NPTEL-Python-for-Data-Science-Course

This repository contains my complete documentation and practice work for the NPTEL Python for Data Science course.
All course content from Week 0 to Week 4 is organized clearly for learning, revision, and future reference.

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🟢 Week 0: Prerequisite

• Python setup guide
• Prerequisite Assignment for practice

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🟢 Week 1: Python Basics & Spyder

• Introduction to Spyder IDE
• Setting Working Directory
• Creating and saving script files
• File execution & console management
• Clearing variables and environment
• Commenting script files
• Variable creation
• Arithmetic & logical operators
• Data types and their operations

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🟢 Week 2: Sequence Data Types & NumPy

Sequence Data Types

• Strings
• Lists
• Arrays
• Tuples
• Dictionary
• Sets
• Range

NumPy

• Introduction to ndArray
• Basic NumPy operations

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🟢 Week 3: Pandas, EDA & Visualization

Pandas & Dataset

• Pandas DataFrame operations
• Reading files
• Toyota Corolla dataset
• Exploratory Data Analysis (EDA)
• Data preparation & preprocessing

Data Visualization (Matplotlib & Seaborn)

• Scatter plot
• Line plot
• Bar plot
• Histogram
• Box plot
• Pair plot

Control Structures & Functions

• if–else family
• for loop
• for loop with if & break
• while loop
• Functions

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🟢 Week 4: Case Study

Regression

• Predicting price of pre-owned cars

Classification

• Classifying personal income

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🎯 Purpose of This Repository

• Course documentation
• Concept revision
• Practice reference
• Useful for exams, interviews, and projects

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🛠 Tools & Libraries Used

• Python
• Spyder IDE/ Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn

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📌 Note

This repository is created purely for learning and documentation purposes as part of the NPTEL course.

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