Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. Data science also integrates domain knowledge from the underlying application domain, such as natural sciences, information technology, and medicine.
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. It is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction.
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to take data, understand it, process it, extract value from it, visualize it, and communicate it. They use complex machine learning algorithms to build predictive models and determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting, find patterns and trends in datasets to uncover insights, create algorithms and data models to forecast outcomes, use machine learning techniques to improve the quality of data or product offerings, communicate recommendations to other teams and senior staff, and deploy data tools such as Python, R, SAS, or SQL in data analysis.
