Data science for social good: Leveraging analytics for positive impact

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The geeks are going to save the world. If "data science" isn't two of the geekiest words you've heard put together that's genuinely interesting and we'd love to know what are the geekiest words you've seen put in a single sentence.

However, it doesn't take long to see why it's the data scientists who will be the ones responsible for saving the world.

To work in this field of science is to analyse the happenings of the globe, and then to find the patterns that create them.

Every war, every famine, every medicine shortage, every period of political instability - all of these produce quantifiable data that can be catalogued and analysed.

A Master's of Data Science online (or on campus) doesn't just teach people how to read and make charts, but it teaches people how to quantify the very phenomena of life itself - and therefore leverage that data to make the changes that will, inevitably, change the world.

Up until this point, you might not have thought of the collection of data as a science in and of itself. After all, we use data to make decisions every day.

We watch the news and read articles to determine where we lie politically, we read reviews and compare prices to determine which product we're going to buy, and we expose ourselves to creators and what they produce to refine and enhance our taste.

All of this is a minor form of data science.

However, this may not explain to you fully what "Data Science" actually is, so let's try to expand on that.

Data science is the practice of using modern tools and methods to analyse the phenomena and circumstances around a particular eventuality, and then recognise the patterns around it. For example, if a particular country is going through a famine, there will be several quantifiable elements around it:

This list isn't even exhaustive as to what factors can influence a famine. It is the job of a data scientist to look at all the available factors, collect any relevant data on them, and look for the patterns that led to the disaster.

Then, the data scientist takes these findings to the appropriate authoritative body to effect lasting change, and possibly prevent the same thing from happening again.

Although you might not know it, Data Science has been around since...well...since mankind.

The first evidence of humans gathering data comes in the form of the Ishango Bone - an artefact from the Upper Paleolithic age.

The bone is 10cm in length, possibly from a baboon or other large mammal, and has a series of intermittent cuts etched into the side. Although the purpose of the bone is still debated, the most agreed-upon consensus is that it is a tally stick, a primitive tool of wood or bone to record numbers.

The first concepts of data analysis are credited to John Graunt in 1662 in his publication "Graunt's Natural and Political Observations."

The book outlined the concept of using statistics surrounding death, mortality rate by age, and causes of death could revolutionise medicine. Before this point, such information wasn't considered.

These concepts, when combined with the natural evolution of science and engineering knowledge, eventually led to the modern pinnacle of data science, Artificial Intelligence.

Artificial intelligence is the current peak of modern computing. The ability to have a machine think with a facsimile of human cognitive processes is devastatingly important.

While our brain does operate a little faster than a computer, a computer is not subject to the biological imperfections that cause human error.

A machine can't "think" with the same nuance and grace as a human, but it can think almost as quickly, with complete accuracy (according to the data it's been fed), and can provide invaluable assistance to human jobs.

AI is capable of a lot of things, but some of the tasks it excels at are summarising research and recognising patterns.

After all, these are the keys to human cognition. Everything we do is based on previously gained knowledge (research) and pattern recognition. For example, think of a car.

Did you think of a Ferrari? A hatch-back? Sedan? Maybe you thought of a smart car or even a military vehicle.

The point is, that you have absorbed the data around vehicles, and using pattern recognition you've been able to condense the information into an understanding of what does and does not constitute a "car." AI does the same thing.

So it's easy to see how an AI model would help in the area of data science.

Rather than having to analyse reams and reams of statistics, figures, and phenomena, then categorise them, separate what is useful from what isn't, and then search for the patterns yourself; you can simply feed the requisite data into your AI model, request the necessary parameters, and done - your AI has a full write-up of all necessary data for you to analyse and use in your research.

AI is still a tool that is in development, and there is a lot of discussion to be had around its implications for various industries. However, it can't be denied that when used this way, as a digital research assistant, AI is merely a tool made to help people streamline their work and get more reliable results.

Data science is more than just numbers and graphs and charts gone wild.

Within the technobabble and mathematics of data science, there are the quantifiable observances that make up our quality of life.

Through data science, we can visualise the trends, sentiments, economic trends, social paradigms, government policies, and logistical methods that determine whether or not we enter into a prosperous golden age or a global spiral.

The fact that we have this interesting, and essential branch of science is amazing. Now let's have the courage to use it.