Data Science: A Comprehensive Overview
Open accessAbstract
The 21st century has ushered in the age of big data and data economy, in which data DNA , which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics . Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.
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We are living in the age of 'data science and advanced analytics', where almost everything in our daily lives is digitally recorded as data.
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From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [17]
Plenty of computational tools and AI models have been developed for data-driven discovery.
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From ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery (2024) · cites this paper as Cao, 2017
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CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories
Martínez‐Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández‐Orallo, J., Kull, M., Lachiche, N., Ramírez-Quintana, M. J., & Flach, P. A. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2962680 · Cited by 445 · see citing works
Process mining for healthcare: Characteristics and challenges
Muñoz-Gama, J., Martin, N., Fernández-Llatas, C., Johnson, O. A., Sepúlveda, M., Helm, E., Galvez-Yanjari, V., Rojas, E., Martínez-Millana, A., Aloini, D., et al. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.103994 · Cited by 299 · see citing works
“Similarly, data science is, in its nature, multidisciplinary [92], and process mining is no exception [8].”
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Network Structure Inference, A Survey
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“, data science excluding representation learning procedures [36], deep-learning-based NNs [147], and GANs [89].”
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Enhancing pedestrian mobility in Smart Cities using Big Data
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Passing the Data Baton : A Retrospective Analysis on Data Science Work and Workers
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Predictive Analytics for Predicting Customer Behavior
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Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop
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“new and potentially actionable insights from real-world data [3, 6, 12].”
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“Data Analytics refers to “the theories, technologies, tools, and processes that enable an in-depth understanding and discovery of actionable insight into data” (Cao, 2017).”
INVESTIGATING DATA LIKE A DATA SCIENTIST: KEY PRACTICES AND PROCESSES
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“Should the reader wish to delve more into the details of particular BigData disciplines, the reviewpapers [1] and [11] are excellent sources of information.”
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Digital Twin for Federated Analytics Using a Bayesian Approach
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“Data science 89,90 is a broader field that encompasses the collection, analysis, and interpretation of data using various techniques, including AI, ML, and other statistical methods.”
Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management
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Regulatory learning: How to supervise machine learning models? An application to credit scoring
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Linking data science to lean production: a model to support lean practices
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“…business and management, to quickly and effectively extract information and transform it into knowledge, by processing and interpreting massive volumes of data, and make better and more informed decisions (Vicario and Coleman 2020; Kamble and Gunasekaran 2020; Cao 2017; Provost and Fawcett 2013).”
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Learning data science in elementary school mathematics: a comparative curriculum analysis
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“First, data deluge has necessitated computational methods to manage, process and mine big data (Cao, 2017).”
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“A wide spectrum of data analytics exist to utilize such data; however, meaningful interpretation of large complicated data may not be adequately handled by traditional data analytics [4].”
Real-World Applications of Data Analytics, Big Data, and Machine Learning
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“, 2009) culminating in a data product—a tangible data-driven deliverable (Cao, 2017).”
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“…of enormous amounts of data gives the statistical business a completely new push in a direction that is not yet sufficiently understood – although there is growing awareness for the synergies and potentials of close cooperation between statistics and other disciplines of data science [21,22].”
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Creative Data Work in the Design Process
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“While working with data is familiar from decades of research in knowledge discovery and data mining [16], information seeking [35] or information visualization [11], the evolution of big data collection and analytics triggered new types of data work [10] to be developed and to be used in myriad use-cases.”
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Urs, S. R., & Minhaj, M. (2022) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24649 · Cited by 25 · see citing works
“…in human centricity, the DS program of iSchools would be distinctive and reflect the academic lineage and ethos of iSchools. iSchools have the academic moorings to focus on Open Science, which both Donoho (2017) and Cao (2017) surmise as “ the future ” of DS and are included in the KAs of EDSF.”
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“…used tasks ( i.e ., “ machine-learning ” , “ deep-learning ” , and “ classi fi cation ” ), algorithms ( i.e ., “ neural-network ” , “ cnn ” , and “ lstm ” ) and tools ( i.e ., “ python ” , “ keras ” , and “ scikit-learn ” ) in data science (see Table 5) ( Cao, 2017 ; Karbasian & Johri, 2020 ).”
