Q4-2021 FDP Exam: Registration is now closed
Q2-2022 FDP Exam: Registration opens November 25th




THE GLOBAL DESIGNATION FOR FINANCE PROFESSIONALS IN A DATA-DRIVEN INDUSTRY


The transformative effect of data science on the finance industry requires today's finance professional to understand the application of big data, data mining, workflow automation and machine learning in investment decisions.




FDP CHARTER

The FDP Institute has designed this self-study program to provide financial professionals with an efficient path to learn the essential aspects of financial data science.

The Financial Data Professional (FDP) designation showcases your global value by validating your financial acumen and data analysis skills.  FDP® is the globally recognized credential for professionals managing, analyzing, translating, and distributing data in finance. Become an FDP Charter holder. The FDP designation is powered by the Chartered Alternative Investment Analyst Association.

Successful completion of the FDP exam and the online requirements puts you in an influential group of Financial Data Professional Charterholders worldwide. And your education does not end once you have passed and become an FDP Charter holder. The FDP Institute strives to continually offer learning opportunities to the FDP community.

Earning the FDP Charter is an investment in your career.

Applicable: Learn and apply strategies to be a well-rounded Financial Data Professional.
Current: Program materials are reviewed and updated regularly.
Efficient: 77% of Charter holders who earn their FDP Charter do so in 3-4 months.
Comprehensive: Our all-encompassing exam covers everything from the characteristics of data analysis to the targeted applications in finance. 


CURRENT FDP CHARTER CURRICULUM

Topics 

Approximate 
Weight % 

       1. Introduction to Data Science & Alternative Data4-10
       2. Machine Learning: Introduction to Algorithms 4-10
       3. Machine Learning: Regression, Support Vector Machine & Time Series Models 4-10
       4. Machine Learning: Regularization, Regression Trees, Random Forest & Overfitting 4-10
       5. Machine Learning: Classification & Clustering 4-10
       6. Machine Learning: Performance Evaluation, Backtesting & False Discoveries 4-10
       7. Data Mining & Machine Learning: Naïve Bayes & Text Mining 4-10
       8. Big Data & Machine Learning: Ethical & Privacy Issues 4-10
       9. Big Data & Machine Learning in the Financial Industry25-50




 Brought to you by: 

2021 Financial Data Professional Institute©
Contact Us: info@FDPinstitute.org 
Privacy | Policies | Terms of Use | FAQ

Powered by Wild Apricot Membership Software