Over the past century, so many companies thrived on oil, gas and steel to make a great fortune and stood out as the most valuable companies on the planet. Prior to 1900s and early after then, John Rockefeller’s Standard Oil Company and J.P Morgan’s steel businesses were ranked top becoming the first billion dollar companies in the world and this was known as the industrialist era. Forward to early 2000s, the world faced the internet bubble that burst out so many companies that were either slow or unwilling to face the new reality; the INTERNET.
Today, we leave in a data era, a one that has born new companies that thrive on more of software than hardware. Companies like Google, Amazon, Microsoft, Facebook, Oracle, IBM, etc and several other winning start-ups in silicon valley have by no doubt benefited from this data era. They have leveraged the immerse data obtained from the Internet Of Things (IOT) to near accuracy in predicting user requirements, market segmentation, achieve consumer satisfaction and thus optimize revenues without geographical barriers.
By now, I presume you have heard of data science a terminology that has taken the world by storm and a profession that has been labeled the sexiest of the 21st Century. We have been familiar with terms like data analysis and statistics and the emerging trend of data science may be a bit confusing, but don’t get it twisted, am about to break it down a little bit (am not that data science expert! J).
Fast forward, let’s imagine a relatively big retail shop that sells several items and compiles a sales report every month/year. A data analyst would be charged with data aggregation and answering questions like; “What was the most sold item in the period?” , “Which day of the week collectively recorded the highest number of sales? “, “What age category was most fond of each of the products? “, “What is our profit trend over the past periods? “, e.t.c.. However, a data scientist would have to answer more of what if questions from both structured and unstructured data such as; “What if we increased the price of product x, what would be the effect of that increment on products Y and Z and the overall profit?”, “If a customer buys product x, what’s the chance that they will buy y and z?”, “What is the chance that a customer whose profession is M, is aged ii, drives a car and obtained university education, is likely to pay for goods purchased on credit in time?”. To mention data science, you will always mention data mining, clustering and classification. With clustering, you will for example segment your customers in clusters depending on their behavior as told by the data. Whereas with classification, you will already have pre-defined classes such as age groups to which you will group customers and proceed to build insight.
In simple terms, data science combines statistics and math with computer science to create a whole new data field. Concepts like machine learning and neural networks have become popular in both data science and artificial intelligence and all these can’t be separable as one leads to the other. To me, data scientists are the modern fortune tellers and the fairest of them all as they are able to predict the outcome of your actions or actions of your competitors with facts. The field is still new and still undergoing structuring with numerous tools being built to support data science.
Companies like NETFLIX, Amazon, Google, Microsoft and Facebook have greatly invested in data science and no wonder they always try to accurately predict what we want and have managed to keep us locked to their platforms. Unfortunately, it’s not yet obvious that the small businesses can leverage data science to grow their customer base as a lot of data is needed to build more accurate models and since most of these small businesses may not have the data collection, storage and data processing infrastructure, the field remains an advantage to the big companies lest you rely on the aggregated forecasts drawn by these giants. But never mind, you too can get involved by joining this sexy space as a data science professional.
In my subsequent articles, I will talk about the common tools in data science and try to contrast them as well as the different platforms that areg proven good for your learning. See yuh!