Why I Chose (and Stayed in) ISYE
Entering college, I wasn’t sure which engineering path to pursue. Industrial & Systems Engineering felt like the most versatile foundation — a bridge between business, math, and computing with grads going into consulting, finance, tech, data, and beyond. Midway through, I discovered software engineering and systems. The creativity, rigor, and problem-solving unlocked by software made the career path click for me. If you’re a recruiter reading this, you might be wondering: why hire an Industrial & Systems Engineer for a software role?
Even after that epiphany, I stayed in ISYE intentionally. Why? Because ISYE trains you to optimize complex systems with data, probability, and optimization — the same mental models that power great software and modern AI. This page connects the dots between my ISYE background and my goal to build software systems, especially in machine learning and AI.
What ISYE Builds in a Software Engineer
Industrial & Systems Engineering focuses on designing and improving complex processes and organizations using data, mathematics, and technology. The degree blends business intuition, rigorous probability/statistics, optimization, and computing. I pursued the Analytics & Data Science concentration and paired it with a CS minor, giving me a balanced mix of ISYE and CS coursework.
On the computing side, my ISYE curriculum emphasized data modeling, databases, and building systems to solve real problems. The deeper unlock came from the math: probability and statistics inform the foundations of modern AI, while optimization (linear models and programming) connects directly to ML training and decision-making. That overlap made topics like Bayesian inference and model optimization feel natural when I built AI/ML projects.
Beyond tools, software engineering is about structured problem-solving under pressure and collaborating to ship reliable systems. ISYE reinforced that mindset: define the problem precisely, model the system, measure outcomes, iterate, and optimize.
Beyond the Classroom: IE in Practice
Outside coursework, I’ve applied IE thinking in team settings — scoping ambiguous problems, aligning on metrics, designing processes, and building data-driven solutions. These experiences translate directly to software: requirements gathering, system design, iteration, and delivery.
The throughline is simple: ISYE trained me to think in systems. Software engineering lets me build them.
Coursework That Fuels My SWE Path
A snapshot of classes that shaped how I think about systems, data, and building software — shown as nodes with the takeaway that mattered most to me.
ISYE 2027 — Probability with Applications
Built a strong foundation in probability: axioms, set theory, discrete/continuous distributions, Bayesian reasoning, and key results like the Central Limit Theorem and Markov’s inequality. This class sharpened intuition for uncertainty — essential for ML and reliable systems.
CS 2316 — Data Input and Manipulation (for ISYE)
Hands-on Python for industry workflows: cleaning, transforming, and analyzing messy datasets; building practical scripts and reports. Projects emphasized end-to-end data work — from ingestion to insights.
ISYE 3030 — Statistical Methods
Core statistical tools for decision-making: sampling distributions, hypothesis testing, and simple linear regression. Gave me the quantitative backbone to understand and justify ML modeling choices.
ISYE 3232 — Stochastic Manufacturing and Service Systems
Applied probability to real systems: newsvendor decisions, cost minimization, and profit maximization; then deepened with Markov chains and their applications. Connected these ideas to CS problems and even interview prep (dynamic programming intuition via state modeling).
ISYE 3133 — Optimization
The crown jewel: modeling complex problems as linear and integer programs, solving with methods like Simplex and industry solvers such as Gurobi. Showed me how optimization and ML reinforce each other — from training objectives to operational decision systems.