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PyDataBerlin 2018 : PyData Berlin 2018


When Jul 6, 2018 - Jul 8, 2018
Where Charite Campus Berlin
Submission Deadline Mar 31, 2018
Notification Due Apr 30, 2018
Final Version Due Jul 6, 2018
Categories    python   data science   machine learning

Call For Papers

Conference: PyData Berlin 2018 on July 6-8, 2018

Talk Proposal Deadline: March 31, 2018

Dates: Fri July 6: tutorials; Sat+Sun July 7-8: main conference.

Venue: Charite Campus, Berlin. Address: Augustenburger Platz 1, Berlin 13353 Germany

Important dates:

Call for speakers/submissions open on February 5th
Submission deadline on March 31.
Acceptance notices will go out by May 15.
The program will be published by May 18.
To submit a talk proposal: go here

PyData brings together analysts, scientists, developers, engineers, enthusiasts and others from the data science community to discuss applications of new tools and techniques within data science. Our talks often include data management, analytics, visualization as well as new machine learning approaches including statistical and neural network approaches.

Topics: PyData welcomes presentations focusing on a variety of languages from Python to R, Julia and Scala. (See a detailed list of languages and frameworks included, but not limited to the keywords below.) In particular, PyData introduces talks that concentrate on either data science or Python, or both.

To see the type of topics presented at previous PyData events, please look at our past conference sites at or check out the videos on

Format: Presentation content can be at a novice, intermediate or advanced level. Talks will run 30-40 minutes and hands-on tutorials will run 90-120 minutes.

Accepted speakers receive a free ticket to attend the conference. Travel expenses will not be paid; but we do have a diversity travel program and encourage speakers with diverse backgrounds to apply for travel funding.

Non-profit: PyData is a volunteer-run conference. It supports open source development by donating all proceeds to NumFOCUS, a non-profit organization that supports the development of open-source tools, such as Numpy, IPython, Jupyter, and many others.

Open Source: As a reminder, PyData presentations are intended to share knowledge and experience. To this end, we encourage the code and/or data that your talk relies on to be open-source. Ideally, the audience would have access to the necessary tools to reproduce the results of the talk. Also, we welcome talks focused on your own practical application of tools and concepts either at work or in your free time, but discourage sales oriented proposals whose sole aim is to sell a product.

Your Submission: In our experience, attendees pay close attention to proposal abstracts when deciding which talks to attend during the conference. The submitted abstract will be published as is in the conference program (you can edit the submission later).

We encourage you to include details about the theory and/or practice that you will discuss. Specifically, if the system you've built uses open source tools, please mention the libraries in the proposal and make it clear whether you will be presenting a case-study of their use or if you will discuss details of their design.

In general conference attendees, as well as the review committee, should be able to answer these questions based on your submission:

What problem is your talk addressing (are you talking about a well known problem or have you found something new during a project)
Why is the problem relevant to the audience
What is(are) your solution(s) to the problem, or are you simply pointing out the fact there is an issue we should be aware of (this is also extremely useful)
What are the main takeaways from your talk. For tutorial submission this is extremely important, please specify what people will have learned at the end of the tutorial session.
You can check out the following submissions from the last year’s conference as a reference for some good examples.

Irina Vidal Migallon

Matti Lyra

Trent McConaghy

Diversity & First-Time Speakers: If you are interested in presenting a talk or tutorial, we encourage your submission(s). We especially encourage first-time speakers and submissions by underrepresented members of the community. Towards that goal, we have created a PyData Berlin Mentorship program available to local and remote attendees and speakers. The program aims to help first-time and diverse speakers and attendees by pairing them with mentors who may offer advice and feedback on talk ideas or questions about the conference. Interested in learning more? Please check our Diversity Program page, where you can also apply to be a mentor or mentee.

Diversity Travel Sponsorship: As a volunteer-run and non-profit supported conference, we cannot offer travel sponsorship for speakers. However, as part of our diversity program, we do have a limited number of travel scholarships for attendees and speakers. You can learn more about the scholarship on our Diversity Program page, where you can also apply for a scholarship.

If you or your company is interested in helping increase diversity at PyData Berlin, we encourage you to reach out at as we have several sponsorship opportunities available.

Submission Process: After you submit a talk proposal, the PyData committee will review the proposals and communicate any needed feedback or improvements. We aim to include many first-time speakers, and therefore will attempt to communicate and iteratively help you improve the abstracts. Closer to the CfP close date, the PyData Berlin mentorship program will run a workshop for improving your submissions, and closer to the conference date, we will try to find the best mentors for your abstract to help you improve your talk.


Data Visualization
New libraries
Python fundamentals
Data Mining / Scraping
Massive data
Devops: Pipelines, Deployment, Scalability, Packaging
Jupyter / Notebooks
Databases and ETL
GIS / Geo-Analytics
Natural Language Processing
Computer Vision
Predictive Modelling
Python in Social Sciences
Neural Networks / Deep Learning
Unsupervised ML
Theory of machine learning
Statistics in ML
Ethics of Machine Learning (Privacy, Fairness, …)
Transparency in ML / Interpretable Models
Reproducible Science
Functional Programming
Use Cases
Best Practice
To submit a talk proposal: go here

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