If you search the web for a definition of data science, you will certainly find many. This reflects the reality that data science means different things to different people. There is no real consensus on what data scientists exactly do and what training they must have; it all depends on the task they're trying to accomplish, for example, data collection and cleaning, data visualization, and so on.
For now, I'll try to use a universal and, hopefully, consensual definition: data science refers to the activity of analyzing a large amount of data in order to extract knowledge and insight leading to actionable decisions. It's still pretty vague though; one can ask what kind of knowledge, insight, and actionable decision are we talking about?
To orient the conversation, let's reduce the scope to three fields of data science:
Descriptive analytics: Data science is associated with information retrieval and data collection techniques with the goal of reconstituting past events to identify patterns and find insights that help understand what happened and what caused it to happen. An example of this is looking at sales figures and demographics by region to categorize customer preferences. This part requires being familiar with statistics and data visualization techniques.
Predictive analytics: Data science is a way to predict the likelihood that some events are currently happening or will happen in the future. In this scenario, the data scientist looks at past data to find explanatory variables and build statistical models that can be applied to other data points for which we're trying to predict the outcome, for example, predicting the likelihood that a credit card transaction is fraudulent in real-time. This part is usually associated with the field of machine learning.
Prescriptive analytics: In this scenario, data science is seen as a way to make better decisions, or perhaps I should say data-driven decisions. The idea is to look at multiple options and using simulation techniques, quantify, and maximize the outcome, for example, optimizing the supply chain by looking at minimizing operating costs.
In essence, descriptive data science answers the question of what (does the data tells me), predictive data science answers the question of why (is the data behaving a certain way), and prescriptive data science answers the questions of how (do we optimize the data toward a specific goal).