BackgroundInfluentialOfficial statistics in the era of big data opportunities and threats
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An intriguing future is approaching: Artificial intelligence meets molecularly imprinted polymers
Karasu, T., Çalışır, F., Pişkin, S., Özgür, E., Armutçu, C., Çorman, M. E., & Uzun, L. (2024) · Journal of Pharmaceutical and Biomedical Analysis Open · doi:10.1016/j.jpbao.2024.100041 · Cited by 26 · see citing works
Data Science: Profession and Education
Cao, L. (2019) · IEEE Intelligent Systems · doi:10.1109/mis.2019.2936705 · Cited by 23 · see citing works
Self-citationTeaching Data Science through Storytelling: Improving Undergraduate Data Literacy
Li, Y., Wang, Y., Lee, Y., Chen, H., Petri, A. N., & Cha, T. (2023) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2023.101311 · Cited by 22 · see citing works
Missing Data Problem in Predictive Analytics
Nugroho, H., & Surendro, K. (2019) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3316615.3316730 · Cited by 22 · see citing works
“Also, challenges and directions that might occur in the predictive analytics domain with the presence of missing data will be presented. making and action [4].”
BackgroundData science for job market analysis: A survey on applications and techniques
Rahhal, I., Kassou, I., & Ghogho, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124101 · Cited by 25 · see citing works
Foregrounding data in co-design – An exploration of how data may become an object of design
Seidelin, C., Dittrich, Y., & Grönvall, E. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102505 · Cited by 21 · see citing works
“The increasing use of data and the continuous growth of the field of data science has generated new opportunities for applying new tools to aggregate, combine, and visualize data (Cao 2017).”
BackgroundMethodThe arcanum of artificial intelligence in enterprise applications: Toward a unified framework
Herrmann, H. (2022) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2022.101716 · Cited by 21 · see citing works
Data Analytics Anywhere and Everywhere
Elmqvist, N. (2023) · Communications of the ACM · doi:10.1145/3584858 · Cited by 20 · see citing works
Reconciling the Promise and Pragmatics of Enhancing Computing Pedagogy with Data Science
Bart, A. C., Kafura, D. G., Shaffer, C. A., et al. (2018) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3159450.3159465 · Cited by 20 · see citing works
“Readers interested in a comprehensive overview of the field of Data Science, including its roles in both industry and classrooms, can refer to Cao’s survey paper [6].”
BackgroundRoman, D., Reeves, N., González, E., et al. (2021) · Data Technologies and Applications · doi:10.1108/dta-10-2020-0253 · Cited by 20 · see citing works
Data science in organizations: Conceptualizing its breakthroughs and blind spots
Cybulski, J. L., & Scheepers, R. (2021) · Journal of Information Technology · doi:10.1177/0268396220988539 · Cited by 19 · see citing works
“There are many different definitions of data science as a field of scientific and professional endeavor (e.g. Cao, 2017a; Donoho, 2017).”
BackgroundModel-based plant phenomics on morphological traits using morphometric descriptors
Noshita, K., Murata, H., & Kirié, S. (2022) · Ikushugaku zasshi · doi:10.1270/jsbbs.21078 · Cited by 20 · see citing works
“Although finding rules in plant morphology is generally the result of trial and error, data accumulation will accelerate the process (Cao 2017).”
BackgroundIntroduction to machine learning
Saifuzzaman, M., & Ananna, T. N. (2024) · Advances in computers · doi:10.1016/bs.adcom.2024.06.005 · Cited by 19 · see citing works
Bug Analysis in Jupyter Notebook Projects: An Empirical Study
de Santana, T. L., Neto, P. A. D. M. S., de Almeida, E. S., & Ahmed, I. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3641539 · Cited by 18 · see citing works
Key Concepts of Systemological Approach to CPS Adaptive Information Security Monitoring
Poltavtseva, M. A., Shelupanov, A. A., Сергеевич, Б. Д., Петрович, З. Д., & Alexandrova, E. (2021) · Symmetry · doi:10.3390/sym13122425 · Cited by 18 · see citing works
Data science curriculum in the iField
Zhang, Y., Wu, D., Hagen, L., Song, I., Mostafa, J., Oh, S. G., Anderson, T. D., Shah, C. S., Bishop, B. W., Hopfgartner, F., et al. (2022) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24701 · Cited by 17 · see citing works
“Despite the growing number of DS programs and courses, however, most such programs have been based on existing curricula from statistics, computing and informatics, business, and information technology programs (Cao, 2018b).”
