Sufficiently advanced testing + analytic web apps: Python Adelaide and friends!

This month, Python Adelaide is joining Sydney Python, Melbourne Python Charmers, Canberra PUG, and Perth Django and Python Developers for an online event!

You can watch it online at these links:

https://www.youtube.com/watch?v=K7eDJT7djro
https://www.facebook.com/pythonadelaide/videos/271312207225950/

Sufficiently Advanced Testing
Zac Hatfield-Dodds

Writing tests is a great start - but property-based testing libraries like Hypothesis can help you find bugs you didn't know were possible!

There are even more advanced techniques out there, like symbolic execution, fuzzing, metamorphic relations, and delta-debugging. Come find out how they work, why you'd use them, and change the way you think about testing!

Zac is a Hypothesis core developer and Pytest maintainer, and often speaks about advanced testing at Python conferences. He is employed by the Australian National University in a mixed research-and-teaching position, and leads the software component of his institute's Masters program.

Analytic Web Apps in Python: A data superpower
Ned Letcher

Interactive interfaces for exploring and working with data are valuable across a broad range of analytic contexts, including business intelligence dashboards, interactive reports, tools for dataset exploration, and custom tools supporting the machine learning lifecycle.

In the last few years, there has been a flurry of open source Python frameworks that facilitate the rapid construction of data-oriented web apps. Notable examples include Plotly Dash, Voila, Panel, and Streamlit. These libraries enable you and your team to more rapidly prototype, develop, and deploy analytic applications without needing to bring in a frontend technical delivery team or spin up and maintain a modern web development stack yourself. But how do you choose between all these libraries?

I will take the audience on a whirlwind tour of popular Python libraries in this space, presenting real-world applications that they have been used to create, outlining features that differentiate the frameworks, and identifying contexts that each is potentially well-suited for. This talk will be useful for individuals, team leads, and decision-makers looking to make an informed decision about libraries to add to your toolkit for rapidly producing data-oriented applications.

Ned is a data scientist and software engineer who has helped a range of organisations in projects involving machine learning, natural language processing, information retrieval, and data visualisation. Ned has been using Python for data analysis, visualisation, machine learning, and web development for over 10 years. Ned is a contributor to the Plotly Dash library and an active member of the Dash community.

Ned has a PhD in computational linguistics from the Natural Language Processing group at the University of Melbourne. He also has a Bachelor of Arts (philosophy and linguistics) and a Bachelor of Science with Honours (computer science). Ned regularly presents at local meetups and organises the Melbourne Data Visualisation Meetup.