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Data Science Trends and Peculiarities

Data science is on the up. It’s the next big thing. Between 2011 and 2012, data scientist jobs increased by 15,000%. That’s due in large to the use of big data, with 64% of organisations surveyed in 2013 using big data solutions to optimise their performance. Without big data, there would be no data science. Previously, organisations got by via data mining alone, but data science has since become an umbrella term that includes mining as well as a host of other tools. Essentially, it’s mining and analytics on a more advanced level.

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Big Data vs Data Science

Big data is the information that businesses collect and hoard, with the anticipation of analysing it. Whilst organisations themselves may be inherently capable of amassing huge swathes of information, they are not all that savvy when it comes to understand and interpreting it.

This is where data science come in. In the world of buzzwords, big data vs data science means that big data is the big, brash, conglomerate of usefulness and outright junk that we refer to as data. As the name suggests, it’s simply a swimming pool of debris. Some of it has value, some of it doesn’t, but all of it can be downright unfathomable to the layman.

Data science is much more scientific and rewarding – as the name might suggest too. A data scientist will sift through the swimming pool of big data to make some sense of everything, highlighting trends and peculiarities, and at the same time optimising a businesses potential and performance.

Data Mining vs Data Science

Data science vs data mining is a discussion that is trending owing to the success of data science. Data mining, to put simply, is – metaphorically at least – fishing. A data miner will dip into a huge swathe of big data to pull out various bits and pieces. The problem is that data mining has no real purpose because it doesn’t real know what it’s looking for. Moreover, mining is not analysing.

Data science, on the other hand, has more purpose, a sense of what it’s looking for, and is capable of business data analysis. It seeks to bring to the surface useless data before making some sense of it. Less brutish than mining, it’s a more sophisticated method of enhancing an organisation’s performance.

Think of a jigsaw puzzle emptied fresh out of a box. All the pieces are scattered everywhere. What does any of it mean? Data mining would attempt to grab any pieces at will and force an image together. Data science is more exact and considered, and would piece together the puzzle in less time.

Data science v data mining is something of a redundant topic, though, because, essentially, data mining can fall under the umbrella term data science. Data science is mining, but on a more purposeful level, whereas data mining is simply mining. Data science is everything, including analytics and analysis as well as data mining.

The argument of Data mining v data science can be best rounded off by taking a look at their trends. Whereas use of data mining has slowly decreased since 2005, data science has veritably lifted off since 2011, and is predicted to overtake data mining by 2015.

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