Data is all around us. I am not exaggerating when I say that we all are surrounded by data. We are all familiar with the saying
the beauty lies in the eye of the beholder,
that is exactly where data science lies. A guy driving home from his office goes on his set path on any normal day. The path is carefully monitored by his eyes each day. He finds out where the potholes are, where there are lights, the divergence, the steeps and the slopes. Everything is measured and monitored continuously each day of his life for days.
If the distance is measured in kilometres and plotted on the X axis and the number of traffic lights are plotted on the Y axis. The graph will probably show a linear increase in the number of traffic lights per kilometre. This graph can now be interpreted in various ways, with respect to any number of drivers, like the ones mentioned above (potholes or divergence etc) and more. This is an elementary representation of data and its interpretation. Increase in complexity with respect to a number of variables like trees and grass presents various challenges for the data scientist to form any hypothesis.
Just like the above scenario our eyes have been constantly monitoring our movements and our environment till now. But, there is no easy way to gather all that data from our brains. The most common way of gathering data nowadays is our mobile phones and computers. These devices monitor all of our action and gather data from various access points to gather deeper insights about various activities. This data when represented with the help of graphs and charts to deduce certainty of happenings is what is at the core of data science.
More so, at an elementary level, tool like pie charts and graphs can suffice a good enough representation of simplistic data. But, with the technological advancement the avenues to collect data are not just limited to our computers. Technologies like Augmented Reality and blockchain rely heavily on data and its interpretation. The technological advancement presented itself with a need for data science as a profession, a career and an industry.
Why is data science a sought after career, you ask ?
Accuracy in decisions
There is and will never be anything more accurate and closer to reality than a decision based wholly on data. This is the reason why truth seekers are inclined towards data science.
It’s not all math
The data when visualised with the help of a charts and graphs on tools like tableau is an exciting career path in itself. With a dashboard created to analyse the data, the indices which represent the anomalies in trends and which help in creating the “right problems first” are some of the many wide array applications of data science.
The future is near
With historical data and the indicators for which that data is collected, when extrapolated to prove hypothesis will result in creating a futuristic view. Till now businesses have mostly relied on impulse decisions or decisions based on human intellect, whereas with data science the gap between the charted path to the future and the actual can be miniscule.
The science is transferable
Data science like any other science is knowledge deeply rooted in concepts. Concepts which remain the same across different industries and domains.
In today’s business environment the amount of resources required to separate usable information from the noise is immense. Data science is where the information which was earlier unusable or non-reliant can now be represented in a way where it’s easier to be consumed and internalized.