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  • admin 9:53 am on November 17, 2015 Permalink
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  • admin 9:52 am on September 21, 2015 Permalink
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    Why Science’s Loss is a Gain for Data Science 

    A few years ago the role of Data Scientist was described as the sexiest job of the 21st Century. In the big data jungle Data Scientists are the apex predators, and yet they are relatively few in number. They have been described as being as rare as unicorns, with estimates that by 2018 the US alone will experience a shortage of 190,000 skilled data scientists (a shortfall of approximately 60%). More recently the number of job postings for Data Scientists rose by 57% for the first quarter of 2015, with searches for Data Scientists growing by 73.5% in the same period.

    So what are these mythical beasts? Part of the problem is that the Data Scientist skill-set is not very clearly defined, and there has been a lot of debate as to what makes a Data Scientist. Generally speaking they have a strong knowledge of maths and statistics, good programming abilities, a familiarity with a wide and varied range of data analysis techniques (particularly in machine learning), plus excellent problem solving skills. Perhaps the most important characteristics are an intense curiosity and inherent creativity, traits that are hard to measure.

    Until recently there haven’t been any formal training pathways to become a Data Scientist. Most Data Scientists come from backgrounds in statistics or computer science. However, while these other career paths develop some of the skills listed above, they typically don’t cover all of them. Statisticians are very strong on the maths and stats side, but generally have weaker programming skills. Computer scientists are very strong in the programming arena, but typically don’t have as strong a comprehension of statistics. Both have good (yet different) data analysis skill sets but can struggle with creative problem solving, which is arguably the hardest skill to teach.

    In the last couple of years a number of post-graduate courses (and even a couple of undergraduate degrees) have popped up around the world. However, it will take a few years before the graduates from these courses trickle out into the work force. How will we meet the projected demand in the meantime? At least part of the solution may come from an unexpected source: astronomy and astrophysics.

    Modern day astronomers generally have a really good mix of most (if not all) of the skill sets sought after in a Data Scientist. They have a very good knowledge of maths and statistics, are highly computer literate and proficient with at least one programming language (Python being the current favourite). They have a very wide and diverse range of high-level data analysis skills, and are exceptional creative problem solvers (astronomy research is a bit like sitting in Sydney and trying to solve a murder mystery in London using only a pair of binoculars, so creativity and lateral thinking are a necessity). Many also have experience working with high performance computing and parallel processing.

    What astronomers are typically lacking is experience in some of the standard software packages used in industry and some of the specific techniques that are commonly used (e.g. machine learning and graph analysis), as well as industry knowledge. However, the obtainment of a PhD (a requirement for professional astronomers) demonstrates the ability to become a world expert in a complex and highly technical field in a short period of time, so rapidly obtaining additional skills and expertise is unlikely to be an issue.

    What would entice someone to give up astronomy and shift to a career in Data Science? Scientists are pretty poorly paid compared to other professionals, and they are almost certainly going to significantly increase their salaries by moving to industry. Job security is perhaps the biggest enticement, as most astronomers (64% in Australia) are on fixed-term contracts between 1-3 years duration, often with no chance of renewal. The typical astronomy career path includes a number of these short fixed term positions, often involving moving countries in between jobs. For the lucky ones, this path leads to the coveted permanent (i.e. tenured) position at a university or research organisation, but there are far fewer permanent jobs than there are highly qualified applicants.

    Perhaps the biggest inducement is the increasingly poor outlook globally for science research funding (and therefore job opportunities). Australia has had it pretty good in recent years, with a significant spike in astronomy funding in 2011 with the Super Science initiative. This has contributed to a dramatic increase in Australian professional astronomy community (up 26% from 2005-2014), including a 69% increase in PhD student enrolments. At the same time though the number of ongoing (i.e. permanent) astronomy positions only increased by 3% over the same period. Some astronomers will move to non-research technical positions and still stay connected to astronomy, but the majority (>90%) will end up leaving the field.

    This is sad for science but a golden opportunity for industry. Astronomy alone won’t plug the gap as there are only ~10,000 professional astronomers world-wide. However, the same arguments in favour of why astronomers would make good Data Scientists can also be applied to the other hard sciences (biology and chemistry) as well as other areas of physics, which are also facing funding cuts globally. Perhaps it is providence that at the same time that we are facing a global shortage in Data Scientists we are seeing a surge in very intelligent people with highly transferable skills looking for alternative career paths. The shortage of Data Scientists won’t last forever, but while it does perhaps at least some of the data analytics MasterChefs that we are looking for can be found studying the wonders of the Universe.

    Sean Farrell is a Data Scientist in the Advanced Analytics group at Teradata ANZ. Based in the Canberra office, Sean is responsible for supporting Teradata’s engagement with the Australian Federal Government. He spent 11 years working as a professional astronomer before moving to Data Science in search of greener pastures.

    The post Why Science’s Loss is a Gain for Data Science appeared first on International Blog.

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  • admin 9:52 am on April 27, 2015 Permalink
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    Teradata Delivers Big Analytics Innovation for Life Sciences 

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  • admin 9:46 am on January 22, 2015 Permalink
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    Creating a Data Driven Advantage in Life Sciences 

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