https://github.com/yash-kavaiya/data-analytics-with-python-nptel
Repository for the NPTEL course 'Data Analytics with Python'. Includes code examples, notebooks, and exercises covering data manipulation, visualization, statistics, and machine learning with Python libraries like NumPy, Pandas, and Matplotlib.
https://github.com/yash-kavaiya/data-analytics-with-python-nptel
matplotlib numpy pandas python
Last synced: 6 months ago
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Repository for the NPTEL course 'Data Analytics with Python'. Includes code examples, notebooks, and exercises covering data manipulation, visualization, statistics, and machine learning with Python libraries like NumPy, Pandas, and Matplotlib.
- Host: GitHub
- URL: https://github.com/yash-kavaiya/data-analytics-with-python-nptel
- Owner: Yash-Kavaiya
- Created: 2022-04-08T05:56:49.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2025-03-31T09:02:21.000Z (11 months ago)
- Last Synced: 2025-03-31T10:21:59.925Z (11 months ago)
- Topics: matplotlib, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 140 KB
- Stars: 11
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 Data Analytics with Python - NPTEL Course 🐍
🎓 Course Review: 
## 📋 Course Overview
This repository contains materials for the NPTEL course "Data Analytics with Python," including code examples, assignments, and resources for statistical analysis and machine learning.
## 🗓️ Course Layout
Week
Topics
Status
📍 Week 1
Introduction To Data Analytics And Python Fundamentals
✅
📍 Week 2
Introduction To Probability
✅
📍 Week 3
Sampling And Sampling Distributions
✅
📍 Week 4
Hypothesis Testing
✅
📍 Week 5
Two Sample Testing And Introduction To ANOVA
✅
📍 Week 6
Two-Way ANOVA And Linear Regression
✅
📍 Week 7
Linear Regression And Multiple Regression
✅
📍 Week 8
Concepts Of MLE And Logistic Regression
✅
📍 Week 9
ROC And Regression Analysis Model Building
✅
📍 Week 10
C2 Test And Introduction To Cluster Analysis
✅
## 🎯 Learning Path
```mermaid
graph LR
A[Python Fundamentals] --> B[Probability]
B --> C[Sampling]
C --> D[Hypothesis Testing]
D --> E[ANOVA]
E --> F[Regression]
F --> G[Logistic Regression]
G --> H[Model Building]
H --> I[Cluster Analysis]
style A fill:#d1f0ff,stroke:#0077b6
style B fill:#d1f0ff,stroke:#0077b6
style C fill:#d1f0ff,stroke:#0077b6
style D fill:#ffe5d9,stroke:#f4a261
style E fill:#ffe5d9,stroke:#f4a261
style F fill:#e9ecef,stroke:#6c757d
style G fill:#e9ecef,stroke:#6c757d
style H fill:#e9ecef,stroke:#6c757d
style I fill:#d8f3dc,stroke:#2d6a4f
```
## 📝 Assignment Solutions (Previous Course)
Week
Solution Link
Status
📝 Week 1
✅ Available
📝 Week 2
✅ Available
📝 Week 3
✅ Available
📝 Week 4
✅ Available
📝 Week 5
✅ Available
📝 Week 6
✅ Available
📝 Week 7
✅ Available
📝 Week 8
Coming soon!
⏳ Pending
📝 Week 9
Coming soon!
⏳ Pending
📝 Week 10
✅ Available
📝 Week 11
Coming soon!
⏳ Pending
📝 Week 12
Coming soon!
⏳ Pending
### 📺 YouTube Playlist
[Complete NPTEL Course Videos](https://youtu.be/cmRkGyrcsrI)
### 👨👩👧👦 Join Our Study Group
## 📚 Week 1: Introduction To Data Analytics And Python Fundamentals
Resource
Link
🎬 Assignment solution
🧩 Practice Questions
📝 Summary of Week-1
📊 Central Tendency and Dispersion
💻 How to Run Python Notebook Online
## 🧮 Statistical Tools & References
Tool
Description
Link
📈 Z-table
Standard normal distribution table
Access Z-table
🧮 Z-score Calculator
Calculate Z-scores easily
Use Calculator
📊 T-table
Student's t-distribution table
Access T-table
🧮 T-value Calculator
Calculate T-values easily
Use Calculator
## 📌 Key Topics Covered
Arrays & Matrix Operations
Data Manipulation
Data Visualization
Hypothesis Testing
💡 Course Tip: Focus on understanding statistical concepts first, then learn how to implement them in Python. The combination of theoretical knowledge and practical coding skills will make you proficient in data analytics.
## 🙏 Credits
---
⭐ Star this repository if you find it helpful! ⭐
📚 Happy Learning and Data Analysis! 📊