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ARPM Bootcamp
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 Export to Your Calendar 8/13/2018 to 8/18/2018
When: Monday, August 13, 2018

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6-day Intensive Quantitative Training

Online anytime | New York, 13-18 August 2018

 

The Advanced Risk and Portfolio Management (ARPM) Bootcamp with ~500 onsite attendees (70% practitioners - 30% academics)
- Consolidates risk managers’, financial data scientists’ and portfolio managers’ expertise into a structured and rigorous quantitative framework
- Empowers avid learners with a quantitative background to gain the deep technical knowledge necessary to operate across the complex world of quantitative risk management and asset management

Grants Academic credits with partner Universities; ARPM Bootcamp Certificate of Completion; 40 GARP CPD;

 
Learn

In 50 hours of instruction (37 hours lectures + 13 hours review sessions) the training program covers data science and machine learning; classical/Bayesian multivariate statistics and econometrics; financial analytics, market, credit & liquidity risk management; estimation error and model risk; and much more

- Onsite: intense 6-day training/networking event, with weekly preparations reading, and review sessions

- Online: curated video lectures, embedded in the ARPM Lab for immediate access to all supporting tools

Choose whether to attend the ARPM Bootcamp onsite in 6 packed days, or online at your own pace

Practice anywhere, anytime
Revisit theory, case studies, Python & MATLAB code, simulation clips, exercises, slides in the ARPM Lab online available for months upon enrollment

Connect
- Gala Dinner: (first 330 onsite registrants) dine and socialize with world-renowned quants and industry leaders; plus lottery, charity, and more…
- Social Mixer: mingle with ~500 practitioners and academics; chat, play, and share memories and photos
- Breaks: multiple, informal occasions to network with like-minded fellow attendees

                                                             Contact us for group discounts and alumni discounts

Registration | Program VideoTestimonials

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