Talks, Workshops

Talks

Talks should be 30 minutes long (including Q&A – 5 mins) and can be on any topic related to Python.

Workshops

Workshops are either 90 mins or 3 hrs long interactive sessions where attendees can learn by doing.

Format

• The format of each talk will be up to the speaker but should include a mix of lectures, demos, and Q&A.

• Workshops are aimed at beginner, intermediate, and advanced experience-level participants.

• Workshop proposals must include a detailed outline of the covered topics and allocated time.

Schedule

The full schedule with detailed information will be published separately.

What to Propose

Any topics of interest to Python programmers, with a focus on the following but not limited to:

• Lessons learned from using Python in projects, including unconventional and conventional wisdom and advice for similar problem-solving. E.g., I tried Python for video processing or in my medical imaging project, and here are the lessons.
• Contributions to improving the Python language/ecosystem, such as creating libraries or proposing optimizations.
• Knowledge gained from other languages, like type systems, functional programming patterns, or logic programming.
• Thoughts on tech culture, diversity and inclusiveness, the physical and mental health of programmers, productivity improvement, workplace issues, and their impact when using Python.
• Alternatively, consider offering teaching sessions on niche topics, recent technologies, or scientific papers, even at a beginner level.

Proposals can belong but not limited to any of the below topics:

1. Developer tools and automation - Tools and practices for automating development workflows and enhancing productivity like DevOps, Testing, CI/CD, Containers, Orchestration, Logging, and Monitoring, Selenium
2. Concurrency - Approaches to concurrent Python programming using multiple threads like Multiprocessing, Multithreading, Async
3. Core Python - Focused on understanding and mastering fundamental Python concepts and syntax like Documentation, Python Libraries, Language Features & Internals, Python Implementations
4. Culture and Society - Exploring cultural aspects and societal influences like Diversity, health, life, education, productivity, workspace issues, community, coding for causes
5. Ethics and Philosophy - Examination of ethical considerations and philosophical questions in technology like Govt. Policies, Responsible AI, Privacy and Bias.
6. Others - Other topics not explicitly covered in the above sections like Quantum Computing, Storage, Education, etc.
7. Cloud Computing - Using Python to leverage cloud-based services and infrastructure like Cloud services (AWS, GCP, Azure, etc.) for Python, IaaS for Python.
8. Blockchain - Understanding and developing blockchain systems and Web3.0 using Python.
9. Distributed Computing - Techniques for processing data across multiple machines or processes using Python like PySpark, Dask, Distributed ML (Horovod), MPI (Message Passing Interface).
10. Embedded Python and IOT - Using Python in embedded systems and Internet of Things (IoT) devices like MicroPython, Python on Hardware, Robotics, Arduino, and Raspberry Pi.
11. Desktop Applications - Design and implementation of applications for desktop environments using Python like Qt, GTK+, Tkinter, Gnome, KDE.
12. Web & App development - Building interactive websites and mobile applications using Python like APIs, REST, GraphQL, PyScript, Django, Flask, FastAPI, Kivy.
13. Networking and Security - Concepts and practices for secure, efficient networking using Python like Network Programming, Network Security and Encryption.
14. Game Design and 3D Modelling - Creation and manipulation of 3D models, AR/VR and principles of game design using Python.
15. Scientific Computing - Using Python to solve complex scientific and mathematical problems like Python usage in scientific computing and research. GIS, Mathematics, Simulations.
16. Data Science, AI & ML - Exploration and implementation of data science techniques, artificial intelligence, and machine learning algorithms using Python like Generative AI, Data Analytics & Visualisation, Data Engineering, Speech Processing, NLP, Computer Vision, MLOps and Others.