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Applications of Data Science

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ADAM AMOS: So to me, data science is anything where you can’t immediately derive an answer from a dataset, where there’s gonna be some level of analysis has to be performed to draw links between the data that aren’t otherwise immediately apparent. What has led to the rise of the data science in the last decade? I think a big factor’s gonna be mobile phone technology. What’s come out of mobile phones is the ability not just to look at cat videos on the internet, but a whole raft of electronics and software that allows us to collect significantly more information that we have ever been able to do before.
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That’s opened the door to being able to perform analysis on a scale that’s never been done before. It’s a no-brainer that we basically seen the last ten years a huge uptake in data science along with mobile phones. I would say to somebody who’s just getting started in data science is pick something you’re interested in. There’s a lot of interesting datasets out there to go and have a look at. The fastest way to build your skills is to take on a project and try and work out what can you bring that’s new to the data science. There’s no rules, there’s no barriers to entry. It’s really just how much you can do with the data you’ve got available.
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I’ve seen significant applications of predictive analytics in anywhere in mining where we’re delivering bulk materials to sites on regular basis. We’re able to forecast based on what’s happened previously with an extremely high degree of accuracy from only a handful of inputs.

Moore’s law has proved accurate for the last 50 years; however, many argue that it will not be applicable in future, as we may not be able to compress the number of transistors inside a microprocessor at the same rate in an economical way.

In the last decade, the commoditisation of storage and computation has led to significant reductions in costs. Further, advancements in cloud computing have made it easier to store large amounts of data and provide access to advanced computational power at a relatively low cost.

All of these factors have led to the rise in the use of data science in organisations. Technology companies, such as Google, Facebook, LinkedIn, Amazon, Microsoft, UBER and Netflix, are at the forefront of using data science. Most organisations today use data to derive information in some form and have different levels of analytics and data science maturity.

Additional Resources

Best jobs in America. (2020). Retrieved from https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm

Donoho, D. (2015). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766.

Data scientist: The sexiest job of the 21st century. (2020). Retrieved from https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century

Davenport, T. H. & Patil, D.J. (2020, October). How to take on the data science career path right after college? Retrieved from https://www.datasciencesociety.net/data-science-career-path-after-college

Patrizio, A. (2020, 3 December). IDC: Expect 175 zettabytes of data worldwide by 2025. Retrieved from https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html

Schwab, K. (2017). The fourth industrial revolutionUK: Penguin

Data is the new oil? No, data is the new soil.
David McCandless
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