Data science is much broader concept than machine learning. It starts from simple data visualization and descriptive statistics to get insights, manipulations like cleansing to prepare data. Before you can use some ML algorithms.
Basically such huge stacks as bigdata, visualization and data preprocessing are out of machine learning scope. And they are all integral parts of "Data Science".
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Machine Learning tries to create systems that can learn from data. As such it can be used in a wide variety of settings, for example to make robots learn to walk or train virtual agents to play video games.
Data science concerns itself with the extraction of knowledge from data. In order to do so it uses a bunch of different techniques from different disciplines. Machine learning includes some techniques that can be very useful for a data scientist such as deep learning, decision trees and different clustering algorithms. However, machine learning has more to offer than Data Science uses and Data Science does not solely rely on Machine Learning.
Data science is much more broad. It's sort of a catch-all term that right now doesn't honestly have a very clear definition. But data-science includes all of the skills and techniques required to make sense of data which has high velocity (it's coming at you quickly), volume (there's a lot of it), or variability (it's messy, like natural language processing). This means that it certainly includes machine learning and AI, but that it's also about the tools one might use in a real-world situation such as SQL, Hadoop or Spark (and related information such as a knowledge of parallel programming). Additionally, data science may or may not include the communication aspect such as making good graphs and using Excel.
Basically, Data Science is ML+.
Data Science is, as others have noted, a much broader term than machine learning. Applying Machine learning techniques is one aspect of data science. Data Science, more generally, is the science of deriving knowledge from data. The term was coined back in 1960 and kept evolving to describe the flow and interplay of problem definition, data collection, data transformation, data modeling/ analysis, and decision making. So to answer your question specifically: