DataHow Events & Training

Meet us at our next event or join one of our regular trainings and webinars

15th June, 2023

Full day: ETH Zurich

DataHow’s 3rd Symposium will bring together industry experts, academia, and innovation providers to discuss the progress of digital innovation in Biopharma. We will discuss the status of digitalization in the industry, and understand what technologies are available and how these may help.

The themes which will be covered will include: 

• The role of data in the (bio)pharma business model  
• Technological possibilities and limits of the digitization of pharmaceutical processes 
• The role of quantitative tools and hybrid models 
• Key technologies and solutions 
• The perspective of regulators 
• Successful industrial implementations 
• Ideas and findings from academic research 
• A look into the future 

12th, 13th and 14th June, 

@Zurich, Switzerland 

Join us over 3 days and learn from DataHow's leadership team who are leading innovators in model-based bioprocess development.

The aim of this course is to provide an overview and advanced insight into data analytics and modeling methodologies for process data. The course scope includes:

• Special analysis techniques for process data
• Introduction to multivariate data analysis
• Introduction to machine learning techniques
• Hybrid process modeling based on process data and process know-how
• Model-based process understanding & optimization
• Model-based process monitoring and forecasting
• Application of techniques to industrial use cases

19-21st April,  26-28th April

Virtual Event: 6 x half day 

Join us over 6 half-days and learn from DataHow's leadership team who are leading innovators in model-based bioprocess development.

The aim of this course is to provide an overview and advanced insight into data analytics and modeling methodologies for process data. The course scope includes:

• Special analysis techniques for process data
• Introduction to multivariate data analysis
• Introduction to machine learning techniques
• Hybrid process modeling based on process data and process know-how
• Model-based process understanding & optimization
• Model-based process monitoring and forecasting
• Application of techniques to industrial use cases