Data science is an interdisciplinary field focused on extracting meaningful information from large sets of data. To discover hidden patterns, Data Scientists use math, science, algorithms, and systems to identify opportunities for increased efficiency, productivity, and profitability.
In simpler terms, data science uses math and technology to find hidden patterns (and ways to be more productive and profitable) in raw data. To find those patterns, a Data Scientist spends a lot of time collecting, cleaning, modeling, and examining data, from numerous angles, some of which have not been looked at before.
Essentially, data science is about knowledge creation: it makes use of the most state-of-the-art techniques and tools the fields of computer science and statistics have to offer to turn a mess of data into knowledge that an organization can use to inform their business practices.
Among the most noteworthy techniques a Data Scientist uses are predictive causal analytics, prescriptive analytics, and machine learning. The first, predictive causal analytics, uses data to predict the likelihood of different possible outcomes of a future event. Prescriptive analytics goes a step further, suggesting a range of different actions based on those possibilities, with an eye toward optimizing outcomes.
Machine learning, unlike the two techniques just mentioned, is not the “what” but the “how” of data science: it’s the practice of using data-based algorithms that improve automatically based on past experiences – essentially learning to do their job better – to discover patterns and make predictions.
That said, in the real world, the practice of data science involves much more than simply using computers to crunch numbers. In fact, Data Scientists may be heavily involved in the decision-making process across departments, which means that, practically speaking, data science also involves collaborating with others, and especially knowing how to communicate important findings to other people.