Data Science using Python Programming

🧠 Module 1: Introduction to Data Science

• What is Data Science?

• Applications and Trends

• Data Science Lifecycle

• Roles: Data Scientist, Analyst, Engineer

• Tools of the Trade: Python, Jupyter, Git, etc.

🐍 Module 2: Python for Data Science

• Python Basics

  • Variables, Data Types

  • Conditionals and Loops

  • Functions and Modules

• Data Structures

  • Lists, Tuples, Dictionaries, Sets

• File Handling (CSV, TXT, JSON)

• Exception Handling

• Virtual Environments and Package Management

📊 Module 3: Data Analysis with Pandas and NumPy

• Introduction to NumPy

  • Arrays, Vectorization, Broadcasting

• Introduction to Pandas

  • Series and DataFrames

  • Indexing, Filtering, Sorting

  • GroupBy, Aggregation

  • Merging and Joining DataFrames

  • Handling Missing Data

📈 Module 4: Data Visualization

• Matplotlib Basics

• Seaborn for Statistical Plots

• Plotly for Interactive Visuals

• Customizing and Styling Plots

• Real-world Data Visualization Projects

🧹 Module 5: Data Cleaning and Preprocessing

• Dealing with Missing Values

• Data Encoding: Label, One-hot

• Normalization and Scaling

• Handling Outliers

• Feature Engineering

🧮 Module 6: Introduction to Statistics & Probability

• Descriptive Statistics

• Probability Distributions

• Inferential Statistics

• Hypothesis Testing

🧠 Module 7: Machine Learning with scikit-learn

• ML Basics: Supervised vs Unsupervised

• Model Building Pipeline

• Supervised Learning Algorithms:

  • Linear Regression

  • Logistic Regression

  • Decision Trees and Random Forests

  • K-Nearest Neighbors

• Unsupervised Learning:

  • K-Means Clustering

  • PCA (Dimensionality Reduction)

• Model Evaluation: Accuracy, Precision, Recall, F1-Score, ROC

🧪 Module 8: Project Work

• Real-world dataset analysis (e.g., Titanic, Iris, Sales data)

• End-to-End Data Science Project

• Data Collection to Prediction Deployment (optional)

🛠️ Module 9: Tools and Deployment (Optional Advanced)

• Version Control with Git

• Intro to SQL for Data Science

• APIs and Web Scraping with Python

• Model Deployment using Flask or Streamlit

• Basics of Docker (Optional)