Data science

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Not to be confused with information science.

The existence of Comet NEOWISE (here depicted as a series of red dots) was discovered by analyzing astronomical survey data acquired by a space telescope, the Wide-field Infrared Survey Explorer.

Data science is an interdisciplinary academic field[1] that uses statistics, scientific computing, scientific methods, processes, 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]

History[edit]

1962[edit]

In 1962, John Tukey authored a paper called The Future of Data Analysis describing his view on the shift of Statistics and its effects on Computer science. Tukey repeatedly refers to the field of Data analysis as a mix of Statistics and Computing to help guide observation, experimentation, and analysis [10]. In Tukey’s view, Statistics has contributed much to Data analysis in many direct and indirect ways. Some of the well-noted examples he gives are the work of Mann and Wald (1942) on the asymptotic power of chi-squared goodness of fit, the development of a general theory of decisions under the leadership of Wald, and the development of effective procedures for determining properties of samples. Tukey also goes on to say that great innovations in Statistics do not always correspond to great innovations in Data Analysis with the exception of “sums of squares” [10].

The paper references problems that Data Scientists face to this day. Tukey calls the data generation process a “stochastic process” that deserves more attention. Data that is not uniform or data that is incomplete offer challenges [10]. Tukey claims that data should be studied as it occurs but not as how it should be because data is sometimes affected by personal idiosyncrasies and with the intent to make a point. However, he also calls for growth in this field hoping for data selection to be unbiased.

1974[edit]

In 1974, Peter Naur authored the Concise Survey of Computer Methods. This was one of the first publications where the author used the term Data Science repeatedly. Naur states the most fundamental principle of Data Science as “The data representation must be chosen with due regard to the transformation to be achieved and the data processing tools available.” [11] The author then goes on to make 3 remarks about Data Science.

(i) The author thinks that data science is primarily concerned with constructing data processes; hence, basic principles are involved in designing these data processes.

(ii) These principles give us the freedom to choose our data representation.

(iii) Data processing tools are consistent with the idea that data are things to be processed [11].

1977[edit]

In 1977, the IASC, or International Association for Statistical Computing, was established with a mission that includes integrating conventional statistical methods, contemporary computer technology, and expertise from various fields to transform data into meaningful information and knowledge [12].

In 1977, Tukey wrote a second paper called Exploratory Data Analysis the author emphasizes the significance of employing data to determine which hypothesis to test. They advocate for collaborative efforts between confirmatory data analysis and exploratory data analysis.

1989[edit]

In 1989, the Knowledge Discovery in Databases, which would make its way into the ACM SIGKDD Conference on Knowledge Discovery and Data Mining organized its first workshop [12].

Knowledge discovery in databases refers to the challenging task of identifying valid, original, possibly valuable, and ultimately comprehensible patterns or relationships within a dataset, aiming to inform important decision-making processes [13]. Data science encompasses the inference and iterative testing of numerous hypotheses. A critical aspect of data science lies in the process of generalizing patterns from a dataset, ensuring this generalization remains applicable not only to the observed data but also to new, unseen data. Data science operates through structured steps, each involving specific tasks. The term "novel" indicates that data science often uncovers previously undiscovered patterns within data. Ultimately, the goal of data science is to derive potentially valuable insights that can inform actionable decisions for users of the analysis [14].

1994[edit]

In 1994, Business Week featured a cover story titled Database marketing exposing the alarming trend of companies collecting vast quantities of personal data and intending to launch unconventional marketing initiatives. Company executives were left baffled by the scale of data, not understanding how to process it and use it effectively. Database marketing is an organized approach to the gathering, consolidation, and processing of consumer data. Data of both potential customers and current customers is gathered and maintained in the company’s database. The company aims to use this personalized data to better understand how to market to its clients and potential clients [15].

Database Marketing has several benefits. It helps companies distinguish themselves from the noise and deliver result-oriented strategies. Companies can organize data into various categories like previous years, customer demographics, customer shopping habits, etc. to make an analysis [16].

