30th March 2019
Data Science (Past, Present, Future)
Currently, data science is the most discussed topic in every segment of industry. The core of data science is about uncovering findings from data to help us make smarter business decisions, i.e. deep diving into the granular level to mine and understand complex behaviours, trends and inferences. Due to the generation of vast data, coupled with advancement in technology, the data science stream has been evolving at a fast pace. If we consider data science as a function of time, then it is interesting to understand where it came from and the direction in which it is headed.
Specific to the pharma industry, there is a growing pressure to improve overall productivity and reduce development time to maximize the period of patent protection so that R&D costs can be recovered. The drug development process is extremely complicated and multidisciplinary and therefore no overall model serves as a basis for optimisation and improvement. Pharmaceutical companies are generating increasing amounts of data and, as a result, the value of the data scientist. There is a growing belief that data science and approaches from engineering and computer science will be critical for the long-term future of the industry. Across the industry, expectations on using technology-driven solutions are increasing and investments in applying artificial intelligence, machine learning and automation to drug development use cases are also taking a leap. While technology is making progress, the skills requirement of the workforce is also going to evolve. The industry has started talking about data managers being tomorrow’s data curators and statistical programmers being data analysts; and all this will lead to building skills to meet forthcoming job roles. There are visionary organisations moving with agility towards embracing these changes and preparing for the future work environment.
AI in Pharma – Myth or Reality! – Sowmyanarayan Srinivasan, Accenture
Reaping The Fruits of Clinical Research Tree Using AI and ML – Dinesh Pillaipakkamnatt, Sineflex Solutions
The Use of Data to Find Efficiencies in Drug Development – Getting the Most out of Wearable Technology in Clinical Research – Supriya Deshmukh, Syneos Health
Building Bridges – The Paradox of Big Data in Pharma – Aruna Chakraborty, MMS
Evolution of Statistical Methodologies in Clinical Trials – Namita Shinde, PPD
Allegory of Prudent Data Science – Subbiah M, Acroama Gnan Vikas
Observational Study: Journey of Film Without Dialogues in Superhit Movie – Seema Shah, Cognizant
Data Science in Pharmaceutical Industry – An Introduction – Shveta Natu & Ganpatiai, Quanticate
Explainable AI – Moving AI from Black Boxes to Glass Ones – Munshi Imran Hossain, Cytel
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