Welcome to PyCon India 2020 CFP
The major change this year is the online format of the Conference.
There are 2 categories of Proposals - Talks and Workshops. You can select the category from the 'Proposal Type' Dropdown in the Proposal Submission Page. Here is a brief overview of both Categories.
Talks
Technical talks are the most important event at PyCon India, the core of the conference essentially. Two of the four days are dedicated to talks. Talks are short lectures (25 min slot) supported by a presentation. Speakers come from the Python community.
This year we have introduced a preview video section where you need to add a short preview video mentioning the overview of the Talk. To know read the CFP announcement blog post
Talks are selected through a CFP (Call For Proposals) process. Interested members of the community propose their talks. An editorial panel designated by the organisers makes the selections. The 2019 edition of the conference saw some 303 proposals, of which 36 were selected.
CFP applications from the previous years can be seen here.
Important Points
- There are three parallel tracks
- Talk duration is 30 mins (25 mins for the talk, 5 mins - Q&A)
- CFP for Talks is closed.
- Schedule shall be released on 15th September 2020
- Talks will be presented on 2nd and 3rd October 2020
Workshops
Workshops are an important part of the PyCon India. Hands on learning is as important as talks for a fulfilling conference experience. Like talks, workshops are also conducted by the members of the Python community.
Anyone can propose for conducting a workshop. Any topic of interest to the Python community is okay - the workshop should help attendees learn a new skill, technology or library. To get a sense of the topics from last year, take a look here.
In the CFP proposal, please mention an outline of the workshop and the prerequisites. And the slides if possible. Also mention if you have conducted the same workshop before.
The workshop should be interspersed with proper hands-on exercises. After the workshop people should be ramped up on the workshop topic, and should be able to take it forward themselves.
To know about Best practices please check the Workshop Proposal Announcement blog post.
Important Points
- Workshop duration is 2.5 hours (A small break in between as planned by the speaker)
- CFP for Workshops closes on 31st August 2020 23:59 UTC+05:30
- Schedule shall be released on 15th September 2020
- Workshops will be conducted on 4th October 2020
What to Propose
Anything of interest to Python programmers is welcome. However, there are a few topics that we feel might be great -
- Lessons from using Python in your project. Did you find something against conventional wisdom? Something confirming conventional wisdom ? Do you have advise for people solving similar problems? Example - I tried Python for video processing, or in my medical imaging project, and here are the lessons.
- Something you're doing to make the language/ecosystem better. Writing a library to solve an interesting problem ? Or have some new ideas on optimisation.
- Something you learned from a different language that may be useful to Python community. How about a type system? Or patterns from functional programming. Or logic programming maybe?
- Thoughts on tech culture and living. Ideas on improving diversity and inclusiveness. On programmers’ physical and mental health. On getting better at productivity. On workplace issues. Anything that can make an impact, especially if you have used Python for any of the above or have seen someone using Python.
And if you don't get any ideas along these lines, try plain and simple teaching. Pick up an niche topic (maybe a recent technology, or a scientific paper), and help us learn. A well delivered lecture even at a beginner level is often well received.
Guidelines for creating Preview Video
With PyCon India 2020 being online this year, we are introducing preview videos to our proposal submission workflow. Preview videos are you talking about your proposal, topics you intend to cover in the talk, and how you intend to cover them. Participants are strongly suggested to upload links of their preview videos while submitting their proposals. It will be immensely helpful for reviewers to go through preview videos and take that into account before making final decisions on your talk proposal. Please keep in mind to strictly follow all guidelines for creating your video preview as mentioned below:-
Duration & orientation
A 1 to 3 minute video to be recorded, preferably in portrait mode.
Upload to YouTube
The video has to be uploaded on Youtube. If you don’t want other people to find it, mark it as unlisted. Don’t mark it as private or disallow embedding, or we won’t be able to see it.
Only you talking about your talk
The video should contain nothing except you talking about your talk. Try to make a video that holds our attention and helps us understand more deeply about what's your talk is all about.
No effects & No Music
Please do not add any background music to your video. No screenshots or postproduction wizardry please; we don’t want this to turn into a video making contest. If you’re going to spend time making something cool, put that into your slides & proposal instead.
No Script
Please do not recite a script written beforehand. Just talk spontaneously as you would to a friend. People delivering memorized speeches (or worse still, reading text off the screen) usually come off as dull and uninspiring. Think of this as fun activity and let your heart do the talking. Be vanilla!
