PyDataMCR - Online Code Night
ONLINE CODE NIGHT - GET HELP or GIVE HELP
Working on a project? Want to meet other data professionals? Want to help others with their projects?
Join us on our online code night! A night to work on your own project, with supportive peers available for advice or teach somebody else something great!
WHAT YOU WILL NEED: A laptop or desktop
Slack Access: PyData MCR Slack (tinyurl.com/pydatauk-slack) Slack Channel: #code_night
A project (optional!, feel free to join us for a chat or to provide others with advice) - feel free to register your problem, or help you're able to provide on the google doc in our slack channel
HOW IT WILL WORK: Event Starts (18:00) - Come and say Hi in the #code_night channel, let everyone know what you're working on
During Event - Put any questions or conversation topics you want to discuss to the PyData community, its likely someone will be interested and have an opinion!
If you can help others - start up threads, or even slack channels (Call them #cn_ to make it clear for anyone else joining).
Event Closes (20:00) - Come back next month to enjoy some more!
EVENT GUIDELINES: PyDataMCR is a strictly professional event, as such professional behaviour is expected.
PyDataMCR is a chapter of PyData, an educational program of NumFOCUS and thus abides by the NumFOCUS Code of Conduct
Please take a moment to familiarise yourself with its contents.
ACCESSIBILITY: Under 16s welcome with a responsible guardian.
SPONSORS: Thank you to NUMFocus for sponsoring Meetup and further support
HOW DO I GET STARTED?
Don't have python?
Download Anaconda (https://anaconda.org/) or use Google Colab (https://colab.research.google.com/)
Don't have a project?
You can always help others in the Slack Channel
Look for coding challenges at https://leetcode.com/
Look for data challenges at https://kaggle.com/
A variety of kaggle problems that might be fun for you: - The classic - kaggle.com/c/titanic/data - Image Recognition - kaggle.com/google/google-landmarks-dataset - NLP - kaggle.com/google-nlu/text-normalization