1999[edit]

In 1999, Jacob Zahavi noted the need to develop better algorithms that can process and analyze huge datasets as fast as possible. Companies have found various use cases for data science. For instance, Capital One used data science and data mining techniques to send high-interest rate offers to high-risk customers and low-interest rate offers to low-risk customers. The Pharmaceutical industry also uses data mining to find the key compounds that have the best combined effect from a database that is impossible to search manually. The reason for Zahavi calling for better tools is the explosion of information due to the Internet. With the Internet, data is easier to capture than ever, and due to the widespread reach that the Internet offers means that the customer has more choices to choose from. Hence there is a need for special data mining and data science tools to achieve the most effective and quick results in today’s digital book [17].

2001[edit]

In 2001, William S. Cleveland laid out plans for training Data Scientists to meet the needs of the future. He presented an action plan titled, Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics to increase the technical experience and range of data analysts. Cleveland says, “A basic premise is that technical areas of data science should be judged by the extent to which they enable the analyst to learn from data”. The author calls for change in the field of data science because there are critical fields that could massively benefit if data science techniques are applied the right way. The author chose universities as the best means to train upcoming data analysts into data scientists because they have historically been primary hubs for innovation, capable of swiftly shifting focus areas by updating the curriculum for data science graduates. As a result of this program, the author hopes to achieve improvements in two areas of data science – models and methods, and computing with large sets of data [18].

2002[edit]

In 2002, the International Council for Science: Committee on Data for Science and Technology initiated the publication of the Data Science Journal. This journal concentrates on topics like data system descriptions, online publishing, applications, and legal concerns. The editors of the Data Science Journal accept articles that adhere to particular guidelines [12].

2006[edit]

In 2006, Hadoop 0.1.0, an open-source, non-relational database, was launched. It was built upon Nutch, another open-source database. The challenges related to handling large volumes of data include storing immense amounts of data and efficiently processing it afterward. Hadoop effectively addressed these issues. Since it is based on the MapReduce programming model it allows for the parallel processing of large datasets. Hadoop is used for handling extensive data and analytical tasks. It divides tasks into smaller components that can run concurrently. By clustering multiple computers, Hadoop accelerates the parallel analysis of massive datasets [19]. It has since evolved into Apache Hadoop, an open-source software library enabling the exploration and analysis of big data.

2008[edit]

In 2008, the title, “Data Scientist” became a buzzword, and eventually a part of the language. DJ Patil of LinkedIn and Jeff Hammerbacher of Facebook are given credit for initiating its use as a buzzword [12].

2011[edit]

In 2011, there was a 15,000% surge in job postings for data scientists. Additionally, there was a notable rise in seminars and conferences specifically dedicated to Data Science and big data. Data Science had established itself as a lucrative field and had become ingrained in corporate culture. Furthermore, in 2011, James Dixon, the Chief Technology Officer (CTO) of Pentaho, advocated for the concept of data lakes over data warehouses. Dixon highlighted the distinction between a data warehouse and a data lake, emphasizing that a data warehouse categorizes data upon entry, resulting in time and energy wastage, whereas a data lake utilizes a non-relational database (NoSQL) to simply store data without categorization [12].

Foundations[edit]

Data science is an interdisciplinary field[20] focused on extracting knowledge from typically large data sets and applying the knowledge and insights from that data to solve problems in a wide range of application domains. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. As such, it incorporates skills from computer science, statistics, information science, mathematics, data visualization, information visualization, data sonification, data integration, graphic design, complex systems, communication and business.[21][22] Statistician Nathan Yau, drawing on Ben Fry, also links data science to human–computer interaction: users should be able to intuitively control and explore data.[23][24] In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.[25]

Relationship to statistics[edit]

Many statisticians, including Nate Silver, have argued that data science is not a new field, but rather another name for statistics.[26] Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data.[27] Vasant Dhar writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g., from images, text, sensors, transactions, customer information, etc.) and emphasizes prediction and action.[28] Andrew Gelman of Columbia University has described statistics as a non-essential part of data science.[29]

