This course further guides students through the end-to-end lifecycle of building data-driven solutions - from setting up environments to packaging final deliverables for real-world use.

Lectures-Tutorials-Practicals-Credits: 3-0-0-3

Week 1: Setup - Setting up development environments, managing dependencies, and configuring tools required to build reliable data workflows and applications.

Week 2: Deploy - Deploying models and applications to production environments, ensuring scalability, accessibility, and smooth integration with real-world systems.

Week 3: Source - Identifying and collecting relevant data from diverse sources such as APIs, web scraping, databases, and external repositories.

Week 4: Wrangle - Cleaning, transforming, and structuring raw data into usable formats, handling missing values, inconsistencies, and data quality issues.

Week 5: Analyse - Applying statistical and computational techniques to extract meaningful patterns, trends, and insights from data.

Week 6: Test - Validating models, pipelines, and outputs through systematic testing to ensure correctness, robustness, and reliability.

Week 7: Present - Communicating insights effectively using narratives, dashboards, and visualizations tailored to different stakeholders.

Week 8: Package - Packaging models, code, and workflows into reusable, maintainable, and deployable units for production and collaboration.