What is data science?

Data science is a complex discipline that explores how to collect and apply the vast swathes of data now available to analyse. In 2013, one study found that 90% of all data to ever exist had been created in just the past two years. This is why data science has become so important; there’s so much of it out there that we need to research and discover new ways of engaging with it. You might think that data science begins and ends with A.I. and machine learning, but there’s much more going on underneath the surface. So what is data science and how is it relevant to you?


What it is


The discipline of data science has existed in some form since 1962, however, it wasn’t until the early 21st century that it really started to take off. This was the era of “Web 2.0,” where the internet changed from static webpages to sites that users could truly engage with. It’s this engagement that created the massive amounts of data we deal with today, or “Big Data.” When users engage with these websites, such as Facebook and YouTube, they leave a kind of footprint that can be analysed by discerning data scientists.


What it involves


There are five stages to data science. In smaller firms like startups, just one person might be responsible for multiple stages. However, in larger firms each stage will have one or more professionals dedicated to them, as the different stages require different skills and programs to properly execute.


The first stage is collection. This involves either gathering the data yourself or purchasing it from a third party. Having the right data is important; there is little you can do if its not applicable to what you want it for. At this point, its completely unprocessed so will ultimately just take up space if it isn’t used for anything.


The next stage is maintaining the data you have, whether that’s moving or storing it. This means developing solid infrastructure and accounting for whether you have structured or unstructured data. Importantly, you should to take into account how you intend to apply the data, or else it is easy to become fatigued over the volume of it.


Afterwards, the third stage involves processing the data. This means cleaning the data and detecting if there are any anomalies that ought to be discarded. It also involves modelling the data and summarising what it entails.


The fourth stage is aggregating and communicating what you’ve gathered. This could involve visualising and reporting the data. As well as this, it can involve applying it to decision making and business intelligence.


The fifth and final stage is analysing and learning from your data. You might want to optimise the process you’ve put the data through so far or use it to make predictive analyses. It’s only from this point that A.I. and machine learning can be applied – and they’re not always necessary for the successful application of data.


Each stage requires the previous to occur before they can be implemented, but a singular vision is important throughout to ensure success. As much as it is a data scientist’s job to overcome the challenges involved in data collection and application, their expertise is useless without a direction to focus on.


Relevance to you and customers


Good data science has huge implications for the conduction of business and the consumer experience. It can make it easier to make informed choices, to know existing audiences and how to approach new ones. For the customer this means that their wants and needs are more quickly and directly met by informed businesses. As the discipline evolves, it may even allow us to move ever-closer to automated systems that can serve our needs before we even know we h


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