In this 3-part series of live streams, you'll see Wolfram programmers solve problems in (pseudo) real time. You'll get to see programming in its raw form, from sourcing and importing different data types to dealing with large or messy datasets, to stumbling upon bugs and using hacky code! Each live stream will focus on a different problem and look at how it might be tackled with Wolfram Language, following a data science pipeline along the way. Problems may be solved completely or may simply be explored, and if an instructor gets stuck, you may be asked to jump in with suggestions!
In the case study used in this live stream, we will see if Wolfram Language can help us avoid traffic in London's morning rush hour. Along the way, we will encounter a few different paradigms and methodologies: we will call an API to find images from traffic cameras, do some machine learning to detect cars, apply some classical image processing on some geographics to create a graph of London's road network, do some graph theory to match cameras to nodes, and finally discover our least-busy path from home to work!
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