Check Audio
Try to keep your voice clear and check that its being recorded properly. Make sure there isn't any background noise. Try to record this in an empty room if possible without much going on in the background.
The Review Process
- Authors should propose their talks using the CFP application
- CFP volunteers review the proposals for completeness
- Once the proposals are ready, they are be reviewed by a panel of experts
- If the proposal does not look complete, or the reviewers need clarifications, the author is notified via comments
- The panel of experts finally vote on the proposals
- A pre-final shortlist is eventually prepared based on the votes
- The shortlisted proposals go through a round of rehearsals (more details in section below)
- A final list is created and published.
Rehearsals
Shortlisted speakers will be expected to participate in rehearsal sessions. Rehearsals will be done via teleconferencing, where the speaker shall give a mock run of their talks in a time-bound manner. The audience will consist of volunteers, reviewers and possibly other speakers. The speakers will be given feedback if necessary.
The point of this exercise is to make sure speakers are ready with their talks ahead of time. And also, to make sure they can finish the talk in the stipulated time. It is useful for the speakers too as they'd get feedback on the content delivery and presentation.
Participation in the rehearsal sessions is likely to be a required step - chances of an unrehearsed talk making it to the final stage are substantially lower.
Diversity
We in the Python community believe in making our community more diverse. This means we are encouraging content from diverse walks of life. This also means we want to improve participation from under-represented groups.
Our goal is to maximise content from under-represented groups. You can help us by encouraging your friends, family and colleagues to submit talks. You can also help by mentoring.
Also note that we have a strict code-of-conduct. This is to make it clear, in intent and practice, that we are committed to making the conference a pleasant, welcoming and harassment free experience for everyone, especially for under-represented groups.
Best Practices for Speakers
1. Apply
Even if you have a vague idea, submit a proposal. We're available for help with ideas and feedback (contact information is in the section below). Don't worry about communication skills or English - we are there to help with that too. And our focus is more on the content.
2. Make it detailed
Add as much detail as possible to the proposal. Add the presentation slides if you already have one. Add a short minute video giving a summary of the proposal. More detail helps reviewers make better judgement.
3. Propose early
We will start the review process as the proposals come in, and not at the end. Proposals submitted early will get more attention and feedback
4. The code of conduct
Take a look at the code of conduct, and be mindful of it. The gist is, be nice and avoid using sexist language.
5. Add a preview video
Add a small intro video about what your talk is about to provide a preview to what's to be expected.
We've put together a set of detailed best practices - take a look. It also contains links to some well written proposals from previous years.
Notes:
- Please do not share any confidential information in your proposal. The PyCon India team will not be liable for any intellectual property related to your content. Do get proper approval as required from your respective organisation for your content involved.
- All the artifacts from selected talks are to be made public under creative commons license post conference.
- The content you submit for your talk will be visible to the volunteers and reviewers of the PyCon India Review and CFP working groups.
Questions and Discussions
Ping us on Zulip or IRC (#pyconindia) Or contact the coordinators through email:
Anirudha - anirudhastark@gmail.com
Ritesh - udr.ritesh@gmail.com
The team: cfp@in.pycon.org
Proposal Sections
- Decentralised and Distributed Technology - Blockchain, Tangle or any DLT protocol or infrastructure related product.
- Others - Everything else that may be of interest to the audience.
- Desktop Applications - Qt, GTK+, Tkinter, Gnome, KDE, Accessibility
- Scientific Computing - Python usage in scientific computing and research. GIS, Mathematics, Simulations
- Game Design and 3D Modelling - Python in developing games, 3-D modelling and animation
- Culture and society - Diversity, health, productivity, workspace issues, privacy, community building, coding for causes
- Embedded Python and IOT - MicroPython, Python on Hardware, Robotics, Arduino and Raspberry Pi
- Networking and Security - Network Programming, Network Security and Encryption
- Web development - Web, Apis, Microservices
- Developer tools and automation - Testing, CI/CD, Containers, Orchestration, Logging and Monitoring
- Data Science, Machine Learning and AI
- Core Python - Language Features, Python Implementations, Extending Python and Standard Library, language internals.