BackgroundInfluentialData science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice
Bellomarini, L., Fayzrakhmanov, R. R., Gottlob, G., et al. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.021 · Cited by 17 · see citing works
Towards Ethical Data Ecosystems: A Literature Study
Rantanen, M. M., Hyrynsalmi, S., & Hyrynsalmi, S. M. (2019) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice.2019.8792599 · Cited by 17 · see citing works
Data Science and AI: Trends Analysis
Aparicio, S., Aparício, J. T., & Costa, C. J. D. C. (2019) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2019.8760820 · Cited by 16 · see citing works
Data science education through education data: an end-to-end perspective
Rao, A. R., Desai, Y., & Mishra, K. (2019) · IEEE Integrated STEM Education Conference (ISEC) · doi:10.1109/isecon.2019.8881970 · Cited by 16 · see citing works
“Given the vast amounts of data collected through devices powered by the internet-of-things, mobile phones, and social media, there is a growing need to extract knowledge from this data [22].”
BackgroundFrom Data Asset to Data Product – The Role of the Data Provider in the Enterprise Data Marketplace
Eichler, R., Gröger, C., Hoos, E., et al. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-18304-1_7 · Cited by 16 · see citing works
Data science pedagogical tools and practices: A systematic literature review
Memarian, B., & Doleck, T. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12102-y · Cited by 15 · see citing works
“With the availability of current computing and storage technology, data science is evolving at a rapid rate (Cao, 2017; Feyyad, 1996; Manyika et al., 2011).”
Aliferis, C., & Simon, G. (2024) · Health informatics · doi:10.1007/978-3-031-39355-6_1 · Cited by 16 · see citing works
Barreno-Alcalde, S., Diéz‐Martín, F., & Solano, S. E. (2024) · SAGE Open · doi:10.1177/21582440241284951 · Cited by 15 · see citing works
“( Cao, 2017).”
Chen, Y., Silva, E. A., & Reis, J. P. (2020) · Regional Science Policy & Practice · doi:10.1111/rsp3.12292 · Cited by 15 · see citing works
Data Science Skills and Graduate Certificates: A Quantitative Text Analysis
Jiang, H., & Chen, C. (2021) · Journal of Computer Information Systems · doi:10.1080/08874417.2020.1852628 · Cited by 15 · see citing works
“34,35 In summary, most studies in data science education focused on undergraduate program development, 20,22,36 and few studies had been devoted to certificate programs of data science.”
BackgroundZhu, R., Dong, Y., Guo, J., He, J., Chen, H., Wang, R., Ren, F., Raben, A., & Martínez, J. A. (2025) · Clinical Nutrition · doi:10.1016/j.clnu.2025.10.009 · Cited by 14 · see citing works
Advancing zoo animal welfare through data science: scaling up continuous improvement efforts
Liptovszky, M. (2024) · Frontiers in Veterinary Science · doi:10.3389/fvets.2024.1313182 · Cited by 14 · see citing works
Heterogeneous Knowledge Learning of Predictive Academic Intelligence in Transportation
Lü, H., Zhu, Y., Lin, Q., Wang, T., Niu, Z., & Herrera‐Viedma, E. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3041904 · Cited by 14 · see citing works
Al‐Debei, M. M. (2023) · Journal of Business Analytics · doi:10.1080/2573234x.2023.2285483 · Cited by 15 · see citing works
“For their analytical and business reporting purposes, typical business intelligence projects rely on descriptive analytics, including data reporting (Arnott et al., 2019; Cao, 2017; Delen & Demirkan, 2013).”
BackgroundInfluentialIntroducing the enterprise data marketplace: a platform for democratizing company data
Eichler, R., Gröger, C., Hoos, E., et al. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00843-z · Cited by 13 · see citing works
“An enormous amount of data is generated in the big data era by, for instance, the Internet of Things (IoT), social media networks, transactional processing systems, or wear-ables and mobile devices [7].”
BackgroundData science in healthcare: techniques, challenges and opportunities
Devi, P. P., & Bansal, K. L. (2024) · Health and Technology · doi:10.1007/s12553-024-00861-8 · Cited by 14 · see citing works
Li, H., Sun, H., Chen, J., Chen, B., Zhong, D., & Yang, M. (2024) · Energy & Fuels · doi:10.1021/acs.energyfuels.4c04924 · Cited by 15 · see citing works
What can Venn diagrams teach us about doing data science better?
