Learn With Bit Bros
Think Different About Decisions.
Most organizations chase precision in a world defined by uncertainty. We teach a different approach — one built on probabilistic thinking, sequential decision analytics, and systems that get smarter over time.
Our Approach
The supply chain world is full of false certainty. Deterministic forecasts that pretend they know the future. Plans that shatter on contact with reality. Dashboards full of numbers that don't drive action.
We believe in a fundamentally different approach: embrace uncertainty, don't hide from it. Use probabilistic forecasts instead of point estimates. Build decision policies instead of static plans. Test ideas in simulators before deploying them in the real world.
This section is where we teach what we know — from foundational philosophy to concrete, hands-on lessons. Whether you're an executive trying to understand why your forecasts keep failing or an engineer building your first optimization model, there's something here for you.
Explore by Topic
Our Philosophy
How we think about decisions, uncertainty, and building systems that work.
2 lessons
Supply Chain
Rethinking logistics, planning, and delivery through a quantitative lens.
2 lessons
Optimization
MILP, linear programming, and mathematical modeling for real-world problems.
2 lessons
Decision Science
Sequential decision analytics, policies, and simulation-driven decision-making.
2 lessons
Data Careers
Career strategy, leadership, and navigating the data profession.
2 lessons
Our Philosophy
How we think about decisions, uncertainty, and building systems that work.
Why We Embrace Uncertainty
The case against deterministic planning and why probabilistic thinking changes everything.
Decisions Under Uncertainty: An Introduction
Every business decision is a bet. The question is whether you're making informed bets or blind ones.
Supply Chain
Rethinking logistics, planning, and delivery through a quantitative lens.
Optimization
MILP, linear programming, and mathematical modeling for real-world problems.
When to Use Mixed-Integer Programming
MILP is powerful — but it's not always the right tool. Here's a practical guide to when integer programming shines and when you should reach for something else.
The Art of the Big-M Constraint
Big-M constraints are the duct tape of MILP modeling. Used well, they're indispensable. Used badly, they'll destroy your solver performance.
Decision Science
Sequential decision analytics, policies, and simulation-driven decision-making.
Sequential Decision Analytics: A Better Framework
Why single-shot optimization isn't enough and how to think about decisions that unfold over time.
Simulation as a Laboratory for Decisions
Why building a simulator is the most underrated step in solving complex decision problems.
Data Careers
Career strategy, leadership, and navigating the data profession.
The Skills That Actually Get You Promoted
Technical skills get you hired. Everything else determines whether you go anywhere. Here's what actually matters for career growth in data.
Building a Personal Brand in Data
Your GitHub and resume aren't enough. Here's why building a public presence matters — and how to do it without being cringe.
Want to Go Deeper?
Our books dive deep into the topics covered here. From MILP optimization to career strategy to sequential decision analytics.