descriptive variables (observable but not measurable).
A data analyst is someone who extracts valuable insights from confusing. These days, the world is filled with people trying to turn data into valuable observations.
For example, dating site OkCupid asks its members to answer thousands of questions in order to find the best partner for them. But he also analyzes those results to figure out the kinds of harmless questions you can ask to find out how likely you are to be intimate after the first date.
Facebook asks you to provide your hometown and current location, ostensibly to make it easier for your friends to find you and contact you. But it also analyzes these locations to determine global migration patterns and where fans of various soccer teams live. Major retailer Target tracks online and in-store purchases and interactions. It uses the data to build predictive models about which customers are pregnant to better sell baby products to them.
For work in the field of science, a lot of software libraries, platforms, modules and tools have been developed that effectively implement the most common algorithms and techniques used in science. Anyone who becomes a analyst will undoubtedly have an in-depth knowledge of the scientific computing library NumPy, the scikitlearn machine learning library, the pandas analysis library, and many others. They are great for solving science challenges. But they also encourage people to start solving data science challenges without actually understanding it.
A healthy controversy has erupted over which programming language is best for teaching science. Many insist on the statistical programming language R. Some suggest Java or Scala. Someone thinks Python is ideal.