Stanford professor David Donoho writes that data science is not distinguished from statistics by the size of datasets or use of computing and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data-science program. He describes data science as an applied field growing out of traditional statistics.[30]

Etymology[edit]

Early usage[edit]

In 1962, John Tukey described a field he called "data analysis", which resembles modern data science.[30] In 1985, in a lecture given to the Chinese Academy of Sciences in Beijing, C. F. Jeff Wu used the term "data science" for the first time as an alternative name for statistics.[31] Later, attendees at a 1992 statistics symposium at the University of Montpellier  II acknowledged the emergence of a new discipline focused on data of various origins and forms, combining established concepts and principles of statistics and data analysis with computing.[32][33]

The term "data science" has been traced back to 1974, when Peter Naur proposed it as an alternative name to computer science.[6] In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic.[6] However, the definition was still in flux. After the 1985 lecture at the Chinese Academy of Sciences in Beijing, in 1997 C. F. Jeff Wu again suggested that statistics should be renamed data science. He reasoned that a new name would help statistics shed inaccurate stereotypes, such as being synonymous with accounting or limited to describing data.[34] In 1998, Hayashi Chikio argued for data science as a new, interdisciplinary concept, with three aspects: data design, collection, and analysis.[33]

During the 1990s, popular terms for the process of finding patterns in datasets (which were increasingly large) included "knowledge discovery" and "data mining".[6][35]

Modern usage[edit]

In 2012, technologists Thomas H. Davenport and DJ Patil declared "Data Scientist: The Sexiest Job of the 21st Century",[36] a catchphrase that was picked up even by major-city newspapers like the New York Times[37] and the Boston Globe.[38] A decade later, they reaffirmed it, stating that "the job is more in demand than ever with employers".[39]

The modern conception of data science as an independent discipline is sometimes attributed to William S. Cleveland.[40] In a 2001 paper, he advocated an expansion of statistics beyond theory into technical areas; because this would significantly change the field, it warranted a new name.[35] "Data science" became more widely used in the next few years: in 2002, the Committee on Data for Science and Technology launched the Data Science Journal. In 2003, Columbia University launched The Journal of Data Science.[35] In 2014, the American Statistical Association's Section on Statistical Learning and Data Mining changed its name to the Section on Statistical Learning and Data Science, reflecting the ascendant popularity of data science.[41]

The professional title of "data scientist" has been attributed to DJ Patil and Jeff Hammerbacher in 2008.[42] Though it was used by the National Science Board in their 2005 report "Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century", it referred broadly to any key role in managing a digital data collection.[43]

There is still no consensus on the definition of data science, and it is considered by some to be a buzzword.[44] Big data is a related marketing term.[45] Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.[46]

Data science and data analysis[edit]

summary statistics and scatterplots showing the Datasaurus dozen data set
Example for the usefulness of exploratory data analysis as demonstrated using the Datasaurus dozen data set

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.[47][48]

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.[47]

Data science, on the other hand, is a more complex and iterative process that involves working with larger, more complex datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models and make data-driven decisions. In addition to statistical analysis, data science often involves tasks such as data preprocessing, feature engineering, and model selection. For instance, a data scientist might develop a recommendation system for an e-commerce platform by analyzing user behavior patterns and using machine learning algorithms to predict user preferences.[48][49]

While data analysis focuses on extracting insights from existing data, data science goes beyond that by incorporating the development and implementation of predictive models to make informed decisions. Data scientists are often responsible for collecting and cleaning data, selecting appropriate analytical techniques, and deploying models in real-world scenarios. They work at the intersection of mathematics, computer science, and domain expertise to solve complex problems and uncover hidden patterns in large datasets.[48]

Despite these differences, data science and data analysis are closely related fields and often require similar skill sets. Both fields require a solid foundation in statistics, programming, and data visualization, as well as the ability to communicate findings effectively to both technical and non-technical audiences. Both fields benefit from critical thinking and domain knowledge, as understanding the context and nuances of the data is essential for accurate analysis and modeling.[47][48]

In summary, data analysis and data science are distinct yet interconnected disciplines within the broader field of data management and analysis. Data analysis focuses on extracting insights and drawing conclusions from structured data, while data science involves a more comprehensive approach that combines statistical analysis, computational methods, and machine learning to extract insights, build predictive models, and drive data-driven decision-making. Both fields use data to understand patterns, make informed decisions, and solve complex problems across various domains.

