Data Science and Business Analytic Program

Description

Data science is an interdisciplinary academic field^[1] that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.^[2]

Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine).^[3] Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.^[4]

Data science is “a concept to unify statistics, data analysis, informatics, and their related methods” to “understand and analyze actual phenomena” with data.^[5] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.^[6] However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.^[7][8]

A data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.^[9]

^{Data science and data analysis}

^{Data science and data analysis are both important disciplines in the field of data management and analysis, but they differ in several key ways. While both fields involve working with data, data science is more of an interdisciplinary field that involves the application of statistical, computational, and machine learning methods to extract insights from data and make predictions, while data analysis is more focused on the examination and interpretation of data to identify patterns and trends.[37][38]}

^{Data analysis typically involves working with smaller, structured datasets to answer specific questions or solve specific problems. This can involve tasks such as data cleaning, data visualization, and exploratory data analysis to gain insights into the data and develop hypotheses about relationships between variables. Data analysts typically use statistical methods to test these hypotheses and draw conclusions from the data. For example, a data analyst might analyze sales data to identify trends in customer behavior and make recommendations for marketing strategies.[37]}