📊 Science, Data, and Analytics

Data and number crunching, analysis and visualisation, machine learning, and how those things affect us human beings.

Talks in this track:
  • Science Data & Analytics Open by Maia Sauren, Javier, Rachel, Genevieve Buckley, Ned Letcher
  • Fair game: the ethics of telco fraud by Laura Summers
  • High performance machine learning with JAX by Mat Kelcey
  • Classifying Audio into Types using Python by Jyotika Singh
  • Graph Data Science by Paco Nathan
  • Time Series Forecasting Techniques in Python by Gajendra Deshpande
  • A Beginners Guide to GPUs for Pythonistas by Marlene Mhangami
  • Improved Decision Making with Pareto Fronts by Eyal Kazin
  • Systems of the world: cataloguing the world's data for great good by Lars Yencken

  • The Science, Data, and Analytics specialist track focuses on the use of Python in data analysis, scientific programming and machine learning as well as the human side of data and it's management. If you’re processing and understanding data, be it statistical analysis, visualisation or machine learning then there’s a plethora of Python based tools available to you. This track is for people doing things in the areas of data analytics, data science industry, working with data in academia, or generally interested in using Python to analyse and do things with gain insights from your data. Alongside the shiny tools and techniques, we will also focus on the importance of data management, team collaboration, and the societal impacts that data can have


    Genevieve Buckley she/her • @DataNerdery • Dask
    Maia Sauren she/her • @sauramaia • ThoughtWorks
    Ned Letcher he/him • @nletcher • ThoughtWorks
    Javier Candeira he/him • @candeira
    Rachel Bunder she/her • @ADuckIsMyFiend • Servian