Date:
6th–10th September 2021, Boston/Virtual
https://www.dataAdvanced Process Data Analytics Coursehow.ch/news/events/6th-data-analytics-course/
Aim:
The aim of this course is to provide an overview and advanced insight into data analytics and modeling methodologies for process data. Fundamental concepts to visualize high-dimensional and highly correlated process and product quality data, to identify the important process drivers as well as to forecast the process and product quality behaviour will be presented in lectures. Hands-on and brainstorming sessions will be used to solve case studies from the (biopharmaceutical) industry. After the course the participants will be aware of relevant techniques and literature for process data analysis and will be able to evaluate different analysis paths for a given problem.
Scope:
• 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
Who should attend:
The target group of the course encompasses scientists and engineers from academia and industry who encounter or are working with (big) process data. The course shall motivate to utilize the presented techniques in ongoing and perspective projects. Previous experience in data analysis can be advantageous but is not mandatory to follow the course.
Course fees:
The course fee depends on the format of that the events. For a virtual course it is CHF 1’750 (CHF 1’100 for academia and 450 for students). For an on-site event it is CHF 2’500 (CHF 1’500 for academia and 500 for students). A 20% fee reduction is offered if the optional pre-course is not attended. The fee includes lecture and case study summaries in electronic formats. For the virtual event it includes the cost of all communication platforms used for the course, in case of the on site event in includes all catering during coffee and lunch breaks and one social dinner. It also includes one free participation voucher (non-transferrable) for the Symposium on Digitalization and Big Data Analytics (https://www.datahow.ch/news/events/symposium/), preliminary scheduled in Zurich on 28 October 2021.
SWISSCAM members have a 5% discount in this course
To information in Brazil, please contact:
Paula Ambrogi
[email protected] | +55 (11) 99798 9525
Registration:
September: https://www.surveymonkey.de/r/2021c2
October: https://de.surveymonkey.com/r/course21EU
Principal Lecturers:
Michael Sokolov, Ph.D., COO of DataHow and Lecturer at ETH Zurich. Michael is an expert in bioprocess modelling and regular speaker on the potential of smart digital pharma solutions on
international conferences. He conducted his research in close collaboration with the pharma industry and co-authored more than 25 publications
Alessandro Butté, Ph.D., CEO of DataHow & Lecturer at ETH Zurich. Besides a long-standing research experience in polymer, separation and biotechnological processes, Alessandro has several years of experience in the pharma industry and a MBA from St. Gallen. He is a co author of more than 70 publications and 4 patents.
Moritz von Stosch, Ph.D., CIO of DataHow. Moritz is one of the leading experts for hybrid modelling of bioprocesses. He combines an academic career path with several years of experience in the pharma industry. He is a co-author of more than 30 publications on microbial and mammalian upstream as well as downstream processing.
Further Lecturers and Tutors:
Dr. Fabian Feidl, bioprocess digitalization expert
Dr. Nicolas Cruz, modeling and automation expert
Prof. Massimo Morbidelli, thought lead bioprocessing
Dr. Adam Szalkowski, IT infrastructure expert
Dr. Martin Luna, PhD, DoE and optimization expert
Dr. Harini Narayanan, machine learning expert
Please contact [email protected] in case of questions. Registration is only complete after payment or payment confirmation. Registration is bind-ing unless the minimum of participants cannot be reached.