Statistics, relation to Data Science

"Is data science just a trendy name for statistics? While data science and statistics are overlapping fields, statistics is generally focused on the specific task of testing hypotheses based on data. Data science more broadly includes the storage, manipulation, visualization, filtering, and preparation of data that is typically required prior to statistical analysis. Data science does also encompass statistics, as well as machine learning; whereas statistics generally involves deriving conclusions from existing data, machine learning involves making predictions from a data set that will generalize to other data. ........ Data science includes practices that are more exploratory. In experimentally-oriented disciplines such as psychology and neuroscience, statistics are a natural approach to deriving meaning from data. This is because data typically come from experiments, in which the research systematically and intentionally manipulated certain variables. A good experiment is hypothesis-driven, meaning that the researcher has predictions in advance as to how the data will systematically vary with the experimental manipulations. These predictions are usually based on past experimental findings, or models of the process being studied. Statistics are fundamentally embedded in data science — and indeed, the concept of “data science” as a discipline emerged from the field of statistics — but data science can be thought of as a larger set of practices the includes statistics, machine learning, data cleaning and transformations, and visualization. Many of these approaches are more exploratory than hypothesis-driven. That is, rather than looking for a specific, predicted pattern, the data scientist explores the data to find systematic patterns that may emerge from the data. For instance, researchers using techniques like fMRI have attempted to “decode” specific patterns of brain activity, such as what picture a person is viewing. These lines of research explore a variety of ways to transform the data, and a variety of machine learning approaches to make predictions about what the person is seeing. The goal is to identify the data processing pipeline that makes the most accurate predictions"