• Hadoop 2.x / Spark / R Analytics / Python - Foundation Courses

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    Hey! I'd like to share this amazing learning opportunity with you as well as a very special surprise only for members of this group. We present a series of expert live online foundation courses, priced at only $43 each, covering the latest BIG Data technologies. PS - This time, there is an exclusive enroll for 1 get 1 free offer only for the members of this group. We've got the following scheduled for April 9th & 10th: Registration (http://promo.skillspeed.com/usa-foundation-course-r-analytics/) - R Programming & Analytics (http://promo.skillspeed.com/usa-foundation-course-r-analytics/) • Hadoop 2.x Foundation Course for FREE. Registration (http://promo.skillspeed.com/usa-foundation-course-big-data-hadoop2x/) - BIG Data & Hadoop 2.x (http://promo.skillspeed.com/usa-foundation-course-big-data-hadoop2x/) (http://promo.skillspeed.com/usa-foundation-course-big-data-hadoop2x/) - • Spark & Scala Foundation Course for FREE. Registration (http://promo.skillspeed.com/spark-foundation-course/) - Apache Spark & Scala (http://promo.skillspeed.com/spark-foundation-course/) - • Hadoop 2.x Foundation Course for FREE. Registration - Python & BIG Data Foundation (http://promo.skillspeed.com/usa-python-big-data-foundation) • Hadoop 2.x Foundation Course for FREE. This 1 + 1 deal is valid only for 7 seats per course, so if you're interested, please reserve your seat immediately :) Each of the above courses features: • 6 Hours of Live Instructor Led Training • 4 Hours of Practicals • Fully Equipped Virtual Machines • 24/7 Live Support2 Project Guides • Foundation Level Certification This is a great opportunity for you to dive into BIG Data; and we guarantee you the best learning experience. See you!

  • The Latest and Greatest Pandas Features (since v 0.11)

    Projective Space

    Phillip Cloud, core contributor on the Pandas data analysis library, will present on some of the less-well-known, but really useful features that have come out since version 0.11 and some that are coming soon. Come learn more about how to take full advantage of the Pandas Python library, get some pizza, beer and other drinks, and meet fellow quantitative Pythonistas. This will be a joint Meetup with the NY Finance PUG. Let me know if you will join us downtown.

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  • Implied Volatility with Pandas AND Python in Excel

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    Besides Pizza, Beer and other drinks, we'll have Brian Spector of NAG presenting "Implied Volatility using Python's Pandas Library." Brian will discuss a technique and script for calculating implied volatility for option prices in the Black-Sholes formula using Pandas and nag4py. Also, we will fit varying degrees of polynomials to the volatility curves, examine the volatility surface and its sensitivity with respect to the interest rate. Then Aaron Watters of Enthought will present an overview of replacing VBA with Python in Excel using the PyXLL package. No more Excel Hell. Let me know if you plan to join us downtown.

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  • Let's meet up and brainstorm!

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    Dear Friends, I recently became your new organizer. I would love to hear about what you want to gain from this group and set some target together. This event is a free style discussion. So you have to do a little homework beforehand. Agenda: 6:30-7:00 self Introduction(1 minute/person) --your background --your experience with python and what part you are really good at 7:00-8:30 share your learning about Python(5 minute/person) -- pick three latest Python news or share why it is so exciting for you -- share what you want to learn more or something you would like to work with teammates! Our goal for this meetup is to get familiar with each other, know a little about your skill level, and prepare to work on things together. And we don't have food/drink sponsorship, so bring something with you. Don't starve! I also run a pretty popular group called NYC-Open-Data http://www.meetup.com/NYC-Open-Data . What I did there is to set up workshops(one hour teaching, one hour TA) and inspiring talks given by the experts. I wish I can do the same for this Python group. Please let me know how you think! Hope to know you in person soon. Best, Vivian

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  • Avoiding Excel Hell

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    Welcome to the second QPUG meet-up! The meeting will once again be held at the Cornell Club (Fall Creek Room, 6E 44th St. between Madison and 5th Ave) on Tuesday May 14th! The talk for this meeting is entitled “Avoiding ‘Excel Hell’ using a Reproducible and Flexible Framework for Research, Analysis, and Production”, by Didrik Pinte. He will discuss how the Python ecosystem can provide a viable alternative to the typical Excel workflows seen in trading desks, quantitative research groups, and reporting systems. Didrik's examples will be based on work done in QuantLib and PyQL. The tentative agenda: 6:00pm – Doors open 6:20pm – Opening remarks/housekeeping 6:30pm – Speaker/Q&A 7:30pm to 9:00pm – Reception over Beer/Wine/Soda/Food Bring a smile and plenty of questions; we are hoping to have an interactive session where people get to know each other.

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  • From Research to Application

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    Welcome to the first formal QPUG meet-up! The meeting will be held on Wednesday, March 6th. Bring a smile and plenty of questions; we are hoping to have an interactive session where people get to know each other. Each presentation will last roughly 25 minutes. Catering will be provided by the Cornell Club after the presentations. We are proud to present the following speakers: “Open-Source Portfolio Optimization” by Marcos Lopez de Prado, PhD. Marcos is Head of Global Quantitative Research at Tudor Investment Corporation, and a Research Affiliate at Lawrence Berkeley National Laboratory. He will discuss how and why he uses Python in his research and present the highlights of his open-source CLA portfolio optimization algorithm. Additional details can be found at www.QuantResearch.info (http://www.quantresearch.info/). “From Research to Application” by Kelsey Jordahl, PhD. Kelsey is a scientific developer at Enthought. He will use an open source UI library called Enaml to illustrate how one can use Python to rapidly develop an application. This application will use real world data to drive common analyses and visualization. The tentative agenda: · 6:00pm – Doors open · 6:15pm – Opening remarks/housekeeping · 6:25pm – First Speaker · 6:55pm – Second Speaker · 7:25pm to 9:00pm – Reception

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