We all used data throughout our daily lives without even realising it. Of course, we do not perform the rigorous statistical exercise that is usually associated with the craft. However, if you were tasked with opening someone’s eyes to the world of data science and how they do it too, albeit at a different scale, examples are abound around us. Let us look at three examples where we do this in our lives regularly.
Buying a car or a house
For most of us the most expensive thing we buy would be a house. Obviously there are many factors to consider when embarking on this project. We want to get this right as not only does it cost a lot of money but most of the time we might have an emotional connect to the place, because we intend to live in it. Instantly we recognise that we’re dealing with a lot of different factors ranging from location, design, finance to regulations. We try to make sense of this complex set of data through spreadsheets. We deal with statistical concepts like average, what if analysis, trends etc. We also familiarise ourselves with calculated terms like ROI, rates (interest rates, inflation rates, rates of change of house prices etc). Not only do we educate ourselves with these things but also realise very quickly that the data itself isn’t enough. In spite of our best efforts we are left with the feeling that we are still best guessing a lot of the decisions in the process. This process of buying a house, especially the analysis that goes into it, is no different from what happens across data terms around the world. They collect some data, clean it, ask questions that need answering, run data through statistical analysis to try and answer those questions, with the hope that a decision can be arrived at confidently backed by data.
Biometric exercise watches
This is the most subtle way we are all exposed to the craft of data science daily. Our humble watches have evolved from being just mere time keepers, to fashion statements to biometric data hoovers & SciFi level dashboards! Countless times a day we look at our watches to see how we are doing with our metrics. Have we walked enough steps? Have we burnt enough calories? Are we being stationary for long periods during the day? How is our heart rate doing? Maybe once in a while we ask more detailed questions like – Is my gait changing? Am I walking slower or faster compared to the last year? Is my stride reducing? what is my BMI? etc. If you ever get a time you should google and find out how to look at the data that is on your phone. You would be surprised the amount of lines of data that’s constantly written throughout the day. It will run to tens of thousands of rows of data just for one days!! The quality that I most admire about this whole affair is that it is subtle when it comes to end user interaction but complex in the background and is at a massive scale. All of the data collection and analysis is hidden away and wrapped up. All that we the user is provided are insights! Constant and repeated exposure to performance dashboards is making all of us into analysts. All of us have already made the first leap into data science without knowing. Of course, the real craft is more rigorous and requires a mathematical and computational grounding. However, the craft it is not a distant for anyone thinking about exploring a career in it.
Buying a cup of coffee
The last example I would like to visit to show that we all use data analysis in our daily lives is the mundane task of buying a cup of coffee from a local cafe. I have always felt overwhelmed when I walk into a coffee shop with the plethora of choice available. What size drink, what strength coffee, how much milk, how hot, sugar, milk alternative, flavourings, price, eating in/ take out. Phew!!! Our brains take in all of this information and work our way through sequentially arriving at our final choice as we approach the person across the counter. I have always found this a bit overwhelming and fascinating. Most of the data science when condensed to a super simplistic approach is almost akin to this. We collect data, process it and run through an algorithm to arrive at a decision. Imagine doing a similar task at a much larger scale – with electricity grids, forex prices, rail networks, drug discovery with molecular shapes, composition and pathways to select etc. All of them go through the same steps albeit with a more mathematical approach.
Anyone thinking about taking up a profession in the field of data should not think no that it is a distant unattainable profession. They are most likely already using tools, performing tasks and employing those aspects of their grey cells that will be called upon if they get into the profession. It is just a question of putting oneself through a structured programme to learn rigorous techniques that are employed by professionals. It is as simple as – Decide, plan, sign up for a course, train, qualify and become a data professional.
