Building a Scalable Data Architecture for Predictive Modeling and Interactive Business Analytics.

This session will explore how to design and implement a modern data architecture that supports predictive modeling and interactive business analytics. Attendees will learn about integrating SQL, Python, and Tableau to create a scalable solution that enables dynamic “what-if” analysis for business users on dashboard powered in the backend by a machine learning model.

Key Takeaways

• Learn how to architect a scalable data environment using SQL, Python, and Tableau for business analytics.
• Explore the integration of predictive models into interactive dashboards, enabling real-time “what-if” analysis.
• Understand the end-to-end data pipeline from extraction to analysis, and how it supports business decision-making.

————————————————————————————————————————————————————

Bio

Shilpa Leo – Senior Lead Instructor | General Assembly| Singapore

Shilpa Sindhe is an experienced professional in Business Intelligence, Data Science, and Data/HR Analytics, with a proven track record in digital transformation and automation.

With over a decade of experience as a data practitioner at companies like NUHS, General Assembly, EngageRocket, GovTech, and Micron Technology, Shilpa has led key initiatives in predictive modeling, AI-driven automation, and data visualization. She is skilled in Python, SQL, Tableau, Power BI, amongst others to deliver impactful data solutions in business environments.

An ACLP-certified educator, Shilpa has also trained, and mentored several mid-career professionals, helping them transition into tech careers through data science, coding, and analytics training.