Big data has proven to be extremely advantageous in the healthcare and pharmaceutical domains. Right from drug discovery to marketing, healthcare is largely dependent on big data not only to identify valuable resources but also to integrate it into their foundation. This decreases the time taken to consolidate datasets from various sources while increasing accuracy in the field of pharmaceutical research and development. From a business perspective, big data enables more informed decision-making with which companies can develop efficient medication with negligible side effects in the long run. Biotechnology: medicinal or pharmaceutical is based on a dynamic system that is constantly undergoing change. Each aspect of biology starting from molecular to cellular to anatomical to medical contains big amount quantitative data. To examine, analyze and utilize such data, one requires computational interference. Biotechnologists need to actively employ programing tools to carry out their research, generate and update drug repositories to improve medication and health holistically.
Data Science one of the hottest markets in context of analytics. Biotech-Pharma is no exception when having to realize the power of big data. Various factors contribute to the choice of USA when it comes to pursuing a Masters in Data Science:
A Masters degree is the USA would require two years. Barring the top and elite colleges which includes Harvard, Yale, Stanford, Duke, Pennsylvania, Columbia and others, there are several Tier 2 and/or 3 universities in the USA for Masters (MS) in Data Science/Analytics.
The second most sought-after destination for data science and analytics is the UK. The Dynamics of Data Science as reported by the Royal Society claims that the need for data science graduates have increased by several folds, including in the pharmaceutical and biotechnological sector. The factors contributing to the same are:
In the UK, a Masters degree could be anything between one to two years. Beyond Oxford and Cambridge, there are several top ranked universities in the UK as well which offer Masters in Data Science and Analytics. Also, the system is a little different than in the US. Universities offering Masters in Data Science in the UK are:
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From a realistic point of view, Canada is by far one of the top study abroad destinations for international students because of the job prospects it offers. Also, the economy is flourishing in the pharmaceutical sector because of its extremely collaborative nature of research and development.
The course duration for a Masters in Data Science and Analytics is generally between one to two years.
Biotechnology and pharmaceuticals rely on Big Data just as much as it depends on wet lab research. Although it is a common belief that biotech enthusiasts are less comfortable with coding algorithms (which is not completely untrue), there are a million possibilities of switching into data science masters with a biotech background. One piece of advice to aspirants willing to make it into Data Science and Big Data is to (please) have Mathematics and Computer Science in your electives during Bachelors. Not just for Data Science, this also gives an edge to shift to diverse business-oriented careers in future.
With a Masters in Data Science and Analytics, one can become a pivotal part of the Biotech-Pharma world with their knowledge and expertise. The jobs mentioned below are not mutually exclusive; there might be some overlap of responsibilities depending on one’s expertise and interest. Top job outcomes for Data Science graduates are:
The basic research that biotechnology is based on still remains the same. The fundamentals of biology cannot change. The traditional methods of protein engineering by recombinant DNA technology or genetically modified organisms to generate products of medicinal value still hold. Big Data steps in before and at a more sophisticated stage where the basics are sorted and the application of basic bench work is required (which is the ultimate goal). Also, it helps answer basic questions by narrowing down research into a couple of programming tools in parallel. Predictive modeling is one such example. Another example would be the human genome: the utmost need of computational data has enabled gene sequencing accelerate at an exponential rate with individualized genome sequencing results. Precision medicine also relies on Big Data to have better prognosis in terms of personalized treatments.
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