Data Science: Opportunities and Challenges
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It uncovers the findings from data. Diving in at a granular level to mine and understand complex behaviours, trends, and inferences. It’s about surfacing hidden insight that can help enable companies to make smarter business decisions. Data science uses the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems. It unifies the statistics, data analysis, machine learning, and their related methods to understand and analyze actual phenomena with data in different domains of study such as social media, health, finance, etc. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.
The primary components of science paradigm include empirical, theoretical and computational components. However, recently, data science has been added to this paradigm as a fourth component in the form of the data-driven concept. In many cases, earlier approaches and solutions are now simply rebranded as “data science” to be more attractive, which can cause the term to become beyond usefulness. While many university programs now offer a data science degree, there exists no consensus on a definition of suitable curriculum contents.