This article talks about some things which are fun, some of which are a bit on the serious side, and some of which are pretty grim. The topic of data science has traversed the internet rigorously for almost a decade now. It is something that used to fascinate people in the past and continues to do the same. Data science is more entangled with our regular lives than most of us realize. We as human beings of the society, users of social media, seeking information on the internet, are the primary producers of the most valued digital resource of the day, data. Data science has also become one of the key disciplines in terms of growth and career building. So it is only fair that we keep ourselves aware of its life cycle and also learn a word or two about the applications of data science.
Cleaning and preparing data more time than anything else
Data is generated from various sources in different formats. It can be structured, semi-structured, or unstructured. Now, suppose you are running a model to conduct some behavioral analysis based on the interaction of a customer with a bot. Your work would be incomplete without taking the textual data into account. You might also want to tap into the purchase history of the customer, provided that does not mean a breach in data security laws. This will require you to process both structured – numerical data and unstructured textual data. In order to make them fall in line, you will need to prepare the data, so that the analytical model can process it. So, cleaning and preparing. It is somewhat like being a digital janitor. Nevertheless, a lot of analysts find it really fun.
Nothing such as complete automation as yet
Machine learning algorithms have made things possible which were not earlier. However, they have not eradicated the need for human professionals to get cozy with the data. In case you are doing the same thing again and again with data that comes in a set format, that is different. But remember, in most cases, you will not be that unfortunate. You will have to use your skills and prepare the data.
The business outcome of data analytics is 20%
Yes, that does sound meager considering the drastic implementation of data analytics in various industries. In a Big data and AI executive survey by The New Vantage Point Partners, 91% of executives show the urgency of investing more in analytics. But a striking 77% of them claim to have faced insurmountable obstacles which range from lack of infrastructure to a scarcity of talent. And for those who actually manage to set up a data analytics team, buy some machine learning, or hire a consulting data scientist, the biggest problem becomes the failure to locate the problem. Ill planned and misguided data science does more harm than good. So, no matter what one may say, the land of data science is not made of candies or flowers.
It is indeed the age of machine learning
Training machines to recognize patterns in data means that you can now crunch more data in less time which gives you more room for erroneous judgment and an opportunity to play trial and error with models. We have been witnessing automation powered by machine learning in a lot of different sectors and data science is one of those. The training data has still to be picked, cleaned, and prepared meticulously in many cases, but once trained these systems can work pretty much on their own, so that is less work and more results for the knowledge worker. So, if you have not yet it is time you read some stuff about how to start learning machine learning. You can also consider enrolling for an AI course with machine learning specialization.
The Indian scene during COVID crisis is pretty grim
The sheer population of India amplifies any problem we face. The pandemic fall out has hit us where it hurts. In a month between March and April this year, 122 million Indian citizens have lost their jobs. 37 million among them are trying desperately to gain new skills online to stay in shape and get back in the game once things start normalizing or before that, with some luck. In the meantime, a severe lack of data science and AI-related skills is causing Indian businesses a shattering 330 billion rupees each year. A few well-planned steps can make or break your career at this point.