How Machine Learning is important for Data Science?

Machine learning is an integral part of data science; and is used to solve many predictive analytics business problems. Any data science problem wherein historical data is used to predict future occurrences requires knowledge and implementation of machine learning algorithms. For example – creating a customized recommendation engine for e-commerce portals, video-streaming businesses such as Netflix, YouTube etc. all uses machine learning algorithms as these services are customized depending on the utility and preferences of the users.
Furthermore, machine learning is also extremely important in implementing automation projects as well in data science wherein the machines or systems are trained to self-act to impulses.

ML is extremely important for data science. Many data products are predictive based on past knowledge from data. These are clear tasks for ML.

ML has such as high rank in data science that the common view of a data scientist is someone that uses big data technologies to create pipelines that feed machine learning algorithms. I personally find this view to be very limited and I believe there are better definitions but this widely accepted description of data scientist pretty much nails how important ML is.