We know that it takes more than a large data set and computer software to effectively solve problems. As information technology increases in capability and availability the opportunity to use data to develop and improve processes becomes even greater.
Particular attention is paid to the pedigree of the data: the process that generated the data, the measurement process and the data collection process including the sampling schemes used.
Also essential to success is the use of subject matter knowledge to frame the problem and assess and interpret the results of the analysis.
Why Should You Attend:
The role “Data Analytics” play in Pharma’s Business world today
How to get started in developing models from data
How to verify the prediction accuracy over time
Tips, Traps and guidelines for conducting successful data analytics studies
Areas Covered in this Webinar:
Importance of Data and Analytics in Today’s World
What is Analytics?
Developing Models – Getting Started
Model Verification - Developing Models that Predict Accurately over Time
What We’ve Learned
Understand “Building Blocks” of Data Analytics
How to Assess Data Quality … “Data Pedigree”
Strategic and sequential approach to developing prediction models
How to Validate Prediction Model Accuracy
Success Factors for Analytics Projects
Who Will Benefit:
Research and Development Scientists
Biologists and Microbiologists
Chemists and Chemical Engineers
Process and Manufacturing Engineers
Quality Assurance Personnel
Supply Chain Professionals
Anyone with a desire to learn the fundamentals of methodical performance improvement
Ronald D. Snee, PhD is Founder and President of Snee Associates, a firm dedicated to the successful implementation of process and organizational improvement initiatives. He provides guidance to senior executives in their pursuit of improved business performance using Quality by Design, Lean Six Sigma and other improvement approaches that produce bottom line results.
Prior to his consulting career he spent 24 years at the DuPont Company in a variety of assignments including pharmaceuticals. He has been developing and applying QbD methodologies for more than 30 years. His recent application and research on QbD has produced more than ten articles on use of QbD in Pharma and Biotech. He has also co-authored 2 books on the methods and tools of QbD and speaks regularly at conferences and meetings on the subject. He teaches QbD and related methodologies as an Adjunct Professor at Temple University School of Pharmacy and Rutgers University Pharmaceutical Engineering program.
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