Proposal Types
- Talks
- Workshop
Selected Proposals
Talks
2 0
3. How to make continuous integration work with machine learning
4 2
4. Predicting the Traffic Jam: Congestion-Aware Routing
7 0
5. Narrative-focused video games development with Ren'Py, an open source engine
6 11
11. How to build a production-ready distributed task queue management system with celery
3 1
13. A deep dive and comparison of Python drivers for Cassandra and Scylla
4 103
15. Discrete Probability Distributions: Learning & Testing using tools from Fourier Analysis
2 0
17. Towards a more transparent AI - Decrypting ML models using LIME
5 7
21. Pythonic Interfaces: The secret to building maintainable, quality code!
2 4
26. Resource Utilization as a Metric for Machine Learning
2 2
31. Building a Home Automation system using PyQt, MQTT and Arduino
0 2
33. Reproducible Scalable Workflows with Nix, Papermill and Renku
7 26
34. Combining NLP with Structured Data to map Clinical Entities to the relevant Section Headers in a Clinical Document
Workshop
0 111
2. Meme Saheb - Using Dank Learning to Generate Original Meme Captions
8 1
7. Python for Computational Social Science with Text, Networks and clever data games
List of Proposals
5 3
3. Developing a match-making algorithm between customers and Go-Jek products!
5 1
5. Haptic Learning: Inferring anatomical features using Deep Networks
0 4
8. What happens behind execution of an `import` statement?
3 0
13. Weeping and Gnashing of Teeth:Impact of Deep Learning in the Health Sciences.
10 12
14. Low Dimensional Embeddings in Language Processing
2 2
15. Managing your data science project environments with Conda (+pip)
2 1
16. Deploying Computer Vision Model as API using AWS Lambda and API Gateway
2 2
21. Beyond one-hot encoding: Boosting your model performance
2 7
22. Designing a production-grade realtime Machine Learning Inference Endpoint
2 7
23. Deploying a Machine Learning Inference Server in Production
3 1
29. Identifying “Gender Roles” based Biases and Language in Educational Texts
5 3
30. Effective Development Patterns for Micro-services
3 1
31. Reduce hardware costs in Internet of Things using Python
4 1
33. Crawling your own Dataset for Research using Python
1 4
34. Python for Signal Processing, Communication and Cryptography
2 2
38. Flask Modular Flow: Going Beyond The Factory
1 0
41. Building a data pipeline for processing and storing Twitter data on AWS with Python
4 3
45. Introducing DataSwissKnife - A tool to automate data science operations
3 1
50. Ensemble-X: Your personal strataGEM to build Ensembled Deep Learning Models for Medical Imaging
1 5
52. Understanding Data Science behind the Movie "Moneyball" using Python
3 2
53. Concurrency in Python : from Beginner to Pro
3 1
60. Distributed Storage using Interplanetary File Systems using Python
5 1
65. Using ReactJs & MongoDB with Flask - Introducing the FReMP Stack
3 30
66. SurveilPy - Writing a Monitoring library to display metrics of a remote server on Terminal UI from scratch
4 0
73. [Hands-on]Buidling and training your own neural network with Tensorflow 2.0 and Python
