Data Science: Top 10 Definitions

3 min readDec 12, 2020


“A set of quantitative and qualitative methods that support and guide the extraction of information and knowledge from data to solve relevant problems and predict outcomes.” (Xiuli He et al, “Supply Chain Analytics: Challenges and Opportunities”, 2014)

“A collection of models, techniques and algorithms that focus on the issues of gathering, pre-processing, and making sense-out of large repositories of data, which are seen as ‘data products’.” (Alfredo Cuzzocrea & Mohamed M Gaber, “Data Science and Distributed Intelligence”, 2015)

“Data science involves using methods to analyze massive amounts of data and extract the knowledge it contains. […] Data science is an evolutionary extension of statistics capable of dealing with the massive amounts of data produced today. It adds methods from computer science to the repertoire of statistics.” (Davy Cieln et al, “Introducing Data Science”, 2016)

“The extraction of knowledge from large volumes of unstructured data which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD).” (Suren Behari, “Data Science and Big Data Analytics in Financial Services: A Case Study”, 2016)

“A knowledge acquisition from data through scientific method that comprises systematic observation, experiment, measurement, formulation, and hypotheses testing with the aim of discovering new ideas and concepts.” (Babangida Zubairu, “Security Risks of Biomedical Data Processing in Cloud Computing Environment”, 2018)

“Data science is a collection of techniques used to extract value from data. It has become an essential tool for any organization that collects, stores, and processes data as part of its operations. Data science techniques rely on finding useful patterns, connections, and relationships within data. Being a buzzword, there is a wide variety of definitions and criteria for what constitutes data science. Data science is also commonly referred to as knowledge discovery, machine learning, predictive analytics, and data mining. However, each term has a slightly different connotation depending on the context.” (Vijay Kotu & Bala Deshpande, “Data Science” 2nd Ed., 2018)

“Is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis, and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data.” (Maryna Nehrey & Taras Hnot, “Data Science Tools Application for Business Processes Modelling in Aviation”, 2019)

“Data Science is the branch of science that uses technologies to predict the upcoming nature of different things such as a market or weather conditions. It shows a wide usage in today’s world.” (Kirti R Bhatele, “Data Analysis on Global Stratification”, 2020)

“The concept that utilizes scientific and software methods, IT infrastructure, processes, and software systems in order to gather, process, analyze and deliver useful information, knowledge and insights from various data sources.” (Nenad Stefanovic, “Big Data Analytics in Supply Chain Management”, 2021)

“This is an evolving field that deals with the gathering, preparation, exploration, visualization, organisation, and storage of large groups of data and the extraction of valuable information from large volumes of data that may exist in an unorganised or unstructured form.” (James O Odia & Osaheni T Akpata, “Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection”, 2021)

“Data science is the multidisciplinary field that focuses on finding actionable information in large, raw or structured data sets to identify patterns and uncover other insights. The field primarily seeks to discover answers for areas that are unknown and unexpected.” (Sisense)

“Data science is the practical application of advanced analytics, statistics, machine learning, and the associated activities involved in those areas in a business context, like data preparation for example.” (RapidMiner)

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