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There are not so many differences between Machine Learning and Data Science.

Machine Learning is used synonymously with Data Science.

Machine Learning is the set of techniques concerned with getting a program to perform a task better with respect to some metric as the program gains more experience. Amazon’s recommendation engine is an example of a machine learning system. Machine Learning has three distinct areas that fully describe it-  supervised learning, unsupervised learning, and reinforcement learning. Data science is the process of obtaining, transforming, analyzing, and communicating data to answer a question.

Machine learning is the technology you are expected to understand. Data Science is likely how you wish to apply that machine learning. Data Science is the real-world application of machine learning, with the goal of creating products people use.

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Read More –

Machine Learning and Its Powerful Applications

R vs Python – Best One for Data Science?

Data Science and Its Applications

Python and R – Best one for Machine Learning?

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Machine learning is a field of Computer Science that involves using statistical methods to create programs that either improve performance over time or detect patterns in massive amounts of data that humans would be unlikely to find. Like much of AI, it’s an attempt to replace explicit programming with automatic discovery of parameters.

Data science is an industry term for jobs that might involve machine learning, information retrieval, and other subfields of computer science considered difficult because they require a mathematical sophistication that 90+ percent of programmers don’t have, but too important to ignore.

Data Scientist is a person who actually uses the things that Machine Learning Engineer has built.

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Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this category. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit in this category.

Data science is much more than machine learning though. Data, in data science, may or may not come from a machine or mechanical process (survey data could be manually collected, clinical trials involve a specific type of small data) and it might have nothing to do with learning. But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects.

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In One Word –

Machine learning engineers build, implement, and maintain production machine learning systems.
Data scientists conduct research to generate ideas about machine learning projects and perform analysis to understand the metrics impact of machine learning systems.

Source – google, quora.

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