Learning Outcomes:

• Clean, analyze, and visualize data using Pandas and visualization libraries
• Perform basic SQL queries on structured data

Topics Covered:

• Week 5:
  • Data Cleaning Techniques (Handling missing data, duplicates)
  • String Operations, Date/Time Handling in Pandas
• Week 6:
  • Data Visualization with Matplotlib & Seaborn
  • Bar Charts, Histograms, Line Plots, Heatmaps
• Week 7:
  • Introduction to SQL: SELECT, WHERE, JOIN, GROUP BY
  • Hands-on SQL Practice with Real Datasets
• Week 8:
  • Integrating SQL with Python (SQLite / MySQL)
  • Querying databases from Python scripts

Mini Project: Clean and visualize a dataset (e.g., COVID-19 data or sales data)

Learning Outcomes:

• Understand machine learning concepts
• Apply basic algorithms using Scikit-Learn
• Complete a mini data science project

Topics Covered:

• Week 9:
  • What is Machine Learning? Types: Supervised vs Unsupervised
  • Intro to Scikit-Learn: Dataset Splitting, Model Training
• Week 10:
  • Linear Regression, K-Nearest Neighbors
  • Model Evaluation: Accuracy, Confusion Matrix
• Week 11:
  • Data Preprocessing for ML (Scaling, Encoding)
  • Real-world model deployment examples
• Week 12:
  • Capstone Project Presentation & Review
  • Build a complete pipeline: From data loading → cleaning → ML model → visualization

💡 Capstone Project Examples:

• Predict student performance based on study habits
• Sales forecasting from historical data
• Heart disease prediction from patient data