Cloud Computing for Data Science[edit]

A cloud-based architecture for enabling big data analytics. Data flows from various sources, such as personal computers, laptops, and smart phones, through cloud services for processing and analysis, finally leading to various big data applications.

Cloud computing can offer access to large amounts of computational power and storage.[50] In big data, where volumes of information are continually generated and processed, these platforms can be used to handle complex and resource-intensive analytical tasks.[51]

Some distributed computing frameworks are designed to handle big data workloads. These frameworks can enable data scientists to process and analyze large datasets in parallel, which can reducing processing times.[52]





Limitations in Data Science[edit]

1) Data Science is a Massive Field[edit]

It encompasses extensive areas such as big data, data mining, machine learning, and numerous other subjects. Employing scientific techniques, processes, algorithms, and systems, it aims to derive knowledge and insights from both structured and unstructured data sources [53].

2) Data Security[edit]

Data serves as the fundamental element capable of enhancing industry productivity and revenue through transformative business decisions. It is also the basic raw material for Data Science. However, the information or insights derived from data can be exploited to the detriment of organizations, groups, committees, and so forth. There is also the threat of confidential or private data being leaked to the Internet [54].

3) Interpersonal Interpretations of Subjective Data[edit]

This problem is rooted in Human Psychology. We have different backgrounds, and upbringings that shape who we are. This is why we have personal idiosyncrasies and characteristics. A famous analogy is when William James stumbled upon the problem of measuring pain in 1898. Doctors may ask you to give them an estimate of your pain on a scale from 1-10. But not all people feel pain the same way for instance a soldier who went to war may have a different interpretation of pain than a pharmacist. Similarly, after childbirth, a woman may refer to any pain as 3 or 4. This personal bias in data poses a risk to Data Science [55].

4) Need a Common Goal[edit]

The company implementing Data Science should agree on a common goal and on the rationale behind why they want to achieve that goal. Data-driven models might learn different ways to achieve a goal. For example, if a factory is looking for ways to maximize production, the model might learn how to maximize profits [55]. So data-driven methods can find different patterns but the leaders implementing the decisions based on the patterns should be prudent by making sure that the recommended action takes them one step closer to their goal or not.

Ethical consideration in Data Science[edit]

Data science involve collecting, processing, and analyzing data which often including personal and sensitive information. Ethical concerns include potential privacy violations, bias perpetuation, and negative societal impacts [56][57]

Machine learning models can amplify existing biases present in training data, leading to discriminatory or unfair outcomes.[58][59]

Real-world applications[edit]

Data science algorithms are powerful tools that can be applied across various domains where ample data is either present or collectible. Industries such as finance, commerce, marketing, project management, insurance, medicine, education, manufacturing, HR, linguistics, and sociology, among others, can significantly benefit from gathering, analyzing, and modeling their data. Several variables within a company can be analyzed using data science techniques tailored to the company's industry. These use cases may involve solving a problem, verifying a hypothesis, or answering a question. It's worth noting that data science focuses on solving practical, real-world challenges [60].