Ho, S. Y., Tan, S., Sze, C. C., et al. (2020) · International Journal of Data Science and Analytics · doi:10.1007/s41060-020-00230-4 · Cited by 13 · see citing works
Rädler, S., & Rigger, E. (2022) · Proceedings of the Design Society · doi:10.1017/pds.2022.172 · Cited by 13 · see citing works
Internet en su vertiente científica: predicción y prescripción ante la complejidad
González, W. J. (2018) · ArtefaCToS Revista de estudios sobre la ciencia y la tecnología · doi:10.14201/art2018717597 · Cited by 12 · see citing works
Cao, L. (2018) · Data analytics · doi:10.1007/978-3-319-95092-1_3 · Cited by 12 · see citing works
Self-citationThe role of data science and data analytics for innovation: a literature review
Joergensen, P. N., & Zaggl, M. A. (2024) · Journal of Business Analytics · doi:10.1080/2573234x.2024.2365917 · Cited by 17 · see citing works
“However, without a clear understanding, ambiguities are inevitable, including over-and underestimating the potential of DS/DA (Cao, 2017; Pooja & Sheth, 2023; Provost & Fawcett, 2013).”
Hsu, Y.-C. (2024) · Education Sciences · doi:10.3390/educsci14070763 · Cited by 18 · see citing works
An Empirical Investigation on Business Analytics in Software and Systems Development Projects
Ahmad, M. O., Ahmad, I., Rana, N. P., & Khan, I. S. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10253-w · Cited by 14 · see citing works
Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection
Odia, J. O., & Akpata, O. T. (2020) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-3053-5.ch011 · Cited by 11 · see citing works
Data Science Competency in Organisations
Hattingh, M. J., Marshall, L., Holmner, M., & Naidoo, R. (2019) · South African Institute of Computer Scientists and Information Technologists · doi:10.1145/3351108.3351110 · Cited by 10 · see citing works
Ma, Q.-P., Nguyen, H.-S., Hajnyš, J., Měsíček, J., Pagáč, M., & Petrů, J. (2024) · Applied Materials Today · doi:10.1016/j.apmt.2024.102371 · Cited by 10 · see citing works
A Systematic Literature Review of Undergraduate Data Science Education Research
Dogucu, M., Demirci, S., Bendekgey, H., Ricci, F. Z., & Medina, C. M. (2025) · Journal of Statistics and Data Science Education · doi:10.1080/26939169.2025.2486656 · Cited by 11 · see citing works
“Data science is an interdisciplinary (Cao 2017) and relatively young field.”
Bringing Interdisciplinary Data Science Education Challenges into the Classroom
Bednarowska, Z., & Uprichard, E. (2025) · Journal of Statistics and Data Science Education · doi:10.1080/26939169.2025.2507366 · Cited by 10 · see citing works
“On the other hand, others such as Cao (2017) and Asamoah, Doran, and Schiller (2020) acknowledge that data science is an interdisciplinary field, but then fail to address interdisciplinarity itself as a distinct area of research data science pedagogy more generally.”
Beyond the Unicorn? Job Roles in Data Science
Gunklach, J., Nadj, M., Michalczyk, S., et al. (2025) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00954-2 · Cited by 10 · see citing works
“…nature requires a combination of skills, including mathematics and statistics, computer science, and the targeted application domain Mike and Haz-zan (2023) ‘‘to study data and its environments [ . . . ] by following a data-to-knowledge-to-wisdom thinking and methodology’’ (Cao 2017, 8).”
InfluentialConocimiento y actitudes de la ciudadanía española sobre el big data y la inteligencia artificial
Holgado, P. S., Calderón, C. A., & Blanco‐Herrero, D. (2022) · Revista ICONO14 · doi:10.7195/ri14.v21i1.1908 · Cited by 10 · see citing works
- Authors
- Cao, L.
- Affiliations
- University of Technology Sydney (AU)
- Corresponding author
- Cao, Longbing
- ORCID
- Cao, Longbing
- Venue
- ACM Computing Surveys
- Published
- 2017-06-29
- Volume
- 50
- Issue
- 3
- Pages
- 1-42
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3104305579
- Semantic Scholar
- 79be83a308a9a75ef4e64f63a938b201531c0bbf
- Cited by
- 407
- References
- 102
- Citations by year
- 2026: 38 · 2025: 58 · 2024: 61 · 2023: 61 · 2022: 55 · 2021: 52 · 2020: 33 · 2019: 25 · 2018: 21 · 2017: 3
- FWCI
- 36.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Computing Surveys (publishedVersion, cc-by-nc-sa)
- Funders
- Australian Research Council Discovery Grant (DP130102691)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Keywords
- data science, big data, data analytics, data economy, data science education, data science challenges
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