How to Become a Financial Data Professional®
As companies have come to understand the benefits of utilizing big data to learn more about consumers and increase productivity, data scientists and data science practices have begun to intersect with almost every career field, including finance. Data scientists approach sourced data with a business question or goal, such as increasing revenue, better understanding consumer habits, or performing risk analysis, and proceed to clean, analyze, and communicate the meaning of this data to key stakeholders. In finance, those with a strong knowledge of data science are able to use machine learning, artificial intelligence, and big data practices and analytics to learn more about their clients and “communicate technical information across organizations.”
The Financial Data Professional Institute® (FDPI®) was created to educate financial professionals in the field of data science. Their primary method of education is their assistance in preparing for and obtaining the Financial Data Professional® (FDP®) Charter, a designation awarded by the FDPI to financial professionals and data scientists devoted to implementing data science strategies in the financial sector.
What is the FDP Designation? About the Financial Data Professional Charter:
The FDP Charter is a globally recognized professional designation for those exploring and contributing to the intersection of data science and finance. Those who wish to obtain the FDP Charter must satisfy an online programming class requirement and pass the FDP Charter examination.
Why Earn the FDP Charter?
Big data and machine learning’s emergence continues to transform the financial services industry. Data scientists and financial professionals coupling their abilities and skills is now critical to long-term career success. Your FDP Charter will demonstrate an elevated financial acumen and proficiency with the data analysis skills needed in our increasingly-digitized world.
The process of obtaining the charter will teach you the knowledge and skills to contribute data science expertise to the financial sector, thus enabling you to perform your job functions better and qualify you for new career opportunities.
How to Become an FDP Charterholder
The process of becoming an FDP charterholder includes three steps. First, you must prepare for and pass the FDP Charter Examination. Second, you must satisfy an online programming class requirement on basic Python or R programming. The classes can be
completed before or after you take the FDP Charter Examination, and will take approximately 8-10 hours total. You can take these classes through the following organizations:
Datacamp (offers both classes, available once you register for the charter exam)
Dataquest (offers both classes, available once you register for the charter exam)
Finally, upon successful completion of the exam, you must submit a signed Ethics Agreement for FDP charterholders in addition to two industry recommendations.
Keep in mind that although the requirements for becoming an FDP charterholder may seem simple, it’s assumed that you will begin the process with a deep understanding of finance, including risk management and the roles of and financial models used by various financial institutions. Many people find it helpful to have completed undergraduate or graduate coursework on these topics or take the CAIA®, CFA®, or FRM® exams prior to beginning the process of becoming an FDP charterholder.
About the FDP Charter Examination
You should expect to spend 200+ hours preparing for the FDP charter examination. The exam is offered twice annually at Prometric test centers worldwide. Exam fees are as follows:
Registration fee (refundable)
Enrollment fee (non-refundable)
Total FDP Exam Fee
You may not reschedule your FDP exam; however, you can cancel your testing administration and re-register for a future date for a flat fee of $450. The same fee applies if you do not pass the exam and choose to retake it.
What’s Tested on the FDP Charter Examination?
The FDP exam is made up of 80 multiple-choice questions (75% of your overall score) and 3 constructed response questions (25% of your overall score). The topic breakdown is as follows:
Introduction to Data Science
Linear & Logistic Regression, Support Vector Machines, Regularization, and Time Series
Decision Trees, Supervised Segmentation, and Ensemble Methods
Classification, Clustering, and Naive Bayes
Neural Networks and Reinforcement Learning
Performance Evaluation, Back-testing, and False Discoveries
Ethical & Privacy Issues
Note that the exam does not cover Python or R programming; those topics will be covered in the additional required classes you take.