Do you know that over 2.5 quintillion bytes of data is created on a daily basis? According to IBM, it has been forecasted that the number of jobs for every data expert in the United States will increase by 364,000 openings to 2,720,000 by 2020.
In addition, it has also been predicted that by 2020, an estimate of 1.7 MB of data will be generated every second for every human on the planet. Imagine how much data this would be at the end of the year. How much more by the end of the decade? It is therefore obvious that we can not effectively handle data without data science and machine learning.
The burning question therefore is that: how do we intend to process this amount of large data? Now, this is where data science vs machine learning comes into the bigger picture. It should interest you to know that machines have the capacity to learn on their own.
Yes, this is very much possible and in fact realistic in this rapidly developing technological age. Just like humans, machines can be structured and designed to learn more from a good amount of data. Machine learning becomes highly important so that machines are enabled to learn from experience automatically. This is done without the machines needing to be explicitly programmed.