This is an example of a diagnosis of a patient's eye. Most boxes indicate the labels the AI assigns to different parts of the OCT scan. However, in the top left corner, you can see the system's recommendations and different confidence levels. Image: UCL, Moorfields, DeepMind, et al

Healthcare[edit]

  • Disease Prediction and Diagnosis: Data science plays a crucial role in the healthcare industry, offering a range of benefits to healthcare professionals and patients alike. For instance, it enables the analysis of medical records, imaging data (such as X-rays and MRIs), and genetic information, which can help predict disease risk, identify potential outbreaks, and improve early diagnosis of diseases. Machine learning algorithms can also be used to detect patterns in medical images that may indicate the presence of tumors or other abnormalities [61].
  • Personalized Medicine: Data science helps in the shift towards personalized medicine by analyzing individual patient data to tailor treatments and preventative measures to their specific needs. This can be achieved by analyzing a patient's unique data, including their medical history, genetic makeup, lifestyle, and environmental factors. This approach allows healthcare professionals to provide the most effective treatment to the patient [62].
  • Drug Discovery and Development: Data science streamlines the drug discovery and development process by analyzing vast datasets of molecular structures and biological interactions to identify potential drug candidates. This reduces the time and cost associated with traditional methods, making it faster and more efficient to develop new drugs and treatments [63].

Careers in Data Science[edit]

According to the United States Bureau of Labor Statistics, the field of data science is expected to experience a growth of 36% by 2031 [64], which is significantly higher than the average growth rate for all occupations. The data science industry offers diverse career opportunities with the potential for rapid career progression. The chief data officer role, a relatively new position in the C-suite, has become increasingly important across all types of businesses. With the specialized skill set required in this high-demand field, professionals holding data science degrees or certificates are more likely to secure coveted positions in top companies and enjoy greater job security. Every business, government agency and educational institution generates data, and there is a need to gain insights from such data. With the proliferation of data science, individuals with a degree or certificate in this field can enjoy the flexibility to work in an industry that aligns with their interests and aspirations. Here are some of the data science careers:

  • Data Analyst: Data analysts manipulate company or industry data to perform analyses that help answer specific business questions. They mainly utilize programming languages and frameworks to review data and draw conclusions. Once the analysis is complete, they present the results to management teams, who use this information to enhance a company's strategies, processes, or operations [65].
  • Data Architect: A data architect is responsible for designing, creating, deploying, and maintaining a company's data architecture. Their primary function is determining how the company captures, organizes, and integrates data. Data architects hold senior positions on data science teams and are responsible for defining a company's data standards and principles. Apart from developing architectures, their duties include designing warehousing solutions and performing data modeling [66].
  • Data Engineer: Data engineers are responsible for developing and maintaining an organization's data infrastructure and interfaces, which are crucial in determining the methodologies employed by the business for data collection and storage. They are tasked with constructing data pipelines that facilitate the transformation of raw, unstructured data into formats accessible and usable by data scientists and analysts. Additionally, data engineers execute batch or real-time processing on the accumulated data and identify trends within datasets [67].
  • Data Journalists: Data journalists employ and scrutinize statistical data to deliver objective and comprehensive reporting and news writing. They utilize programming to automate the collection and amalgamation of information. Furthermore, data journalists utilize software tools to unearth links between documents and concepts [68].
  • Data Scientist: Data scientists analyze data and construct machine learning models, which enables them to forecast future trends. These activities support organizations in formulating novel business strategies and establishing long-term objectives. Additionally, data scientists develop customized data solutions, thereby enhancing an organization's comprehension of its customer base [69].

See also[edit]

References[edit]

