| Aditi Khullar |
Introduction to Language Modeling
|
| Aditya Lahiri |
Dealing With Imbalanced Classes in Machine Learning
|
| Agata Sumowska |
Role playing Annotation workshop
|
| Akos Furton |
Production Code in Data Science Consulting
|
| Alex Egg |
Discover your latent food graph with this 1 weird trick
|
| Allen Downey |
The Inspection Paradox is Everywhere,
Unconference - 10:55: Contributing to Open Source, 11:40: Data Science Education
|
| Amanda Moran |
Pandas vs Koalas: The Ultimate Showdown!
|
| Anne Bauer |
Data science at The New York Times: a mission-driven approach to personalizing the customer journey,
Fireside Chat
|
| Anthony Scopatz |
Conda-press, or Reinventing the Wheel,
What's now in NumFOCUS projects? (Part 2),
Stump the Chump
|
| Bhargav Srinivasa Desikan |
Role playing Annotation workshop
|
| Bhargav Srinivasa Desikan |
Role playing Annotation workshop
|
| Bill Lynch |
Bringing mental health data to doctors
|
| Breck Baldwin |
Introduction to Bayesian Modeling with Stan: No Statistics Background Required (Pt 1),
Introduction to Bayesian Modeling with Stan: No Statistics Background Required (Pt 2),
Introduction to Bayesian Modeling with Stan: No Statistics Background Required (Pt 3)
|
| Brian Austin |
How and why to put your Jupyter notebooks in Docker containers
|
| Bryan Cross |
Panel Discussion: My First Open Source Contribution
|
| Bryan Van de Ven |
What's now in NumFOCUS projects? (Part 2)
|
| Cameron Davidson-Pilon |
New Trends in Estimation and Inference
|
| Carlos Afonso |
Visualizing the 2019 Measles Outbreak in NYC (with Python)
|
| Chaya D Stern |
Using Graph Nets (GNs) to predict molecular properties
|
| Chris Fonnesbeck |
A Primer on Gaussian Processes for Regression Analysis,
Panel Discussion: My First Open Source Contribution
|
| Christian Steiner |
Improve the efficiency of your Big Data application
|
| Christopher J "CJ" Wright |
conda-forge sprint,
conda-forge sprint
|
| Chris Wiggins |
Data science at The New York Times: a mission-driven approach to personalizing the customer journey,
Fireside Chat
|
| Colin Carroll |
Building a maintainable plotting library,
What's now in NumFOCUS projects? (Part 1)
|
| Daniel Rodriguez |
Effective Python and R collaboration
|
| Dante Gama Dessavre |
High-Performance Data Science at Scale with RAPIDS, Dask, and GPUs
|
| David Palaitis |
Panel Discussion: Pitching Open Source Up Your Management Chain
|
| Debra Williams Cauley |
Discussion: What’s Missing in Python and Data Online Resources?
|
| Deepyaman Datta |
New and Upcoming
|
| Diego Torres Quintanilla |
Cleaning, optimizing and windowing pandas with numba
|
| Emily A Ray |
Discover your latent food graph with this 1 weird trick
|
| Eric Dill |
Is Spark still relevant? Multi-node CPU and single-node GPU workloads with Spark, Dask and RAPIDS.
