Data Science - A High Level Explanation



This is an homage to my first year of working in the real world. I came out of a neuroscience degree confident that it was not what I wanted to go into directly, but I still had no idea what was to come. I was instead to be immersed in a world of data science and everything that came with it, without any prior programming experience.

And yet, right now, I can’t imagine working anywhere else.

What do you do?

Data science is a little bit trickier to explain and get people excited about compared to neuroscience. In general, data analytics and statistics is something that most people don’t know very much about, nor do they want to. It is especially hard to explain data science, as data scientists themselves are undecided as to what it is exactly. How does it compare to business intelligence, for example? Is a data analyst the same as a data scientist?

What is data management?

There are some core concepts that are common to data science and business intelligence: the reshaping of data into different formats, performing analysis and gleaning usable information from raw data, knowing how to work with very large volumes of data, knowing how to present the results in a meaningful way, and how to use these results to make money. Business intelligence uses this process to support businesses to use their data to make themselves more efficient, identify new opportunities and drive what goes on internally. Businesses are asking new questions based on what we already know – and becoming data driven.

What is data science?

But the 'essential' skill set of the data scientist is growing exponentially. There are so many new tools, technologies and opportunities to consider. We begin to delve into the realms of data science when a business wants to use unconventional methods to extract value from unconventional data.

Can we analyse our customers’ tweets to see how they respond to a campaign, without pestering them with questionnaires? Can we analyse the audio of customer service calls and identify a negative or positive experience based on tone of voice? Can we analyse current trends from Instagram images? Can we train a machine to identify fraud in real time before any damage occurs? Or can we build an algorithm to recommend to make a company more streamlined and produce less waste?

Data is Valuable

Data is like a universal currency. Yet, raw data is worthless if it is unclean and cannot be analysed, or cannot be analysed quickly. Similarly, insights are worthless if they can’t be applied to a business case – they are merely interesting statistics. And don’t underestimate data visualisation – providing a story and a narrative to a data analysis can give it meaning and help you to make the correct decisions. The best visualisations should not even need an explanation!

Engage Consolidata

Data science adopts all of these skills, and it is far from a null field. It encompasses programming, a deep knowledge of statistics and know-how in the latest technical tools. We are constantly learning and growing, and as data science evolves and the roles within it become more defined, we will hopefully see it spill out into every industry, and businesses can tap into a stream of data analytics for any business case.

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