- Conference
- PyData Data science & ML
- Dates
- Jul 12, 2019 – Jul 14, 2019
- Venue
- The Tower Hotel
- Place
- London, United Kingdom
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- pydata.org
- Recorded
- 19 sponsors · 70 speaker appearances
- Notes
- Tutorials 12 July, talks/keynotes 13-14 July. Tickets sold out (homepage). Speakers: all scheduled sessions from the conference site; affiliations extracted from speaker-profile bios where stated (keynote affiliations from homepage). Ticket prices not found (Wayback unavailable during research).
Ticket prices
No ticket prices recorded
Sponsors 19
Speakers and talks 70
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Elina Naydenova | Feebris | Keynote | Bridging health inequalities through machine learning | — |
| Kirstie Whitaker | The Alan Turing Institute | Keynote | The Turing Way: A how to guide for reproducible research | — |
| Lorena A. Barba | The George Washington University | Keynote | One step forward, two steps back: the frustration of diversity efforts in STEM | — |
| Lynn Cherny | — | Keynote | Goofing Off with Fun (Big) Data | — |
| Agata Sumowska | Thoughtworks | Talk | Text annotation role playing workshop (Interactive workshop for unconference) | — |
| Ajay Thampi | — | Talk | Interpretable AI or How I Learned to Stop Worrying and Trust AI | — |
| Alejandro Saucedo | The Institute for Ethical AI & Machine Learning | Talk | A practical guide towards algorithmic bias and explainability in machine learning | — |
| Alla Maher | Man Group | Talk | Kafka in Finance: processing >1 Billion market data messages a day | — |
| Anna Schneider | Stitch Fix | Talk | Just ask: designing intent-driven algos | — |
| Anna Veronika Dorogush | — | Tutorial | Mastering Gradient Boosting with CatBoost | — |
| Bertil Hatt | — | Tutorial | AB-testing by clusters | — |
| Bhargav Srinivasa Desikan | University of Washington | Talk | Text annotation role playing workshop (Interactive workshop for unconference) | — |
| Cheuk Ting Ho | Hong Kong University of Science and Technology | Talk | I am telling you 3 things about Chatbot (so you don't have to learn it the hard way) | — |
| Chie Hayashida | Cookpad | Talk | Understanding of distributed processing in Python | — |
| Chris Fonnesbeck | — | Tutorial | An Introduction to Markov chain Monte Carlo using PyMC3 | — |
| Christopher Samiullah | Babylon Health | Talk | Testing and Validating Machine Learning Models when Deploying to Production | — |
| Connor Tann | bp | Talk | Modelling the subsurface with gamma rays and machine learning | — |
| Dr Egor Kraev | ETH Zurich | Tutorial | Choosing the right neural generative model for your problem | — |
| Eddie Bell | Ravelin | Talk | Weak supervision: a new paradigm for unreliable labels | — |
| Elena Sharova | ITV | Talk | How to Validate Your Client Churn Model | — |
| Eric Drass aka shardcore | — | Talk | The anatomy of a #deepfake | — |
| Fabian Krause | Man Numeric | Talk | Quantamental Investing - the future of finance | — |
| Filip Ter | Barclays | Tutorial | Data Analysis in Parallel | — |
| George Cushen | Farfetch | Talk | Knowledge graphs --enter--> the Hype Cycle | — |
| Gianluca Campanella | Microsoft | Talk | The unreasonable effectiveness of feature hashing | — |
| Guillermo Barquero | — | Talk | Productionising Data Science at Scale | — |
| Harrison Pim | Wellcome | Talk | Image search without image captions | — |
| Ian Ozsvald | — | Talk | Executives at PyData | — |
| Ian Ozsvald | — | Tutorial | A gentle introduction to Pandas timeseries and Seaborn | — |
| Igor Gotlibovych | University of Washington | Talk | Deep Learning and Time Series Forecasting for Smarter Energy | — |
| Imran Rashid | Queen Mary University of London | Tutorial | Training intelligent game agents using deep reinforcement learning | — |
