Data is currently regarded as being among the most valuable commodities in the world. Given how extensively it is now being used in a multitude of different fields and contexts, it isn’t too hard to understand why it is that such enormous value is placed on something which can often seem so mundane and boring. In fact, data underpins a great portion of our everyday lives, even though we are often unaware of it.
Consider when you look online for some shopping, or even a new movie to watch on Netflix. In both these scenarios, when you log onto the relevant website or service, you will be met with some recommendations which are based on your past activity. These recommendations make use of both your data, to see what you have previously shown an interest in, and the data of millions of other people, to identify patterns which might help it to recommend suitable products to you.
Data science is, as the name suggests, the science of data. In practice, data science is concerned with how we can both collect and analyze data to the best effect. Both processes, the gathering, and the analysis, must be considered separately. It is only by paying attention to how we carry out both that we can make data useful.
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Data analysts are specifically concerned with analyzing data after it has been collected. On the surface of it, data analytics sounds like an intellectual indulgence, a potentially interesting exploration of analytical methods no doubt, but it doesn’t sound like the incredibly sophisticated and powerful tool that it is. In fact, data analytics has been put to a variety of different purposes, many of which provide measurable benefits to society. For example, data analytics has allowed us to get one step ahead of deadly pandemic outbreaks and has saved lives.
Not all the applications of data analytics are quite as altruistic. For example, it is sophisticated data analysis that allows Facebook and other social media sites to target advertising to individuals.
Computer scientists and philosophers alike have long been fascinated by the idea of creating artificial intelligence, one which is capable of replicating the complexities of human intelligence. Such a machine is still a long way off, but current research surrounding machine learning and artificial intelligence employs a data-heavy approach to the problem.
Statisticians conduct analyses of relatively small data sets (small relative to the masses of information that some of the biggest online services can generate) to draw conclusions about what the data shows. Along with the capability to draw conclusions from the raw data and numbers, statisticians also understand the more abstract theories of statistics and causality, which are essential for a full and well-rounded analysis.
A data science degree can open several different potential career paths and opportunities for your future. Data science is an essential area of study and one which our modern society is making increasing use of.