Data analysis for bioprocesses

Important information: If you choose the accessible version of the 3D activity, please contact the support team to manually unlock the rest of the training.This module provides an understanding of the data generated throughout the bioprocess lifecycle, their role in continuous process improvement, and the associated visualization and statistical analysis techniques.It is based on the mapping of key data from each phase, visualization tools, quality monitoring indicators (CQA, CPP), as well as analysis methods applied to real-world cases to detect deviations or resolve production issues.Digital modalities : Activity 1 : eLearning/ Activity 2 : Serious Game 2D  / Activity 3 : Serious Game 3DTarget audience:Employees (technicians, engineers, and team leaders in bioproduction), support functions (process monitoring, maintenance, Quality Assurance, Quality Control) wishing to strengthen their skills in process digitalization and data valorization in a cGMP environment.Biotechnology students (Master’s level or engineering schools) wishing to gain an initial understanding of the challenges related to data management and analysis in an industrial environment.Engineers or data scientists in biology/biotechnology looking to explore the specific features and application cases in bioproduction.Evaluation procedure:A short evaluation at the end of the module confirming what you have learnt and enabling you to move to the next one ( 80% of correct answers required).

Important information: If you choose the accessible version of the 3D activity, please contact the support team to manually unlock the rest of the training.This module describes the main types of computerized systems (LIMS, ERP, SCADA, BES, MES, WMS, PLM) used at each stage of a bioproduct's lifecycle and how they structure and record production data.It is based on data flow modeling methods, identification of data collection and storage points, and analysis of interfaces between systems.Digital modalities : Activity 1 : Serious Game 2D / Activity 2 : Serious Game 2D  / Activity 3 : Serious Game 3DTarget audience:Employees (technicians, engineers, and team leaders in bioproduction), support functions (process monitoring, maintenance, Quality Assurance, Quality Control) wishing to strengthen their skills in process digitalization and data valorization in a cGMP environment.Biotechnology students (Master’s level or engineering schools) wishing to gain an initial understanding of the challenges related to data management and analysis in an industrial environment.Engineers or data scientists in biology/biotechnology looking to explore the specific features and application cases in bioproduction.Evaluation procedure:A short evaluation at the end of the module confirming what you have learnt and enabling you to move to the next one ( 80% of correct answers required).

This module explores the digital architectures used in bioproduction (cloud, on-premise, hybrid) and their implications for the value, accessibility, and reliability of industrial data.It is based on comparative evaluation of architectures, challenges related to system migration, identification of error or inconsistency sources, and strategies for optimizing the processing of production data in shared environments.Digital modalities : Activity 1 : eLearning / Activity 2 : eLearning / Activity 3 : Serious Game 2DTarget audience:Employees (technicians, engineers, and team leaders in bioproduction), support functions (process monitoring, maintenance, Quality Assurance, Quality Control) wishing to strengthen their skills in process digitalization and data valorization in a cGMP environment.Biotechnology students (Master’s level or engineering schools) wishing to gain an initial understanding of the challenges related to data management and analysis in an industrial environment.Engineers or data scientists in biology/biotechnology looking to explore the specific features and application cases in bioproduction.Evaluation procedure:A short evaluation at the end of the module confirming what you have learnt and enabling you to move to the next one ( 80% of correct answers required).

Important information: If you choose the accessible version of the 3D activity, please contact the support team to manually unlock the rest of the training.This module covers the principles of data governance and the implementation of standards to ensure data quality, traceability, and usability in a GMP context.It is based on frameworks such as FAIR (Findable, Accessible, Interoperable, Reusable) and ISPE GAMP, as well as practical methods for metadata management, data cleaning, normalization, and transformation within a structured framework.Digital modalities : Activity 1 : eLearning / Activity 2 :  Serious Game 3D / Activity 3 : Serious Game 3DTarget audience:Employees (technicians, engineers, and team leaders in bioproduction), support functions (process monitoring, maintenance, Quality Assurance, Quality Control) wishing to strengthen their skills in process digitalization and data valorization in a cGMP environment.Biotechnology students (Master’s level or engineering schools) wishing to gain an initial understanding of the challenges related to data management and analysis in an industrial environment.Engineers or data scientists in biology/biotechnology looking to explore the specific features and application cases in bioproduction.Evaluation procedure:A short evaluation at the end of the module confirming what you have learnt and enabling you to move to the next one ( 80% of correct answers required).

This module introduces statistical tools and their use through a practical case study illustrating several deviations impacting process capability and robustness.It is based on descriptive analysis methods (graphical representations, means, variability) and diagnostic analysis (correlations, root cause investigations), applied to real-life cases of optimization or problem-solving in GMP environments.Digital modalities : Activity 1 : eLearning / Activity 2 :  Serious Game 2D / Activity 3 : Serious Game 2DTarget audience:Employees (technicians, engineers, and team leaders in bioproduction), support functions (process monitoring, maintenance, Quality Assurance, Quality Control) wishing to strengthen their skills in process digitalization and data valorization in a cGMP environment.Biotechnology students (Master’s level or engineering schools) wishing to gain an initial understanding of the challenges related to data management and analysis in an industrial environment.Engineers or data scientists in biology/biotechnology looking to explore the specific features and application cases in bioproduction.Evaluation procedure:A short evaluation at the end of the module confirming what you have learnt and enabling you to move to the next one ( 80% of correct answers required).