unit 4 – Vedic Mathematics Sutras

  • Logical reasoning with Vedic Math
  • 16 Sutras implemented in Python/C
  • Fast numeric techniques
  • Algorithmic thought development

👉 Notebook: /notebooks/Unit4_Vedic_Math_Sutras.ipynb

🔗 Open Notebooks in Google Colab

Every notebook in this repository is Colab-ready.

Use this badge template:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](
https://colab.research.google.com/github/sbccas/data-analytics-using-python/blob/main/notebooks/<NOTEBOOK_NAME>.ipynb)

🧠 Assignments, Labs & Projects

This repository includes:

  • 📝 Unit-wise Assignments
  • 🧪 Lab exercises
  • 📊 Practice datasets
  • 🚀 Mini-project templates
  • 🎯 Final capstone project outline

Students can open all tasks directly in Google Colab.

📊 Recommended Datasets (Included or Suggested)

  • StudentsPerformance dataset
  • Iris dataset
  • House Prices dataset
  • Small Retail Sales dataset
  • Attendance / Marks dataset

Datasets are located in /datasets/.

🤝 Contributing

Students and educators are welcome to contribute by:

  • Adding new datasets
  • Improving notebook content
  • Creating examples & visualizations
  • Submitting beginner-level ML notebooks
  • Reporting issues or suggesting improvements

Pull requests are encouraged!

👨‍🏫 Maintained By

Hitech Educator & IT Professional Expert in Python, Data Analytics, C Programming, .NET, and teaching under VNSGU for over two decades. Passionate about helping students learn through interactive examples and hands-on exploration.

⭐ Support & Feedback

If you find this repository useful:

  • ⭐ Star this repo
  • 🗣 Share with classmates
  • 📝 Open issues for feedback
  • 🤝 Contribute with notebooks/datasets

📢 License

This repository is intended for educational and academic use. All materials are freely available for students and faculty for learning purposes.