JMP, (a SAS company) since 1989, has been transforming data analytics through its interactive visualization and robust statistics, leading to insight driven advancements. Scientists and engineers’ harness JMP to propel green energy breakthroughs, expedite cancer therapies, and build high tech space probes. JMP, pronounced 'jump,' embodies a leap in innovation and interactivity.

This workshop will equip you with a sound theoretical understanding of the various statistical and analytical techniques for pharmaceutical sciences, complemented by hands-on exercises using JMP's Statistical Discovery software to reinforce your learning

• A Supportive Learning Experience: The workshop, led by experts from JMP division of SAS, will provide immediate assistance and respond to your queries. Both the theory and practice of various statistical applications will be explained, demonstrated with well-chosen examples and case studies, and then implemented by participants in the JMP software’s environment.
• . Teach and Research: Whether you are research scholar or an instructor, this workshop will enable you to effectively embed statistical techniques applied for pharmaceutical industry with JMP into your curriculum.
• A Powerful Addition to Your CV: This workshop offers the opportunity to develop a solid theoretical foundation and gain valuable hands-on experience with JMP's statistical discovery software. Those who atend this workshop will receive a certificate, serving as evidence of your learning.
• Add Value to Your Work: The application of statistical techniques may expedite your R&D process and scientifically aid in identification of key variables and enable critical resource decisions, thereby saving time and money, and providing a competitive edge in the market.
• Use and Communicate Data: Learn new ways to visualize experimental data, uncover fresh insights, and enhance the impact of your presentations and research communications with key stakeholders.

•  Comprehend and Apply Statistical Thinking: Understand the principles of statistical thinking and apply them to the pharmaceutical sciences, including the use of Design of Experiments (DOE) and Quality by Design (QbD).
•  Analyse Experimental Designs: Analyse and interpret various types of experimental designs, including factorial experiments, screening experiments, response surface methodology (RSM), and mixture or formulation designs.
•  Evaluate and Create Predictive Models: Evaluate the outcomes of experimental designs using prediction, simulation, and optimization techniques and create robust models for design space and risk assessment.
•  Understand and Apply Biostatistics: Comprehend the fundamentals of biostatistics, including descriptive and inferential statistics, and apply appropriate statistical tests (T Test, ANOVA, Chi squared test, nonparametric tests) based on data type and research question.
•  Synthesize and Evaluate Data Visualizations: Synthesize data into meaningful visualizations using charts, plots, graphs, and maps, and evaluate these visualizations for effective communication of research findings.

Design of Experiments (DOE) and Quality by Design (QbD) - DOE Essentials, Factorial Experiments, Screening Experiments, Response Surface Methodology (RSM), Mixture/Formulation designs, Modern Experimental Designs, Prediction, Simulation, Optimization, Design Space and Risk Assessment

Biostatistics and Data Visualization - Types of data, Descriptive statistics, Inferential statistics, check for normality, Which test when? T Test, ANOVA, Chi squared test, Nonparametric tests, Correlation and Regression
Data visualization: Charts, Plots, graphs, and maps

• Assistant and Associate Professors - Pharmaceutics, Pharmacology, Quality Assurance and Pharmaceutical Analysis.

The program will be delivered through a combination of lectures, interactive discussions, case studies,and hands-on exercises.