What are the applications of data science and machine learning in the pharmaceutical industry?
Every day your company is coming across lots of data and you have to do something with that to get effective insights that will help you and your company grow.
Example of how data science and analytics saved companies million dollars
- $100 Million, Southwest Airlines saved by reducing the time its planes stood idle on the tarmac.
- The amount of fuel, in gallons, that UPS saved by optimizing its fleet was 39 million
Data science is one of the most growing professions and is required in every industry. Data science is considered as one of the most exciting and challenging career paths for professionals.
A company with lots of incoming raw data needs to put that data in use by analyzing it and finding the right insights that will benefit the business. To get into any industry’s analytics domain, an individual needs to have a good knowledge of analyzing large amounts of data, data mining, and programming skills.
“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
- Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics
When it comes to pharmaceutical industry data science is used in a very advanced manner and the emergence of Machine Learning and Artificial Intelligence helped in various domains. Such as
- Predictive analytics:
a) Drug companies are using predictive models to search for large virtual databases of molecular and clinical data.
b) Pharma companies employ predictive methods to determine which consumers and doctors are most likely to use a drug and create more targeted marketing efforts on the ground.
c) Accelerate drug discovery and development process: Applying predictive analytics to the parameters of the search will help them enhance the relevant knowledge and also gain insight into the avenues that are likely to produce the best results.
- Real-Time Monitoring:
Companies now monitor real-time data from trials to identify safety or operational risks and find problems.
- Patient follow-ups:
Sophisticated at-home devices, smartphones, and health apps, monitoring a patient’s health and that is possible with AL and ML algorithms
- Optimize and improve the efficacy of clinical trials:
Clinical trials are expensive and time-consuming to run and pharmaceutical companies want to make sure they have the right mix of patients for a given trial.
Big Data can help determine the best patients to participate in a study (by examining demographic and historical data), remote monitoring of patients, evaluating past clinical trial incidents, and even helping to detect possible side effects before they become a reality.
These are some of the uses of data science in Pharmaceuticals Industry and I hope this motivates you to start your Data science Journey
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You can use the link to check out courses from CloudXLab https://cloudxlab.com/
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