PyDataNYC May 2016 Meetup

May 25, 2016 · New York, United States of America

PyData NYC meetup is back! Please join us on the 25th of May at Civic Hall. We've got a dynamic lineup of speakers in store for you! 

In addition to our core speakers, there will also be lightning talks.  If you are interested in speaking, this 5-minute format is a great place to start. It's also a great forum for presenting work still in progress. If you are interested, please send a proposal containing the title and a brief description to [masked] and [masked] by May 20.

We will be accepting walk-up attendees the day of the event, but please register early so we know how much pizza to get.

Agenda

• 6pm: Doors open, pizza served, courtesy of Bloomberg

• 6:30pm: Speakers (see below for abstracts): Ritesh Bansal and Jim Schmitz (Meta analysis for predicting election outcomes); Lev Konstantinovskiy (Word embeddings for fun and profit with gensim); Tony Fast (The Internet killed the lab notebook).

• 8:00pm: Lightning talks (see instructions above for submitting one!).

    • Tom Caswell: Awkward Winter Jacket, a dict-like LRU for DataFrames backed by on-disk feather format files. If you have expensive table-producing functions, memoize to disk with feather. https://github.com/tacaswell/awj/

    • Srinivas Sunkara: An analysis of well-being in San Francisco. Developed as part of the Bayes Hackathon, we analyze livability metrics (crime, transportation, etc.) and present the information with interactive Jupyter notebooks. https://github.com/bayeshack2016/quant-hack/

• 8:15pm: Reception

• 9:00pm: End of meetup

Location

Civic Hall is located at 156 5th Avenue on the northwest corner of 20th Street on the second floor.  Be sure to take the elevator bank furthest from the 5th avenue entrance to best access the space.  Should you have any trouble, feel free to ask the doorman for the West Elevators! He's happy to help.  

Abstracts

Ritesh Bansal and Jim Schmitz, Meta Analysis for Predicting Election Outcomes. Meta-analysis is a powerful technique that can be applied to different domains like sports, markets, elections, etc. Nate Silver achieved great success in forecasting the U.S. presidential race by predicting 49/50 states correctly in 2008 and all 50 states correctly in 2012. This achievement is even more impressive as many savvy political consultants called the race incorrectly until election night 2012. Nate Silver's model is outlined on the 538 site and is based on meta-analysis with a series of adjustments. His model is a textbook example of using small-data to make big predictions. In this talk we walk through the model using an IPython notebook. Ritesh Bansal studied math and computer science at CMU and econometrics at the Graduate Center of City College. He has worked for over 15 years in the financial industry building and trading models and consulting for hedge funds and banks in risk management. He founded Rational Insights in 2012 to apply machine learning expertise to the domains of finance and politics. Jim Schmitz studied math and computer science at Rensselaer Polytechnic Institute and math and finance at NYU-Courant. He has worked at several hedge funds and investment banks doing quantitative research and trading. He is interested in politics and prediction markets, and once built a computer program to act as a market maker in the Iowa Electronic Markets. He published a research paper on the results, titled "Algorithmic Trading in the Iowa Electronic Markets.”

Lev Konstantinovskiy, Word embeddings for fun and profit with gensim. Word embeddings is a family of modern neural network techniques for analysing raw text. In this talk we will show how to use several of these algorithms - word2vec, doc2vec, deep inverse regression and word movers distance. The algorithms will assign a genre to a movie plot with various degrees of success. All examples will be using an open-source Natural Language Processing package gensim. Lev is community manager of an open source Natural Language Processing Python package Gensim aka "Topic modelling for humans". In the past he counted microseconds in trading and did algebraic geometry . He starts every day with a couple of hours of yoga.

Tony Fast, The Internet Killed the Lab NotebookOpen Source Software is significantly impacting scientific research.  Advanced analytics and high performance computing have lower barriers to entry than before.  These tools must exist - and they must be accessible - to solve the increasingly difficult real world problems scientists face everyday.  Massive scientific collaborations are happening at internet scale.  And nearly all other scientific organizations are feeling the pressure to make that transition.  Using new tools for a scientist is a big decision and the open source software world is a confusing place.  On the journey to digital science, many are going to discover NumFocus sponsorship projects will be in their toolbelt; as many began as scientific research.  This talk will expand on the impacts of the PyData community and open source software as it pertains to the modern scientist.  Tony Fast is a Community Scientist at Continuum Analytics; he is also Co-Founder of PyData Atlanta.  He uses open source software and the scientific methods to solve everything from Generalized Spherical Harmonics to putting together pizza orders for meetups.  Tony still lives, eats, and breathes science even after crawling out of academia.  He has taught Mechanical Engineering at Georgia Tech, recieved his Ph.D. in Materials Science from Drexel University, and Bachelor's in Ceramic Engineering at Rutgers University.

Event organizers
  • PyData NYC

    PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to)

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