  1. ^ Donoho, David (2017). "50 Years of Data Science". Journal of Computational and Graphical Statistics. 26 (4): 745–766. doi:10.1080/10618600.2017.1384734. S2CID 114558008.
  2. ^ Dhar, V. (2013). "Data science and prediction". Communications of the ACM. 56 (12): 64–73. doi:10.1145/2500499. S2CID 6107147. Archived from the original on 9 November 2014. Retrieved 2 September 2015.
  3. ^ Danyluk, A.; Leidig, P. (2021). Computing Competencies for Undergraduate Data Science Curricula (PDF). ACM Data Science Task Force Final Report (Report).
  4. ^ Mike, Koby; Hazzan, Orit (20 January 2023). "What is Data Science?". Communications of the ACM. 66 (2): 12–13. doi:10.1145/3575663. ISSN 0001-0782.
  5. ^ Hayashi, Chikio (1 January 1998). "What is Data Science ? Fundamental Concepts and a Heuristic Example". In Hayashi, Chikio; Yajima, Keiji; Bock, Hans-Hermann; Ohsumi, Noboru; Tanaka, Yutaka; Baba, Yasumasa (eds.). Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer Japan. pp. 40–51. doi:10.1007/978-4-431-65950-1_3. ISBN 9784431702085.
  6. ^ a b c d Cao, Longbing (29 June 2017). "Data Science: A Comprehensive Overview". ACM Computing Surveys. 50 (3): 43:1–43:42. arXiv:2007.03606. doi:10.1145/3076253. ISSN 0360-0300. S2CID 207595944.
  7. ^ Tony Hey; Stewart Tansley; Kristin Michele Tolle (2009). The Fourth Paradigm: Data-intensive Scientific Discovery. Microsoft Research. ISBN 978-0-9825442-0-4. Archived from the original on 20 March 2017.
  8. ^ Bell, G.; Hey, T.; Szalay, A. (2009). "Computer Science: Beyond the Data Deluge". Science. 323 (5919): 1297–1298. doi:10.1126/science.1170411. ISSN 0036-8075. PMID 19265007. S2CID 9743327.
  9. ^ Davenport, Thomas H.; Patil, D. J. (October 2012). "Data Scientist: The Sexiest Job of the 21st Century". Harvard Business Review. 90 (10): 70–76, 128. PMID 23074866. Retrieved 18 January 2016.
  10. ^ a b c Tukey, John (1962). The Future of Data Analysis. The Annals of Mathematical Statistics 33. pp. 1–67.
  11. ^ a b Naur, Peter (1975). Concise Survey of Computer Methods. Studentlitteratur, Lund, Sweden. pp. 397 p. ISBN 91-44-07881-1.
  12. ^ a b c d e A Brief History of Data Science
  13. ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:https://doi.org/10.1609/aimag.v17i3.1230, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with |doi=https://doi.org/10.1609/aimag.v17i3.1230 instead.
  14. ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:https://doi.org/10.1016/C2017-0-02113-4, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with |doi=https://doi.org/10.1016/C2017-0-02113-4 instead.
  15. ^ database marketing
  16. ^ [1]
  17. ^ [2]
  18. ^ Attention: This template ({{cite doi}}) is deprecated. To cite the publication identified by doi:https://doi.org/10.2307/1403527, please use {{cite journal}} (if it was published in a bona fide academic journal, otherwise {{cite report}} with |doi=https://doi.org/10.2307/1403527 instead.
  19. ^ [3]
  20. ^ Emmert-Streib, Frank; Dehmer, Matthias (2018). "Defining data science by a data-driven quantification of the community". Machine Learning and Knowledge Extraction. 1: 235–251. doi:10.3390/make1010015.
  21. ^ "1. Introduction: What Is Data Science?". Doing Data Science [Book]. O’Reilly. Retrieved 3 April 2020.
  22. ^ "the three sexy skills of data geeks". m.e.driscoll: data utopian. 27 May 2009. Retrieved 3 April 2020.
  23. ^ Yau, Nathan (4 June 2009). "Rise of the Data Scientist". FlowingData. Retrieved 3 April 2020.
  24. ^ "Basic Example". benfry.com. Retrieved 3 April 2020.
  25. ^ "ASA Statement on the Role of Statistics in Data Science". AmStatNews. American Statistical Association. 1 October 2015. Archived from the original on 20 June 2019. Retrieved 29 May 2019.
