FDP Charter is examined twice each year in Cohorts.

It is exclusively designed for finance professionals or students who work with financial data and/or make decisions based on financial data - and those in leadership, support, & audit roles

Why enrol in the FDP Charter?

• Financial Analytics & Predictive Modelling capability are now essential requirements for many financial services roles. It's no longer enough to be a 'financial analyst' - you now have to be a 'financial data analyst'.

• Those in decision-making and/or leadership roles must also upskill - to lead data-driven transformation, and to avoid "AI-Washing" at their organization.

• Don't let yourself get automated! The FDP Charter curriculum is uniquely designed to future-proof & accelerate your career by upskilling in advanced techniques such as machine learning, predictive modeling, and big data analytics specifically for financial data.
  

What makes FDP Charter unique?

• Founded in 2019 by CAIA, we are the "Trade School of Financial Data". Every business school and certification body is now launching a financial data or AI course - we have a singular focus, honed over years.
• Our industry-led Advisory Board blends current industry professionals, top academics, and education experts to lead curriculum development. Research papers, integrated into FDP content, keep the curriculum up-to-date and relevant.
• AI-enabled FDP Tutor platform supports your studies throughout. This market-leading study tool allows those with less finance / data experience to work through the FDP curriculum step-by-step.

Typical roles / industries

• Finance professionals: move from XLS (spreadsheets) to ML (machine learning) to develop your algorithmic decision-making capabilities
• Quant coders / other finance technologists: understand the real-world applications of your work
• Finance executives: lead data-driven transformation, and gain the knowledge required to spot "AI-washing" in your firms and your competitors
• Risk, compliance, audit, and operations: understand the finance models and the decision-making being driven by them (and spot patterns for further investigation)
• Non-investment firms (e.g. trading, banking, cards & payments, other fintech, insurance, finance consulting, private equity & VC): use your access to massive data sets to maximum effect
• Data scientists (Masters / Ph.D. level) trained in natural data: apply your skills to financial data
• Graduates & business school post-grads: wanting to enhance their resume (or demonstrate their knowledge in a recognized certification), and/or prepare for quant interview tests & similar