|
| Ethan Rosenthal |
Time series for scikit-learn people
|
| Eugene Tang |
Introduction to Language Modeling
|
| Evan Patterson |
Semantic modeling of data science code
|
| Ferras Hamad |
Reproducibility in ML Systems: A Netflix Original
|
| Francesc Alted |
Improve the efficiency of your Big Data application,
What's now in NumFOCUS projects? (Part 1)
|
| Gil Forsyth |
Panel Discussion: Pitching Open Source Up Your Management Chain
|
| Hannah Aizenman |
What's now in NumFOCUS projects? (Part 2),
Matplotlib sprint,
Matplotlib sprint,
Panel Discussion: My First Open Source Contribution,
Building a maintainable plotting library,
HoloViz and Matplotlib sprint,
HoloViz and Matplotlib sprint,
Jupyter & matplotlib sprint,
Jupyter & matplotlib sprint
|
| Hillary Green-Lerman |
How to Prove You’re Right: A/B Testing with SciPy,
Hacking the Data Science Challenge
|
| Hyonjee Joo |
Spark Backend for Ibis: Seamless Transition Between Pandas and Spark
|
| Ian Whalen |
Implementing Lightweight Random Indexing for Polylingual Text Classification
|
| Itamar Turner-Trauring |
Small Big Data: using NumPy and Pandas when your data doesn't fit in memory
|
| Jacqueline Gutman |
Simplified Data Quality Monitoring of Dynamic Longitudinal Data: A Functional Programming Approach
|
| James Powell |
PyData Pop Quiz,
Sloth & ENVy
|
| Jason Grout |
Jupyter sprint,
Jupyter sprint,
Jupyter & matplotlib sprint,
Jupyter & matplotlib sprint
|
| Jeff Reback |
Pandas sprint,
Pandas sprint,
Introduction to pandas
|
| Jenny Turner-Trauring |
Working with Maps: Extracting Features for Traffic Crash Insights
|
| Jens Fredrik Skogstrom |
What we learned by running a large custom Bayesian forecasting model in production
|
| Jessica Tyler |
Geo Experiments and CausalImpact in Incrementality Testing
|
| Jim Weiss |
Example
|
| Joe Jevnik |
Zarr vs. HDF5
|
| Joseph Kearney |
New and Upcoming
|
| Joshua Falk |
Generating realistic, differentially private data sets using GANs
|
| Julia Signell |
Data-centric exploration using intake, dask, hvplot, datashader, panel, and binder,
HoloViz and Matplotlib sprint,
HoloViz and Matplotlib sprint,
Panel Discussion: My First Open Source Contribution,
HoloViz and Matplotlib sprint
|
| Kamal Abdelrahman |
Painting A Picture of Public Data
|
| Katherine Kampf |
Build an AI-powered Pet Detector in Visual Studio Code
|
| Keith Kraus |
High-Performance Data Science at Scale with RAPIDS, Dask, and GPUs
|
| Kelle Cruz |
AstroPy sprint,
AstroPy sprint
|
| Kelly Jin |
A Few Good Public Servants: How Great Analysis Inspires Action,
Fireside Chat
|
| Kelly Shen |
Julia for Pythonistas
|
| Kevin Fleming |
Panel Discussion: Pitching Open Source Up Your Management Chain
|
| Lara Kattan |
Machine learning from scratch using the scientific Python stack
|
| Laurence Warner |
Role playing Annotation workshop
|
| Lauren Oldja |
Managing Stakeholders: The Key to Successful Data Science for Business
|
| Lev Konstantinovskiy |
Role playing Annotation workshop
|
| Li Jin |
Spark Backend for Ibis: Seamless Transition Between Pandas and Spark
|
| Maciej Wojton |
Free Your Esoteric Data Using Apache Arrow and Python
|
| Malaika Handa |
Colorism in High Fashion (featuring: K-Means Clustering)
|
| Marc Garcia |
Introduction to pandas
|
| Marianne Hoogeveen |
The physics of deep learning using tensor networks
|
| Mariel Frank |
Introduction to NLP
|
| Marius van Niekerk |
conda-forge sprint,
conda-forge sprint,
A How-to guide for migrating legacy data applications
|
| Martin Hirzel |
Type-Driven Automated Learning with Lale
|
| Matti Lyra |
[SCHEDULE CHANGE 12:45PM - 2:15PM] Neural Networks for Natural Language Processing
|
| Meghan Heintz |
Launching a new warehouse with SimPy at Rent the Runway
|
| Michael Johns |
Propensity Score Matching: A Non-experimental Approach to Causal Inference
|
| Michael Skarlinski |
New and Upcoming
|
| Michal Mucha |
Swiftly turn Jupyter notebooks into pretty web apps
|
| Michoel Snow |
Hacking the Data Science Challenge,
How to Prove You’re Right: A/B Testing with SciPy
|
| Mike McCarty |
Panel Discussion: Pitching Open Source Up Your Management Chain
|
| Mitzi Morris |
Bayesian Inference for Fun and Profit
|
| Moussa Taifi Ph.D. |
Clean Machine Learning Code: Practical Software Engineering Principles for ML Craftsmanship
|
| Natu Lauchande |
Machine Learning Engineering principles with Python and MLFlow
|
| Nick Becker |
High-Performance Data Science at Scale with RAPIDS, Dask, and GPUs
|
| Noam Ross |
Building Software and Communities With Peer Review
|
| Parin Choganwala |
Discover your latent food graph with this 1 weird trick
|
| Patrick Landreman |
A Crash Course in Applied Linear Algebra
|
| Paul Ganssle |
Stump the Chump
|
| Petr Wolf |
From Raw Recruit Scripts to Perfect Python (in 90 minutes)
|
| Piero Ferrante |
Should I develop my own DS library? Maybe.