  26. ^ "Nate Silver: What I need from statisticians". Statistics Views. Retrieved 3 April 2020.
  27. ^ "What's the Difference Between Data Science and Statistics?". Priceonomics. 13 October 2015. Retrieved 3 April 2020.
  28. ^ Vasant Dhar (1 December 2013). "Data science and prediction". Communications of the ACM. 56 (12): 64–73. doi:10.1145/2500499. S2CID 6107147.
  29. ^ "Statistics is the least important part of data science « Statistical Modeling, Causal Inference, and Social Science". statmodeling.stat.columbia.edu. Retrieved 3 April 2020.
  30. ^ a b Donoho, David (18 September 2015). "50 years of Data Science" (PDF). Retrieved 2 April 2020.
  31. ^ Wu, C. F. Jeff (1986). "Future directions of statistical research in China: a historical perspective" (PDF). Application of Statistics and Management. 1: 1–7. Retrieved 29 November 2020.
  32. ^ Escoufier, Yves; Hayashi, Chikio; Fichet, Bernard, eds. (1995). Data science and its applications. Tokyo: Academic Press/Harcourt Brace. ISBN 0-12-241770-4. OCLC 489990740.
  33. ^ a b Murtagh, Fionn; Devlin, Keith (2018). "The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development". Big Data and Cognitive Computing. 2 (2): 14. doi:10.3390/bdcc2020014.
  34. ^ Wu, C. F. Jeff. "Statistics=Data Science?" (PDF). Retrieved 2 April 2020.
  35. ^ a b c Press, Gil. "A Very Short History of Data Science". Forbes. Retrieved 3 April 2020.
  36. ^ Davenport, Thomas (1 October 2012). "Data Scientist: The Sexiest Job of the 21st Century". Harvard Business Review. Retrieved 10 October 2022.
  37. ^ Miller, Claire (4 April 2013). "Data Science: The Numbers of Our Lives". New York Times. New York City. Retrieved 10 October 2022.
  38. ^ Borchers, Callum (11 November 2015). "Behind the scenes of the 'sexiest job of the 21st century'". Boston Globe. Boston. Retrieved 10 October 2022.
  39. ^ Davenport, Thomas (15 July 2022). "Is Data Scientist Still the Sexiest Job of the 21st Century?". Harvard Business Review. Retrieved 10 October 2022.
  40. ^ Gupta, Shanti (11 December 2015). "William S. Cleveland". Retrieved 2 April 2020.
  41. ^ Talley, Jill (1 June 2016). "ASA Expands Scope, Outreach to Foster Growth, Collaboration in Data Science". Amstat News. American Statistical Association.. In 2013 the first European Conference on Data Analysis (ECDA2013) started in Luxembourg the process which founded the European Association for Data Science (EuADS) www.euads.org in Luxembourg in 2015.
  42. ^ Davenport, Thomas H.; Patil, D. J. (1 October 2012). "Data Scientist: The Sexiest Job of the 21st Century". Harvard Business Review. No. October 2012. ISSN 0017-8012. Retrieved 3 April 2020.
  43. ^ "US NSF – NSB-05-40, Long-Lived Digital Data Collections Enabling Research and Education in the 21st Century". www.nsf.gov. Retrieved 3 April 2020.
  44. ^ Press, Gil. "Data Science: What's The Half-Life of a Buzzword?". Forbes. Retrieved 3 April 2020.
  45. ^ Pham, Peter. "The Impacts of Big Data That You May Not Have Heard Of". Forbes. Retrieved 3 April 2020.
  46. ^ Martin, Sophia (20 September 2019). "How Data Science will Impact Future of Businesses?" (PDF). Medium. Retrieved 3 April 2020.
  47. ^ a b c Gareth, Hastie; Witten, Tibshira (29 September 2017). "An Introduction to Statistical Learning: with Applications in R." Springer.
  48. ^ a b c d Provost, Foster; Tom Fawcett (1 August 2013). "Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking". O'Reilly Media, Inc.
  49. ^ Han, Kamber; Pei (2011). Data Mining: Concepts and Techniques. ISBN 9780123814791.