4 4
76. Detecting fraudulent ads in Online Classified sites using Python
7 0
79. Kedro + MLFlow – an open-source integration with Hooks
4 0
84. Serverless - how to speed up tests over 300 times and achieve continuous feedback?
2 1
85. What about make it easier? An introduction about building WebMaps with Django and ArcGIS API for Python
0 1
86. Exploring the Frontiers of Facial Recognition : Applications and Challenges
0 0
87. Applied Machine Learning in Python using scikit-learn, mlxtend and pandas
2 1
90. Lossy and Lossless Compression : Journey Into the compressed universe
2 0
91. Monitoring Python Application KPIs using Prometheus
9 3
92. Porting your Python web app to serverless on AWS in 30 minutes 🐍
2 0
95. Test Driven Development in the real world: background tasks, time travel, mocking
0 2
98. Build and deploy PyTorch models with Azure Machine Learning
2 2
101. UNet (and its variants) for Biomedical Image Segmentation
1 0
104. Simplified Deployment of Microservices from Docker Desktop to Cloud
3 11
106. Building a Production Ready Search Engine using Python and Elasticsearch
6 0
111. Autonomous Vehicles See More With Thermal Imaging: Multi-modal thin cross section Object Detection
4 1
112. Revamp your DevOps,MLOps game: Apache Airflow to orchestrate smart workflows
2 1
113. Eliminating network connections with Python
2 1
115. Building Serverless APIs with Python and Azure Functions
2 6
116. Hands-on Workshop training in OpenCV-Python based Image and Video processing using Google Colaboratory
3 2
118. Explaining Convolutional Neural Networks using Class Activation Maps
1 1
119. Power of Recommender System: Statistical models to Deep Learning models
2 0
120. Writing production-level Machine Learning code from the gecko with least effort
2 0
122. Natural Text to Speech for Indic languages
4 0
125. Don't be Afraid of Async - Automate Mundane Tasks using Discord.py
5 0
126. Getting the best out of GPUs for Deep learning using Python
2 0
127. AI vs. PY (How Python can save the day against the Robot uprising)
0 6
131. Efficient and Optimal Deep Learning Inference for Computer Vision Applications
2 0
132. Managing the lifecycle of a python microservices application by extending kubernetes.
0 1
138. Microwave Image Processing: Exploring SAR images from Foundations to Frontiers through Python
4 6
142. Powering the web with Django : Best Practices and Deployment
0 0
144. Setting up Productive Dev-Instance using Docker-Compose
2 6
145. Travel Time Prediction for Indian Metro Cities using Open Datasets
0 6
146. Jupyterhub - serving python notebooks at all scales. Why we at HackerEarth went for this to provide notebooks as a code editor for our users?
1 0
148. Polyglot data with python: Introducing Pandas and Apache Arrow
1 1
150. Mastering a data pipeline with Python: 6 years of learned lessons from mistakes to success.
10 1
152. Unlock an organization’s knowledge using machine comprehension
2 0
153. Intelligent Bot - chatBot for Data Centre Automation
2 0
157. Named Entity resolution for beer SKUs using Python
4 3
158. Integrating teams by implementing a Design System
3 3
159. EatRight - Building a personalised app for healthy eating using Machine Learning and Python
3 0
161. The Hitchhiker's Guide to test factories for testing your applications.
1 1
163. Introduction to PyNUFFT (Python non-uniform fast Fourier transform)
1 0
165. IoTPy: an IoT platform to sense and respond to critical events.
1 0
166. Acidic, basic or neutral - an evaluation of Pyodide for running your Jupyter Notebook in the browser
1 2
169. Automatically generate test-cases – Schema-based API testing
5 0
171. Building Search Experience for a Python Web Application
0 0
172. Building Search Experience for a Python Web Application
5 0
173. Building scalable and robust Data Science solutions - A Developer's perspective
6 0
183. Competitive Analysis of the Top Gradient Boosting Machine Learning Algorithms
0 3
184. Scalable, Efficient Serverless Data Pipelines using Apache Beam on Google Cloud
0 11
190. Understanding UMAP: The Internal Workings of a State-of-the-Art Clustering Algorithm
0 0
196. An intro to Distributed computing for emerging ML applications with Ray
3 2
198. Developing an emotion capturing app with Python on Azure
2 1
199. Building a Video Indexer Software with Python and Azure Cognitive Services
4 1
201. Deploy PyTorch models in Production on AWS with TorchServe
3 2
203. Build amazing application using "Streamlit" for machine learning projects
2 2
206. Meet quantum computing, a name you will be hearing a lot in the future.
3 0
207. DevOps for Data Science? - automate the boring stuff and leverage the OSS ecosystem
2 0
209. DataOps: automate machine learning process using DevOps principles
2 0
211. Tensorflow For the Web : Converting Python Machine Learning Models to Javascript using TFJS Converter
0 -1
216. AWS Greengrass, an edge - cloud integration using python and Raspberry Pi
8 1
218. Computational Social Science with Python, and how Open Source transforms Academia and Research
3 45
219. Pygeneses:- Simulating complex behaviour in artificial agents
4 0
221. Building web UI using Bokeh-Python (for my AI/ML based Python Application)
2 2
222. Amazon Inspector findings API Build by Python API
2 1
224. How Python Helped Me to Start Contributing to an Open Source Project.
0 0
225. FACE RECOGNITION USING LBPH ALGORITHM AND BLINK DETECTION
2 54
232. Real Time Monitoring Of A Large Distributed Cloud Based Financial System Using Python & Slack
3 1
233. A blessing and a curse for developers: The mystery of git
1 0
235. Intel's Data-Parallel Python Initiative to Prepare Python for Next-generation xPUs
1 0
237. Introduction to Spiking Neural Networks with Python
1 0
238. Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images
5 1
239. Context Based Deep Learning and Models in a Niche Domain: Artwork Management
5 0
240. Interactive "UI automation and Annotated Data Generation For training Deep Learning models" using Python + Jupyter + Selenium
0 0