📊 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:
  • 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
  • Cataloging the world's data for great good by Lars Yencken
  • Football (soccer) data analysis: A pedagogic introduction by Indranil Ghosh

  • 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


    Javier Candeira he/him
    Rachel Bunder she/her


    Genevieve Buckley she/her


    Maia Sauren she/her


    Ned Letcher he/him


    The Science, Data, and Analytics track is proudly sponsored by Thoughtworks.