Call for Suggestions
Earlier in the year, we asked for suggestions of what people would like to see at this year's conference.
If you're stuck for an idea, wondering whether what you've got to say will be popular or just generally curious, here are the responses to that consultation...
What talk/workshop topics would be useful for people new to your field?
- Advanced plotting in Python
- All the things you can do with Python
- Beginner/Intermediate Pandas
- Binding Python to C++
- Contributing to open source sprints
- Cryptography guidelines/good practices
- Cybersecurity and Python
- Embedding
- Gentle introduction talks (i.e "How to get started with...")
- Getting started with common web frameworks
- Graphing & Plotting
- GUI tool chains
- How people fail in python
- HTTP - the basics
- Internationalisation
- Introduction to debugging
- Introduction to django
- Introduction to Heroku
- Introduction to microservices
- Introduction to numpy
- Introduction to web development
- Introduction to XML parsing
- Introductions to circuit Python and/or micropython
- Licencing Issues
- Machine learning, deep learning, natural language processing
- Making games
- Multi threading
- OpinionatedPandas usage
- Overview of PEP8
- Overview of PyPI
- Overview of unit testing
- Packaging/Distributing
- Privacy in online social networks
- REST and other APIs - the basics
- Search and language processing basics
- Strengthening the python community through partnerships and networking
- Testing techniques
What talk/workshop topics do you think will be most important in your field in the next 12 months?
- AWS
- Artificial Intelligence
- Bayesian modelling in python
- Beginner pen testing
- Biopython
- Chatbots
- Concurrency / Async
- Continuous Integration
- Data Analysis
- Data Visualisation
- Data analytics
- Data science
- Database performance enhancements with Python connectors
- Deep learning
- Django
- Docker
- Embedded Pthon and IoT applications
- Graphical / animated content in Jupyter notebooks
- How do we protect our privacy and develop tools using Python?
- Kubernetes
- Machine Learning
- Machine Learning with IoT data
- Mathematical Modelling
- Micropython
- Natural Language Processing
- Neural networks in tensor flow
- Operating Kubernetes
- Packaging and Distribution
- Pandas internals
- Persuading, (possibly non-technical), management to let you use Python.
- Practical applications of word embeddings / deep language models.
- Project management
- Project/schedule visualisation (capital/complex projects)
- PyTest
- Pyspark
- Python community networking
- Python for cyber security
- Python on ARM processors
- Reproducible research
- Sensitivity Analysis
- Sharing code for scientific experiments
- Simulation
- Software development designs, best practices and tips.
- Test Driven Development in infrastructure as code
- Time series analysis
- Type decorations
- Use of hackable hardware
What would be the ideal talk/workshop you would use to persuade somebody to attend PyCon UK for the first time?
- Anything machine learning
- Beginner / Intermediate Pandas in a business context
- Best practice for python installations
- Binding Python to C++
- Creative Hardware Hacking using Circuit Python
- Data Science for everyone
- Data manipulation and analytics
- Data science cross over
- Deep Learning
- Exploring user, lesser-known aspects of common libraries
- Games
- Historical development of Python from a technical and a sociocultural perspective
- How to create and upload a package on PyPI
- How to get useful results quickly with data science
- How to write a talk
- How to write your documentation
- Implementing a neural network or machine learning model from scratch
- Interactive data visualisations
- Interesting projects people have done with Python
- Intermediate Python techniques
- Introduction to Object Oriented Python
- Introduction to Python
- Matplotlib workshop
- Natural Language Processing
- Numpy / numpy for finance
- Powering up your Python from beginner towards intermediate
- Python data engineering
- Python for Machine Learning
- Python for the digital humanities (for beginners)
- Python games
- Python internals
- Reproducible research workflow
- Something big data related - probably spark
- Something related to making (micro:bit, rpi, circuitpython etc...)
- Something to really show off the sheer breadth of Python
- Standard library hidden gems
- The power of Machine Learning with Python
- Workshop on streaming data
What would be the ideal talk/workshop you could use to request funding to attend PyCon UK?
- AWS
- Advanced Python deployments
- Anecdotes of "mistakes I made and how to avoid them"
- Anything - a tool, a technique or a practice - that helps me to be a more productive programmer
- Computational Creativity
- Cross-platform Bluetooth with Python
- Data science for software engineers
- Game making
- Handling large matrix operations in Python
- Image analysis / processing in Python
- Kubernetes
- Machine Learning
- Machine learning
- Microservices
- MongoDB
- Natural Language Generation
- Non-Django web development
- Python for complex (£1bn +) projects
- Reporting and adhoc analysis from spreadsheets using Python
- Research in deep learning
- Security hacking skills for beginners
- Security software development in Python
- Set up a Continuous Integration / Continuous Deployment pipeline in Python
- Speeding up data processes
- Test Driven Development in infrastructure as code
- The commercial advantages of Machine Learning
- The impact of the Python ambassadorial program in Africa
- Using Async Python in a real project
- Working with big data (i.e. doesn't fit in memory) in Python
What do you most wish somebody had explained to you when your first started out?
- All the things you can do with Python
- Benefits of automation
- Career paths in data science
- Code optimisation
- Community dynamics
- Data Science is all about the quality of results, not cool techniques
- Data parsing
- Decorators and context managers
- Don't listen to people who act like they know more than you
- Even though you think you understand something technical, HOW to recognize that you actually don't fully understand it, and HOW to ask specific questions that can help you.
- How to benefit from code review
- How to read documentation and interact with web resources
- How to read error messages correctly
- How to write helpful logs that will allow you trace bugs later
- How to write things that run fast
- How to write unit tests that won't be a stone around your neck when you have to refactor
- How virtual environments work
- It's OK to feel stupid
- It's easier to refactor your code than write it "perfectly" the first time
- It's much easier to work with something concrete you've written down than trying to think your way to perfection
- Learn by doing - open source datasets - contributing more helps to improve writing good quality code
- Machine Learning
- Packaging
- Python 2 - Python 3 differences
- Python best practices
- Real-time data processing(low latency)
- Sets and other less used containers
- Test Driven Development
- That not everyone is well-intentioned.
- That you dont have to be a certain kind of person to be a developer
- The pain is in the maintaining of code not the creating it
- Use of package managers
- Using Pythons magic methods
- Unittest.mock
Which areas would you be most interested to see covered in this year's schedule?
We offered five choices:
- Web Development
- Data Science
- Education
- Research
- Communitty
and the opportunity to add further suggestions.
Of the five main choices, the most popular was Data Science with Web Development a close second. However, all five were popular - even the least popular option was selected by more than one third of respondents.
The additional suggestions were:
- Basic engineering
- Children's activities
- Creative Machine learning
- Data Visualisation
- Embedded Python
- Ethical Hacking
- Gaming
- HR, ethics and career development
- Micropython
- OpenCV
- Python core language