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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.

🟢 Week 0: Prerequisite

  • Python setup guide
  • Prerequisite Assignment for practice

🟢 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

🟢 Week 2: Sequence Data Types & NumPy

Sequence Data Types

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

NumPy

  • Introduction to ndArray
  • Basic NumPy operations

🟢 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

🟢 Week 4: Case Study

Regression

  • Predicting price of pre-owned cars

Classification

  • Classifying personal income

🎯 Purpose of This Repository

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

🛠 Tools & Libraries Used

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

📌 Note

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