|
| Ralf Gommers |
What's now in NumFOCUS projects? (Part 1)
|
| Raman Tehlan |
Genetic algorithms: Making errors do all the work
|
| Raoul-Gabriel Urma |
Advanced Software Testing for Data Scientists
|
| Raphaël Meudec |
tf-explain: Interpretability for Tensorflow 2.0
|
| Rohit Kapur |
A How-to guide for migrating legacy data applications
|
| Romain Cledat |
Every ML Model Deserves To Be A Full Micro-service
|
| Ryan Abernathey |
What's now in NumFOCUS projects? (Part 1)
|
| Samuel Rochette |
Quantifying uncertainty in machine learning models
|
| Sara Seager |
Stars, Planets, and Python,
Fireside Chat
|
| Saul Shanabrook |
Same API, Different Execution,
Jupyter sprint,
Jupyter sprint,
Jupyter & matplotlib sprint,
Jupyter & matplotlib sprint
|
| Sean Law |
New and Upcoming,
New and Upcoming
|
| Stanley van der Merwe |
From Raw Recruit Scripts to Perfect Python (in 90 minutes)
|
| Steve Dower |
Unconference - 10:55: Contributing to Open Source, 11:40: Data Science Education,
The Secret Life of Python,
Panel Discussion: My First Open Source Contribution
|
| Tamar Yastrab |
The Echo-Chamber of Your Social Media Feed
|
| Thomas Caswell |
Stump the Chump,
Building a maintainable plotting library,
HoloViz and Matplotlib sprint,
HoloViz and Matplotlib sprint,
Jupyter & matplotlib sprint,
Jupyter & matplotlib sprint,
Matplotlib sprint,
Matplotlib sprint
|
| Thomas J Fan |
Deep Dive into scikit-learn's HistGradientBoosting Classifier and Regressor,
What's now in NumFOCUS projects? (Part 2)
|
| Tom Augspurger |
Scalable Machine Learning with Dask,
What's now in NumFOCUS projects? (Part 1),
Introduction to pandas
|
| Veronica Hanus |
To comment or not
|
| Will Kurt |
An Introduction to Probability and Statistics
|
| YOU! |
Unconference,
Unconference,
1:20pm-2pm: A Github for Data, 2pm-3:25pm: OPEN,
3:40pm-5pm: Role Play annotation facilitator training, 5:10pm-5:50pm: STUMPY & Time-series analysis,
Unconference - Using GPUs in Python/Julia/R Applications,
Unconference - 2:10: Software Engineering for Data Scientists, 2:55: Equity and Algorithmic Fairness,
Unconference - Native Extension Modules,
Unconference - 3:45: Design Thinking for Data Science, 4:30: Open
|
| Yuanqing Wang |
Using Graph Nets (GNs) to predict molecular properties
|
