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.
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.
✔ 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
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
- EDA introduction
- Types of analysis (Univariate, Bivariate, Multivariate)
- Missing values, outliers
- Normal & skewed distributions
- Skewness & kurtosis
👉 Notebook: /notebooks/Unit1_Fundamentals.ipynb
- Pandas & NumPy techniques
- Automated EDA tools
- Regression basics
- Covariance & correlation
- Machine Learning introduction
👉 Notebook: /notebooks/Unit2_Automated_EDA.ipynb
- Classification vs Regression
- Dataset splitting
- Overfitting & Underfitting
- Evaluation metrics: MSE, MAE, R²
👉 Notebook: /notebooks/Unit3_Supervised_Learning.ipynb
- Logical reasoning with Vedic Math
- 16 Sutras implemented in Python/C
- Fast numeric techniques
- Algorithmic thought development
👉 Notebook: /notebooks/Unit4_Vedic_Math_Sutras.ipynb
Every notebook in this repository is Colab-ready.
Use this badge template:
[](
https://colab.research.google.com/github/sbccas/data-analytics-using-python/blob/main/notebooks/<NOTEBOOK_NAME>.ipynb)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.
- StudentsPerformance dataset
- Iris dataset
- House Prices dataset
- Small Retail Sales dataset
- Attendance / Marks dataset
Datasets are located in /datasets/.
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!
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.
If you find this repository useful:
- ⭐ Star this repo
- 🗣 Share with classmates
- 📝 Open issues for feedback
- 🤝 Contribute with notebooks/datasets
This repository is intended for educational and academic use. All materials are freely available for students and faculty for learning purposes.