Better Code for Data Science
Essential success factors for making Data Science work.
Proper engineering is a key success factor for Data Science and AI in production.
Python offers several ways to achieve the required quality level for a successful implementation.
Beyond writing quality code, there are other aspects to consider such as personas, standardization and architecture
Presented at the PyData Global 2020 on Nov 12, 2020. PyData Global , one of the largest gathering of the data science and AI community worldwide.
The talk outlines the many aspects as:
- Principles for Data architecture, coding and deployment.
- Provides definitions of what makes code good or bad, pitfalls and anitpatterns.
- Programming skills matter for quality assurance
- Personas / backgrounds that have to be on the same page
Your Contact Person
Alexander Hendorf -
Alexander Hendorf is a renowned IT professional and expert in Big Data, Data Mining, Machine Learning and Artificial Intelligence. He is a frequent speaker at international conferences like PyData, PyCons or MongoDB World NYC.
After founding an independent label, Hendorf recognized the potential of digitalization for the music industry and began programming trading platforms and databases.
This combination of entrepreneurship and digitization is reflected in his consulting concepts. With a high level of expertise, he specializes in particular in process optimization through Agile Data Analytics and Data Value Assessments.
Hendorf is a Python Software Foundation Fellow, one of the chair persons of PyConDE and PyData Berlin, chair person of the German Python Softwareverband e.V and one of the 25 MongoDB Masters worldwide. Through his commitment to open source and his membership in corresponding global organizations, he also has an excellent international IT network.