Software engineer 2 at iqvia , currently pursuing data science maasters teaching programming

My Teaching Approach: Bridging Engineering & Data Science
Philosophy & Lesson Dynamics
My lessons completely strip away dry, textbook-only theory. Having spent over five years in the industry shipping production-grade Java applications—and now deep-diving into Data Science—I treat my students like junior colleagues. Our sessions are highly interactive, hands-on, and conversational. We do not just write code that runs; we write code that is clean, optimized, and built to survive in a real tech ecosystem.

Real-Time Industry Examples
The core of my methodology relies on real-world engineering realities. Instead of using generic, abstract puzzles, I pull scenarios straight from live software and data environments:

Data Structures: We do not just manipulate random arrays; we simulate processing incoming server telemetry or handling messy system logs.

Object-Oriented Programming: I show you how backend architecture feeds directly into analytical pipelines, emphasizing why clean-code habits matter when multiple cross-functional teams interact with a codebase.

Data Prep: We tackle data shapes that mimic real enterprise structures—dealing with missing fields, mismatched types, and edge cases that developers face daily.

Content & Experience
I bridge the gap between heavy software development and intelligent data utilization. My curriculum covers core software foundations, robust debugging, Git version control, and data engineering pipelines.

With five years of industry experience, I know exactly what technical teams look for. My current transition into Data Science allows me to offer a rare, dual perspective: teaching you how to write great code, and showing you how to engineer data models so they successfully deploy into production.