Collaborative Data Science in Business - Ioannis Mesionis
Oct 27, 2023
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Ioannis Mesionis, an expert in collaborative data science in business, discusses topics such as collaborating with business stakeholders, optimizing digital marketing, agile project management in data science, and finding time for hands-on work in data science.
Collaboration between data scientists and business stakeholders is crucial in solving business problems using data products and analytics.
Agile methodologies, such as sprint planning, daily stand-ups, and demos, play a significant role in data science projects.
Continuing education and self-learning are essential for data scientists to enhance their skills in both technical and non-technical areas.
Deep dives
Johannes' Career Journey in Data Science
Johannes shares his career journey, starting from a bachelor's in mathematics to postgraduate studies in data science, and how he discovered data science through the Sherlock TV show.
Johannes' Role as a Lead Data Scientist at EasyJet
Johannes explains his role as a lead data scientist, partnering with business stakeholders in digital customer and marketing departments to solve business problems using data products and analytics.
The Agile Process and Methodologies in Data Science Projects
Johannes discusses the agile process and methodologies used in data science projects, including sprint planning, daily stand-ups, and demos, highlighting the importance of adjusting language and explanations for non-technical stakeholders.
The MLOps Zoom Camp Course and its Benefits
Johannes shares his experience taking the MLOps Zoom Camp course, which provided him with the opportunity to gain more hands-on experience in the productionization side of data science and learn about tools like Prefect and Evidently.
Recommended Resources for Effective Communication and Learning
Johannes recommends watching YouTube videos by Casi Cogicor on communicating technical concepts to non-technical audiences and suggests books like "Pattern Recognition" by B. Bishop for a deeper understanding of machine learning mathematics.