Being hired or paid by data analysis agencies, politicians and people in other industries, data scientists and analysts are considered to be among the most likely potential adversaries that have the motivation to attempt a re-identification (identity disclosure, link disclosure and content disclosure), and have the necessary tools.
Data scientists are data wranglers. They take data points (unstructured and structured) and use math, statistics and programming to clean, manage and organise data. Then they apply industry knowledge, contextual understanding, a critical attitude towards existing assumptions – to uncover hidden solutions. There are a few data scientists working for universities and other institutes, answering some very interesting questions and/or working to solve some societal or medical problems (The road to hell may paved with good intentions), but most work for businesses trying to find solutions to business challenges. One might say, these find opportunities for businesses to make more money. A whole new industry.
Data science and data mining are dissimilar terms, but when it comes to data they often go hand in hand:
Data mining is about finding trends in data sets, and using these trends to identify future patterns. It often involves analysing vast amounts of structured data. It is a technique mostly used by businesses to make use of data to find new trends. If you know how to navigate data and have a bit of statistical knowledge, you can do it yourself.
Data science studies everything from big (unstructured) data analytics, data mining, predictive modelling, data visualisation, mathematics, and statistics. It is a field of scientific study that aims to build data-centric products for organisations. It finds application in social analysis, and in building predictive models and unearthing new facts in various domains. To do it you need extensive knowledge of machine learning, programming, the domain it is applied to, and often also includes data mining.