Analytics is a buzzword which has gained prominence in the last two decades starting from the beginning of the twenty-first century. It is not that analytics did not exist earlier. Merriam Webster dictionary defines “analytics” as “the method of logical analysis”. Analytics has been there since the time of Frederick Winslow Taylor who popularized time management exercises. The version of analytics that is more popular now is “Data Analytics”. The only reason for its popularity is the availability of huge amounts of data and an equal amount of computing power to go with it. The lack of any one of these would render data analytics meaning less.
Data analytics is applied across domains. It uses a lot of statistics. The advent of open source software such as “R” and “Python” has accelerated the use of data analytics. One needs just a computer and internet connection to get started. Some of the applications of data analytics are as follows:
Analytics leads to applications in machine learning and artificial intelligence. If a lot of data is available on, say, a particular disease and its symptoms across a large geographical region like China, machine learning can be used to train a model on this data to tease out the characteristics of the symptoms that this disease has. Later, this model can be used to predict the disease based on the symptoms gathered from new data. The point to note here is that the sample for prediction should also be from the same geographical region on which the model is trained; China, in this case. If such carefulness is not applied, this could lead to a biased model. A biased model would give wrong predictions short changing the entire effort.
There are a lot of classification exercises that can be done using data analytics. An example would be the classification algorithm used by online retailers like Amazon. Such algorithms club all the products a customer buys into one big group. Any product that is similar to this group is shown to the customer while browsing to entice him/her to buy it. The name of one of the methods used for such classifications is “Market Basket Analysis”.
The key to a successful career in analytics is the knowledge of statistics. In addition to statistics, if a person can get himself/herself trained in “R” or “Python”, a promising career could ensue.