| Irene Iriarte Carretero | — | Talk | Data Science for Dinner: Recommending Recipes | — |
| Jake Coltman | — | Talk | What Failure Taught Me About Building High-Stakes Models That Actually Work | — |
| James Powell | — | Talk | Because You Can Run, You Can't Hide: Some Musings on API Design | — |
| James Powell | — | Talk | Executives at PyData | — |
| Jan Freyberg | — | Talk | Active learning in the interactive python environment | — |
| Jeffrey Hsu | — | Tutorial | A Deep Dive into NLP with PyTorch | — |
| João Martins | Farfetch | Tutorial | AB-testing by clusters | — |
| John Sandall | Coefficient | Talk | Democracy Hackathon | — |
| Jon Tutcher | — | Talk | Data Science Frameworks and Managed Services: When to Avoid the Shiny New Toys | — |
| Jonathan Fernandes | DXC Technology | Tutorial | Fundamentals of image classification using PyTorch | — |
| Jonny Brooks-Bartlett | Deliveroo | Talk | You got served: How Deliveroo improved the ranking of restaurants | — |
| Jovan Veljanoski | XebiaLabs | Talk | Modern Data Science: A new approach to DataFrames and pipelines | — |
| Karol Grzegorczyk | — | Talk | Embeddings! Embeddings everywhere! - How to build a recommender system using representation learning | — |
| Kevin Lemagnen | Cambridge Spark | Tutorial | Advanced Software Testing for Data Scientists | — |
| Kevin Lemagnen | Cambridge Spark | Tutorial | Maintainable Code in Data Science | — |
| Lev Konstantinovskiy | Full Fact | Talk | Text annotation role playing workshop (Interactive workshop for unconference) | — |
| Liam Kirwin | — | Talk | How am I going to deal with all of these cats? | — |
| Maarten Breddels | — | Talk | Modern Data Science: A new approach to DataFrames and pipelines | — |
| Maciej Arciuch | — | Talk | Embeddings! Embeddings everywhere! - How to build a recommender system using representation learning | — |
| Mahan Hosseinzadeh | — | Talk | Prophet at Scale: Using Prophet at scale to tune and forecast time series at Spotify | — |
| Maria Navarro | LV | Talk | How good is your prediction? - Quantifying uncertainty in Machine Learning predictions | — |
| Mario Bonamigo | — | Talk | Data science and Internet of Things on the edge | — |
| Mark Farragher | — | Talk | On the Path to Causal Inference | — |
| Matthew Hertz | — | Talk | Kafka in Finance: processing >1 Billion market data messages a day | — |
| Michael Sugimura | ShopRunner | Talk | Combining Computer Vision and NLP for Multi-Task Fashion Attribute Modeling at Shoprunner | — |
| Michał Łopuszyński | Signal AI | Talk | Text annotation role playing workshop (Interactive workshop for unconference) | — |
| Michal Mucha | — | Tutorial | Build and Deploy an End-to-end Streaming NLP Insights System | — |
| Omer Yuksel | IMC | Talk | Understanding exchange network dynamics with Python | — |
| Patrick Gonzalez | — | Talk | From healthcare.hospital import datascience | — |
| Pavlos Papaconstadopoulos | — | Talk | From healthcare.hospital import datascience | — |
| Raoul-Gabriel Urma | Cambridge Spark | Tutorial | Advanced Software Testing for Data Scientists | — |
| Richard Chadwick | — | Talk | Democracy Hackathon | — |
| Ruben van de Geer | Vrije Universiteit Amsterdam | Talk | A Primer (or Refresher) On Linear Algebra for Data Science | — |
| Soledad Galli | — | Talk | Testing and Validating Machine Learning Models when Deploying to Production | — |
| Susannah Klanecek | — | Tutorial | A Deep Dive into NLP with PyTorch | — |
| Trevor Sidery | — | Talk | Productionising Data Science at Scale | — |
| Vincent Warmerdam | GoDataDriven | Talk | How to Constrain Artificial Stupidity. | — |
| w4rner | — | Talk | Text annotation role playing workshop (Interactive workshop for unconference) | — |
| Zack Akil | — | Talk | Safer Cycling with an Edge TPU watching your back | Video |