20-21 November 2019
Radisson Blu Edwardian Kenilworth,
Applying Big Data Analytics in Bioprocessing
Familiarise yourself with advanced statistical software to analyse Big Data from your bioprocess on this 2-day course.
Big Data solutions provide insights into how you can maximize the production of your biopharmaceutical, as well as information on how to reduce manufacturing time and costs.
During this 2-day course you will understand how Big Data collection and analysis can maximise the efficiency of your bioprocess and you will learn how to overcome common barriers to the implementation of Big Data solutions, such as maintaining data integrity and quality. You will gain hands-on experience with analytical tools including multivariate analysis and linear regression models, as well as advanced tools such as machine learning and hybrid models to aid in the analysis of Big Data. These sessions will be tailored to your experience by a pre-course survey sent out to you closer to the start date.
What will you learn?
Importance of Big Data
Understand the motivation for collecting Big Data during the bioprocess and where it can be generated.
Overcome the challenges that prevent the implementation of Big Data in bioprocessing
Learn about the different statistical techniques that can be used to make sense of Big Data sets, such as Linear Regression Models
Machine learning and hybrid modelling
Discover Machine Learning and Hybrid Modelling software and how they are used to analyse large data sets
Understand how Big Data can be used to streamline a continuous manufacturing process
Future of the industry
Learn how the future of the industry is likely to evolve with regards to Big Data in bioprocessing
Hands-on demonstrations of statistical software will be tailored to the level of expertise determined by a pre-course survey
Who is this course for?
This course is relevant for professionals wishing to further their understanding of Big Data and the challenges associated with it. Relevant departments include:
- Process engineering
- Upstream processing
- Downstream processing
- Cell line development
- Process development
- Quality control/assurance
- Data quality
- Data management