PyData Barcelona 2017
2017-05-19..2017-05-21
Barcelona, Spain
| title | author | length |
|---|---|---|
| Travis Oliphant - Chief Data Scientist and co-founder of Continuum Analytics | Travis E. Oliphant | 01h 07m |
Filmed at PyData Barcelona 2017
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData… | ||
| The Magic Behind PySpark, How It Impacts Perf & the "Future" | Holden Karau | 55m |
Explains the concrete claim that Python is not Spark's bottleneck when data movement and serialization are fixed, shows actual benchmarks comparing RDD vs… | ||
| Asteroid prediction impact | Gema Parreño | 54m |
Filmed at PyData Barcelona 2017
https://pydata.org/barcelona2017/schedule/presentation/33/
www.pydata.org
PyData is an educational program of NumFOCUS, a… | ||
| Squeeze (gently) your data | Francesc Alted | 01h 03m |
Filmed at PyData Barcelona 2017
https://pydata.org/barcelona2017/schedule/presentation/34/
www.pydata.org
PyData is an educational program of NumFOCUS, a… | ||
| Security for Data Scientists | David Arcos | 22m |
Filmed at PyData Barcelona 2017
https://pydata.org/barcelona2017/schedule/presentation/52/
www.pydata.org
PyData is an educational program of NumFOCUS, a… | ||
| A Beginner's Guide to Weather & Climate Data | Margriet Groenendijk | 44m |
An introduction to working with weather and climate datasets using Python tools common in the PyData ecosystem. It covers formats and sources of meteorological… | ||
| Analyzing code contributions to the CPython project using NetworkX and Matplotlib | Jordi Torrents | 40m |
Builds a reproducible pipeline that turns repo events into a bipartite developer‑file graph, uses NetworkX and Matplotlib to compute k‑components, cohesion and… | ||
| Machine Learning to know if you R coming | Claudia Guirao | 34m |
Models predict cancellations by combining survival analysis on booking lead time with feature engineering from channel, package type and inferred country… | ||
| The Secret Life Of Rolling Pandas | Jaime Fernandez del Rio | 33m |
Shows how pandas and related libraries implement fast rolling aggregations and what actually gets built and why: running sums via cumulative-sum trick for O(1)… | ||
| LympHOS2, Organizing and Sharing Biological Data | Óscar Gallardo Román | 52m |
LympHOS2 is a system for organizing and sharing biological data, presented in the context of PyData tools and practices. The talk covers how LympHOS2 manages… | ||
| Web Scraping with Python | Carles Illa | 34m |
Concrete, reproducible recipes: step-by-step code using requests + BeautifulSoup to fetch and parse HTML, a Scrapy spider that crawls pagination and yields… | ||
| Happiness inside a job: a social network analysis | Guillem Duran Ballester | 36m |
Uses two years of mobile app data from 34 companies and ~4,400 employees to predict employee turnover by combining network features and classic ML The concrete… | ||
| Despicable machines: how computers can be assholes | Maciej Siwek | 34m |
A deep learning model claims to infer criminality from still face images, reportedly reaching 90% accuracy using AlexNet; the pipeline trains on… | ||
| Prototyping Interactive Dashboards with Jupyter Notebooks | Camilo Cardona | 30m |
Prototype claim: notebooks let teams build actionable UI fast using real code not mockups. Uses ipywidgets, jupyter-matplotlib, jupyter dashboards (alternate… | ||
| How to map a protein structure into a network graph [...] | Massimo Domenico Sammito | 42m |
Method converts atomic coordinates into characteristic vectors for secondary elements, builds a complete weighted graph (nodes = vectors, edges =… | ||
| The Neuroscience Lab | Nicholas A. Del Grosso, Javier Martinez Alcantara | 36m |
A working lab pipeline ties stimulus delivery, hardware control, tracking, analysis and plotting into one Python stack: 360 Hertz virtual reality for rats… | ||
| Guillotina - An Async REST Resource DB to manage millions of objects | Ramon Navarro Bosch | 42m |
Guillotina is an async, transactional REST resource DB written for Python 3.6 that maps JSON Schema to objects exposed via traversal URLs and scales… | ||
| Increasing the trading prediction by mining aggregated human texting messages | Yang Liu | 30m |
Claims that mining aggregated social media and news titles improves up/down stock prediction and shows modest accuracy gains but hides exact numbers; uses… | ||
| Marketing Data Science | Joaquin Pais | 41m |
Claims that marketing must become data driven and shows a telecom case where a model using 304 features yields 13× conversion lift versus traditional… | ||
| Blockchain and smart contracts explained (and simplified) | Guillem Borrell Nogueras | 40m |
Explains that blockchain is a costly way to solve hard Byzantine consensus and that smart contract = class not inherently tied to a distributed ledger, walks… | ||
| Using network community clustering algorithms [...] | Claudia Millán | 43m |
Claim: automatic community clustering of secondary-structure elements produces compact rigid-body groups that improve molecular replacement and reduce model… | ||
| Python for Visualization of HPC Applications | Miguel Zavala-Ake | 38m |
Demonstrates using Python tools to visualize data from high-performance computing (HPC) applications, covering libraries and techniques for processing and… | ||
| Data Science for Lazy People... Genetics Will Work for You! | Diego Hueltes | 29m |
A PyData Barcelona 2017 talk demonstrates how to apply data science tools (Python ecosystem implied) to genetics problems, making genetic analysis accessible… | ||
| DeepCare Chatbot - Generating answers to customers using Deep Learning and NLP | Pascal van Kooten | 32m |
Prototype deep-care chatbot generates replies to customer tweets using a rule filter plus a neural generator, trained from an AT&T scrape of 1.3 million tweets… | ||
| TensorFlow Wide & Deep: Advanced Classification the easy way | Yufeng Guo | 40m |
A concrete demo builds a TensorFlow wide and deep classifier with canned Estimators, using feature_columns (bucketed age, crossed categories, and embedding dim… | ||
| How to manage complexity in distributed applications | Guillem Borrell Nogueras | 37m |
Claims that communication and carefully designed abstractions tame distributed complexity: a two‑year effort produces a working in‑house streaming glue rather… | ||
| Feature Importance and Ensemble Methods : a new perspective | Constant Bridon | 43m |
Empirical claim: the default tree split-count importance (scikit-learn style) often ranks useless fields like ID or injected noise highly — in one XGBoost run… | ||
| Running jupyter notebook remotely in a docker swarm cluster | Jordi Deu-Pons | 40m |
Claims a concrete fix: run a full browser plus Jupyter inside a Docker Swarm container and proxy access via an HTTP gateway so long-running cells keep output… | ||
| Extending Jupyter with Google Cloud Storage file system backend | Egor Bulychev | 31m |
Concrete implementation of a Jupyter filesystem backend for Google Cloud Storage that replaces the default disk-based ContentManager, exposes buckets and… | ||
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