Use-Case Talk Series: Best Practices in Data Science Projects
(hosted by Aspaara Algorithmic Solutions AG)
What are the best practices in Data Science projects? We will gather and share knowledge about reproducibility of results, practical guidelines for data acquisition, algorithm development, computational analysis and how to organise data science tasks in a modular manner.
Besides technical practices we also touch business related ones: How to define key question, how to test and validate models on a regular basis?
For registration please write an email (Name, affiliation and position) to us. The number of places is limited!
Alexander Grimm, CEO Aspaara Algorithmic Solutions AG
16:15 Use-Case Talk 1: Is Fair Benchmarking Possible? (25 minutes talk + 20 minutes QnA)
Dr. Thomas Herrmann, Dozent ZHAW IDP
17:00 Live Demonstration (10 minutes Demo + 5 minutes QnA)
Dr. Holger Rommel, ti&m, Head Research & Digital Transformation
17:15 Break (15 minutes)
17:30 Use-Case Talk 2: Learning from the data science trenches (25 minutes talk + 20 minutes QnA)
Marc Schöni, Microsoft, Technical Specialist for AI & Advanced Analytics
18:15 Apéro and Networking
We look forward to seeing you at the Use Case Talk Series 2019!
If you have any question or concern please do not hesitate to contact us.
Talk 1: Is fair benchmarking possible?
Abstract: Benchmarking is both a curse and a blessing: on the one hand it is essential to assess its own position compared to those of the competitors, on the other hand benchmarking is also used to set target in order to increase productivity and efficiency. In my presentation I will show a quantitative method how to make fair comparisons to identify best practice and how to avoid unrealistic targets that can otherwise lead to demotivation and lower productivity.
Speaker: Thomas Herrmann studied mathematics at the Swiss Federal Institute of Technology (ETH) and subsequently worked intensively on timetable creation and new operational possibilities in the field of traffic management for railways during his dissertation. Thereafter, he worked in the private sector for more than 12 years until summer 2018. During this time he conducted several studies in the field of infrastructure management for roads and railways, including benchmarking. Now he is a lecturer at ZHAW, at the Institute for Data Analysis and Process Design (IDP).
Talk 2: Learning from the data science trenches
Abstract: Delivering data science projects is top of mind for many organizations. But how do you ensure that projects are successful and what should you be cautious about? Come to this talk to hear from Marc Schöni about this.
Speaker: Marc Schöni holds a Master’s degree in Data Science and works as a Technical Specialist for AI & Advanced Analytics with Microsoft in Switzerland. On a daily basis, he engages with customers to help them identify suitable first use cases and the most appropriate technologies for successful implementation.