Data science is the discipline of drawing conclusions and predictions from data. It combines statistics, computer programming and knowledge of the subject being studied. The work covers the whole path from raw data to a result: collecting and cleaning data, exploring it, building models, testing them and presenting what was found. Machine learning is one of its main tools, but much of the effort goes into preparing data and checking that conclusions are sound.
In financial markets, data science overlaps with the work of the quantitative analyst. Trading firms apply it to prices, order books, company accounts, news text and unconventional sources such as satellite images or card spending, in search of signals that can be tested through backtesting. Banks and payment firms apply it to credit scoring and fraud detection. Financial data is noisy and changes over time, so guarding against overfitting is a larger part of the job than in most other fields.