Marysia Winkels
where else to find them
what they talk about
all 4 in the filters
where they keep going back
- PyData Global2 editions2022–2024
all 6 in the filters
on a stage with
who talks about the same things
- Vincent D Warmerdam22 talks
- Ian Ozsvald17 talks
- Aileen Nielsen7 talks
- Josip Saban6 talks
- Emily Gorcenski20 talks
- Allen Downey9 talks
| title | conference | date |
|---|---|---|
| Help! There Are Humans in My Data! | PyData Amsterdam 2025 | 2025-09-24 |
Fieldwork shows concrete costs: 350 hours of shadowing police, police systems with 685 incident codes, downstream use in predictive policing, and the Titanic… | ||
| The Data That Shapes Foundational LLMs | PyData Global 2024 | 2024-12-03 |
Explains concrete pipelines: HTML parsing, deduplication and ngram coverage filtering, and modelbased filters such as FastText to surface higherquality web… | ||
| Data Storytelling through Visualization | PyData Eindhoven 2022 | 2022-12-01 |
Demonstrates that specific design choices — chart type, axis limits, color, title and annotations — change conclusions; uses Anscombe and same stats different… | ||
| Data Storytelling through Visualization | PyData Global 2022 | 2022-12-01 |
Argues that visualization must move from analysis to action and demonstrates concrete choices that change conclusions: used basic matplotlib and mentions… | ||
| Data-Centric AI Cookbook-let's prep that data | PyData Global 2022 | 2022-12-01 |
Shows that tiny concrete fixes beat vague promises: a penguin dataset of ~5000 images contains unit errors (cm vs mm) that break models, imputing missing… | ||
| Models Schm-odels: Why you Should Care about Data-Centric AI | PyData London 2022 | 2022-06-17 |
Claim: iterating on data beats fiddling with models for practical gains. Concrete actions include cleaning noisy labels, rebalancing train and validation… | ||
| (Serious) Time for Time Series | PyCon DE & PyData Berlin 2022 | 2022-04-11 |
Shows hands‑on work with an air‑quality dataset (California, 2007–2017) using Jupyter notebooks and Pandas to parse timestamps, resample, and compute rolling… | ||
| (Serious) Time for Time Series | PyCon US 2021 | 2021-05-14 |
Uses Pandas to parse timestamps (pd.to_datetime), set the datetime index and resample hourly/monthly/daily, create lag features with shift, and smooth with a… | ||
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