Our syllabus
Learn exactly what you need to know to succeed in today’s challenging market
We revise and update our syllabus on a regular basis to cover the fundamentals and keep ahead of the current industry trends
Module 1: Python for Finance
The lingua franca of data science, machine learning, and finance
- Introduction to Python
- Data Analysis in Python
- Analysis of financial data using Python
- Financial market case studies using Python
Module 2: Databases in finance – Kdb+/q
The unrivaled tool for big and high-frequency data
- Overview of kdb+/q
- Foundations of the q programming langguage
- Working with tables
- Kdb+/q for big data and machine learning
- Kdb+/q in practice
Module 3: C++ fundamentals with use cases from finance
High performance, production stability and portability
- C++ introduction
- Introduction to OOP in C++
- Defining your own structures in C++
- Introduction to the Standard Library
Module 4: Data Structures and Algorithms in C++
The core of computing
- Analysis Tools, Recursion, and Sorting
- Arrays, Linked Lists, Stacks, and Queues
- Trees and Graphs
- Git and GitHub Trees and Graphs
Module 5: Designing algorithmic trading applications
State-of-the-art content
- The hardware of electronic trading
- The networking of electronic trading
- Low-latency programming and high-frequency trading (HFT)
- Event-driven architectures
- The workflow of a trading platform
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