Better Code for Data Science
The coding quality must be improved in Data Science and AI. Though code in notebooks may provide some working solutions, too often it’s hard to maintain, reuse or even to read for an outsider - apart from being inefficient. In this talk I will share a decade of Python experience and provide background, good practices and guidelines that enable everyone to write proper code for Data & AI.
Alexander CS Hendorf
Alexander’ professional career was always about digitalization: starting from vinyl records in the nineties to advanced data analytics nowadays.
He’s a PyData organiser (Frankfurt and Südwest), a Python Software Foundation fellow, emeritus program chair of EuroPython, PyConDE & PyData Berlin 2019/20 and the scientific Python conference EuroSciPy. He’s one of the 25 mongoDB masters and a regular contributor to the tech community. As regular speaker at international conferences in he love to talk about, discuss and train tech. Being a partner at Königsweg - a boutique Data Science and AI consultancy based in Mannheim, Germany - he’s advising and training industry clients in AI, data science, data literacy and big data matters.