Inside Job: Secret Histories in the National Museum
Codon
DECEMBER 8, 2024
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Perficient: Drug Development
DECEMBER 8, 2024
In the ever-evolving landscape of digital marketing, data is the key to success. Salesforce Marketing Cloud (SFMC) provides a wide array of tools to help marketers harness the power of data, and at the forefront is Analytics Builder. This robust tool enables businesses to gather, analyze, and act on insights to optimize campaigns and improve customer experiences.
Drug Patent Watch
DECEMBER 8, 2024
The pharmaceutical industry is heavily reliant on patents to protect intellectual property and maintain market exclusivity. However, with the increasing competition from generic and biosimilar manufacturers, patent invalidity claims have become a significant challenge for pharmaceutical companies.
Speaker: Simran Kaur, Founder & CEO at Tattva Health Inc.
The healthcare landscape is being revolutionized by AI and cutting-edge digital technologies, reshaping how patients receive care and interact with providers. In this webinar led by Simran Kaur, we will explore how AI-driven solutions are enhancing patient communication, improving care quality, and empowering preventive and predictive medicine. You'll also learn how AI is streamlining healthcare processes, helping providers offer more efficient, personalized care and enabling faster, data-driven
Perficient: Drug Development
DECEMBER 8, 2024
In todays competitive marketing landscape, data is king. Salesforce Marketing Cloud (SFMC) empowers marketers with two powerful tools Reports and Intelligence Reports to measure, analyze, and act on campaign performance. These tools are essential for transforming raw data into actionable insights, helping businesses make informed decisions that drive success.
Quanticate
DECEMBER 8, 2024
The approach to handling missing data in clinical trials has evolved over the past twenty years, particularly regarding methods for incorporating missing data to produce more comprehensive results. Issues surrounding missing data are of particular importance, due to the risks of introducing bias and losing statistical power, creating inefficiencies and detecting false positives (Type I Error).
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