The following is a transcript from the video 'Quant Dev University Course Tier List'

The Ultimate Computer Science Tier List for Quantitative Developers (2026 Guide)

If you want to become a quantitative developer (quant dev), you need clarity on what to study — and what to ignore.

The term quantitative developer is overloaded. In this article, we’re specifically referring to:

A software engineer working in quantitative trading — not a researcher, not a PhD in machine learning, not a mathematician.

This guide ranks university computer science courses based on their relevance to becoming a generalist quant dev (infrastructure, low latency, trading systems).

Tier Definitions

• S Tier — Essential. You are “cooked” without it.

• A Tier — Strongly recommended.

• B Tier — Useful.

• Below B — Optional or Skip.

Everything B and above is worth taking.

S Tier (Must Have)

These courses are foundational for quant dev interviews and on-the-job performance.

Data Structures

You cannot pass quant interviews without deep knowledge of data structures.

This is NOT about inverting a binary tree for fun.

You must understand:

• How std::unordered_map works internally

• Hash tables

• Tree structures

• Memory layout

• Cache behavior

• Time vs space trade-offs

In low latency environments, memory access patterns matter more than asymptotic complexity.

Data structures = how your data lives in memory.

Algorithms

If data structures are the memory, algorithms are the instructions.

Every system boils down to:

• Data

• Transformation logic

• Performance constraints

Algorithms teach you how to transform data efficiently.

If you skip algorithms, you will struggle in:

• Technical interviews

• Optimization work

• Systems design discussions

Operating Systems

Most candidates underestimate this.

Operating systems matter in quant because:

Virtualization

How does the OS create the illusion of unlimited resources?

• Virtual memory

• Context switching

• Scheduling

• Paging

Threading

• Thread lifecycle

• Concurrency models

• Process vs thread

• Synchronization

Many firms have an OS interview round.

Computer Architecture

If you’re writing C++, you must understand hardware.

Topics that matter:

• CPU cache hierarchy (L1/L2/L3)

• Cache lines

• False sharing

• Branch prediction

• Memory alignment

• Instruction pipelines

Some firms (e.g., top-tier trading shops) heavily test this.

If you don’t understand how your code interacts with hardware, you’re leaving performance on the table.

Programming Paradigms (OOP + Beyond)

This is often labeled as “Object-Oriented Programming,” but it's really:

How do you turn an idea into working software?

Understanding:

• OOP

• Functional patterns

• Design trade-offs

• Abstraction

• Code organization

Example Interview Problem

You’re given task logs:

struct Task {
    int TaskID;
    int Duration;
    int NextTaskID;
};

A chain is formed when tasks link via NextTaskID.

You must return:

struct TaskSummary {
    int TaskIDStart;
    int TaskIDEnd;
    int AverageDuration;
    int LongestDuration;
};

Complication:

If two tasks point to the same task, ignore the later chain.

This requires:

• Graph modeling

• Logical filtering

• State tracking

• Clean abstractions

That’s programming paradigms in action. You can do this problem here!

A Tier (Strongly Recommended)

Distributed Systems

Trading systems are distributed.

Servers:

• Communicate asynchronously

• Maintain shared state

• Recover from failure

• Partition by instrument groups

You need to understand:

• Client vs server

• State synchronization

• Failure recovery

• Message ordering

You may not need it to pass interviews — but you need it on the job.

Testing & Reliability

One of the most underrated skills.

You can judge a developer by their tests.

Good testing demonstrates:

• Code clarity

• Edge case awareness

• Debugging ability

• Ownership

Interview tip: If you can’t debug your own code, you won’t pass.

Parallel Computing

As a junior, you don’t need to master:

• std::barrier

• std::latch

• Memory fences

• Advanced atomics

But you should understand:

• Basic threading

• Race conditions

• Parallel performance intuition

More important as you become senior.

B Tier (Useful)

Networking

You are not a network engineer.

But you should understand:

• TCP vs UDP

• Network stack basics

• Packet capture (pcap)

• Wireshark basics

• Latency implications

You will likely face networking interview questions.

If your boss hands you a packet capture and you don’t understand layers, you’re in trouble.

Database Systems

You don’t need to know:

• B-tree internals

• SSTables

• Merkle trees

You do need to know:

• SQL fundamentals

• Database trade-offs

• Indexing basics

• When to use what

Quant systems store:

• Trades

• Logs

• Risk data

• Market data snapshots

Understanding DB trade-offs matters.

Optional

These are helpful but not critical.

Probability & Data Analysis

As a quant dev (not researcher), you need:

• Normal distribution

• Z-score

• Skew

• Kurtosis

You do NOT need:

• Bayesian theory mastery

• Advanced statistical modeling

You are an engineer, not a quant researcher.

Discrete Mathematics

Some exposure helps:

• Basic logic

• Graph theory intuition

But you won’t use heavy proofs daily.

Cloud Computing (AWS/GCP)

Most quant firms use:

Bare metal, on-prem hardware.

You won’t:

• Spin up EC2

• Call Lambda

• Tune cloud storage

Exception: exchanges or adjacent companies (e.g., partnerships with Google Cloud).

Skip

Machine Learning (For Undergrads Targeting Quant Dev)

This is controversial.

You do NOT need:

• Deep learning mastery

• PyTorch wizardry

• ML research

ML in quant is typically handled by:

• PhDs

• Dedicated researchers

• Signal processing specialists

If you’re an undergraduate trying to break into quant dev:

Focus on systems and C++.

Linear Algebra

You are not a mathematician.

Unless you're becoming a quant researcher, heavy matrix math won’t be used daily in trading infrastructure or low latency roles.

Cybersecurity

You may compute an HMAC once.

That doesn’t justify a full semester.

Graphics Programming

Not relevant.

Bonus: Compilers (Special Case)

Compilers are mostly optional — except for one area:

Template Metaprogramming

In low latency C++:

• Compile-time computation

• Type transformations

• Template specialization

• Static assertions

Example style problem:

using Input = Numbers<1,3,5,7,7>;
using Expected = Numbers<7,7,5,3,1>;

static_assert(
    std::is_same_v<
        Reverse<Input>,
        Expected
    >
);

This requires:

• Recursive templates

• Compile-time logic

• Type manipulation

You don’t need to be a compiler engineer.

But you do need template fluency. You can do this problem here!

Final Tier Summary

S Tier

• Data Structures

• Algorithms

• Operating Systems

• Computer Architecture

• Programming Paradigms

A Tier

• Distributed Systems

• Testing & Reliability

• Parallel Computing

B Tier

• Networking

• Database Systems

Optional

• Probability & Data Analysis

• Discrete Math

• Cloud Computing

• Compilers (except template meta)

Skip

• Machine Learning (for this path)

• Linear Algebra (for dev role)

• Cybersecurity

• Graphics

Final Advice

If your goal is to become a quantitative developer in trading:

Prioritize:

• Systems

• C++

• Memory

• Concurrency

• Performance

• Hardware awareness

You are building trading engines, not research models.