  50. ^ Hashem, Ibrahim Abaker Targio; Yaqoob, Ibrar; Anuar, Nor Badrul; Mokhtar, Salimah; Gani, Abdullah; Ullah Khan, Samee. "The rise of "big data" on cloud computing: Review and open research issues". Information Systems. 47: 98–115. doi:10.1016/j.is.2014.07.006.
  51. ^ Qiu, Junfei; Wu, Qihui; Ding, Guoru; Xu, Yuhua; Feng, Shuo. "A survey of machine learning for big data processing". EURASIP Journal on Advances in Signal Processing. 2016 (1). doi:10.1186/s13634-016-0355-x. ISSN 1687-6180.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  52. ^ Armbrust, Michael; Xin, Reynold S.; Lian, Cheng; Huai, Yin; Liu, Davies; Bradley, Joseph K.; Meng, Xiangrui; Kaftan, Tomer; Franklin, Michael J.; Ghodsi, Ali; Zaharia, Matei (27 May 2015). "Spark SQL: Relationhttps://en.wikipedia.org/wiki/Big_dataal Data Processing in Spark". Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM: 1383–1394. doi:10.1145/2723372.2742797. ISBN 978-1-4503-2758-9. {{cite journal}}: External link in |title= (help)
  53. ^ Data Science pros and cons
  54. ^ Data Science advantages and disadvantages
  55. ^ a b Limits of Data Science
  56. ^ Floridi, Luciano; Taddeo, Mariarosaria (28 December 2016). "What is data ethics?". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 374 (2083): 20160360. doi:10.1098/rsta.2016.0360. ISSN 1364-503X. PMC 5124072. PMID 28336805.
  57. ^ Mittelstadt, Brent Daniel; Floridi, Luciano. "The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts". Science and Engineering Ethics. 22 (2): 303–341. doi:10.1007/s11948-015-9652-2. ISSN 1353-3452.
  58. ^ Barocas, Solon; Selbst, Andrew D (2016). "Big Data's Disparate Impact". California Law Review. doi:10.15779/Z38BG31.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  59. ^ Caliskan, Aylin; Bryson, Joanna J.; Narayanan, Arvind (14 April 2017). "Semantics derived automatically from language corpora contain human-like biases". Science. 356 (6334): 183–186. doi:10.1126/science.aal4230. ISSN 0036-8075.
  60. ^ Kosourova, Elena (April 2022). "Data Science Use Cases Guide". DataCamp. Retrieved 24 April 2024.{{cite web}}: CS1 maint: url-status (link)
  61. ^ Vincent, James (13 August 2018). "DeepMind's AI can detect over 50 eye diseases as accurately as a doctor". The Verge. Retrieved 25 April 2024.
  62. ^ Fröhlich, Holger; Balling, Rudi; Beerenwinkel, Niko; Kohlbacher, Oliver; Kumar, Santosh; Lengauer, Thomas; Maathuis, Marloes H.; Moreau, Yves; Murphy, Susan A.; Przytycka, Teresa M.; Rebhan, Michael; Röst, Hannes; Schuppert, Andreas; Schwab, Matthias; Spang, Rainer (27 August 2018). "From hype to reality: data science enabling personalized medicine". BMC medicine. 16 (1): 150. doi:10.1186/s12916-018-1122-7. ISSN 1741-7015. PMC 6109989. PMID 30145981.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  63. ^ "How data science is ushering in a new era of modern medicine". JNJ.com. 10 May 2022. Retrieved 25 April 2024.
  64. ^ "Data Scientists : Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics". Bureau of Labor Statistics. Retrieved 25 April 2024.
  65. ^ "What Does a Data Analyst Do? Your 2024 Career Guide". Coursera. 1 April 2024. Retrieved 25 April 2024.
  66. ^ "Data Science Jobs: What is a Data Architect?". School of Data Science. Retrieved 25 April 2024.
  67. ^ "What Is a Data Engineer? A Guide to This In-Demand Career". Coursera. 15 March 2024. Retrieved 25 April 2024.
  68. ^ "What is Data Journalism? | A Journey into the World of Data-Driven Storytelling | Uncovering Stories Hidden in the Numbers". www.linkedin.com. Retrieved 25 April 2024.
  69. ^ Bowne-Anderson, Hugo (15 August 2018). "What Data Scientists Really Do, According to 35 Data Scientists". Harvard Business Review. ISSN 0017-8012. Retrieved 25 April 2024.
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