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    Pharma Analytics – I Have All These Data, Now What Do I Do?

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    Website http://bit.ly/2lyuIms | Want to Edit it Edit Freely

    Category Pharma Analytics webinar, pharma data analytics, quality assurance in pharma, Data Pedigree, Validate Prediction Model Accuracy, Pharma Compliance Training

    Deadline: March 21, 2017 | Date: March 21, 2017

    Venue/Country: New Hyde Park, U.S.A

    Updated: 2017-02-21 18:00:35 (GMT+9)

    Call For Papers - CFP

    Overview:

    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

    Success Factors

    What We’ve Learned

    Learning Objectives:

    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:

    Executives

    Department Managers

    Quality Engineers

    Research and Development Scientists

    Biologists and Microbiologists

    Chemists and Chemical Engineers

    Process and Manufacturing Engineers

    Quality Assurance Personnel

    Supply Chain Professionals

    Accounting Professionals

    Anyone with a desire to learn the fundamentals of methodical performance improvement

    Speaker Profile:

    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.

    For more detail please click on this below link:

    http://bit.ly/2lyuIms

    Email: referralsatcomplianceglobal.us

    Toll Free: +1-844-746-4244

    Tel: +1-516-900-5515

    Fax: +1-516-900-5510


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
    Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.