How is Big Data changing the face of Biotech/Pharma?

Need of Big Data in Pharmaceuticals?

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.

Universities across the globe

USA

Why USA?

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:

• High rate of job satisfaction.
• Increasing job opportunities for data scientists.
• Salary compensation with years of experience.
• Possibility of remote working.
• The language of communication and education is English.

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.

Universities offering Masters in Data Science in US

• The George Washington University – School of Engineering & Applied Science
• Tufts University – Graduate School of Arts and Sciences
• New York University New York City
• University of Miami
• Georgia Tech University
• University of Minnesota Twin Cities
• North Carolina State University
• University of Texas, Austin
• ‘University of Southern California
• University of Colorado Boulder
• University of Maryland, College Park
• Rutgers University

Eligibility to study Masters in Data Science in the USA?

• A four-year undergraduate degree with corresponding CGPA which is university specific
• GRE for Universities that have not yet waived off its requirement
• Test of English as a Foreign Language for International Students TOEFL or IELTS
• Letter(s) of Recommendation
• Statement of Purpose
• Official Transcripts
• Subjects required in undergraduate curriculum: Mathematics/Statistics and Computer Science

UK

Why UK?

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:

• Home to several AI and Big Data based companies making in-house recruitment more favorable.
• The steep rise in demand for data science experts is supported by turnovers of several million pounds as seen over half of the decade.
• The average salaries of data scientists have also increased substantially making it a suitable destination after US.
• Most importantly, the courses offered in the UK at the Masters level are more versatile. This means there are more options and combinations to choose from for a data science enthusiast without losing the biotech/pharma niche. Depending on the course, there are one-year degree courses available too. However, not all courses might be same in terms of duration; one crucial factor here is to scan through the course curricula carefully before application(s).
• The language of communication and education is English.

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:

Universities offering Masters in Data Science in UK

• University of Edinburgh
• King’s College London
• University of Manchester
• University of Glasgow
• University of Sheffield
• University of Sussex
• University of Southampton

Eligibility to study Masters in Data Science in the UK?

• An undergraduate degree which satisfies university requirements
• For international applicants, a higher second class (minimum 60%) is good to go but better grades are never a bad idea if financial aid requirements are present.
• For the UK, GRE is usually not recommended as an essential admission pre requisite.
• TOEFL or IELTS scores as test of English as a foreign language.
• Letter(s) of Recommendation
• Official Transcripts
• Statement of Purpose/Research Proposal
• Subjects required in undergraduate curriculum: Mathematics/Statistics, Computer Science and/or Business (course-specific)

Canada

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.

Why Canada?

• One of the most immigrant friendly nations.
• Comparatively lower tuition fee and living costs (as compared to the USA and UK).
• Data Scientists are one of the best paid professionals.
• Canada Big Data Consortium predicts a serious hike in data scientists over the next few years with more than a million jobs awaiting them.
• Statistics claims that data scientists would probably surpass software engineers across several verticals. This is exactly why the need for them is growing exponentially in the pharma sector. Information and Technology Council reports data science graduates are in high demand in the top job sectors in Canada, pharmaceuticals being one of them.
• The language of communication and education is English.

The course duration for a Masters in Data Science and Analytics is generally between one to two years.

Universities offering Masters in Data Science in Canada

• The University of British Columbia
• University of West Ontario
• University of Toronto
• University of Montreal
• University of Alberta
• University of Calgary
• McGill University
• Western University
• Trent University
• Thompson Rivers University
• University of Waterloo
• Saint Mary’s University
• HEC Montreal
• Carleton University

Eligibility to study Masters in Data Science in Canada?

• An undergraduate degree which satisfies university requirements
• For international applicants, a higher second class (minimum 70%) is preferred.
• For Canada, GRE requirement is university-specific. Not many universities ask for it but one needs to check entry requirements at university websites. Also, some recommend GMAT.
• TOEFL or IELTS scores as test of English as a foreign language.
• Letter(s) of Recommendation
• Official Transcripts
• Statement of Purpose
• Subjects required in undergraduate curriculum: Mathematics/Statistics, Computer Science and/or Business (course-specific)

Big Data in Biotechnology

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.

Job Outcomes after Masters in Data Science

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:

• Data Scientist
• Data Analyst
• Machine Learning Scientist
• Database Administrator
• Machine Learning Engineer
• Data Architect
• Management Consultant
• Business Intelligence Analyst
• Marketing Analyst
• Analytics Consultant
• Data Engineer
• Statistician
• Database Developer

Crossover between Biotech/Pharma and Big Data

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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