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Showing 1 to 15 of 82 results Save | Export
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David Shilane; Nicole Di Crecchio; Nicole L. Lorenzetti – Teaching Statistics: An International Journal for Teachers, 2024
Educational curricula in data analysis are increasingly fundamental to statistics, data science, and a wide range of disciplines. The educational literature comparing coding syntaxes for instruction in data analysis recommends utilizing a simple syntax for introductory coursework. However, there is limited prior work to assess the pedagogical…
Descriptors: Programming, Data Science, Programming Languages, Coding
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Alexey L. Voskov – International Journal of Mathematical Education in Science and Technology, 2024
QR decomposition is widely used for solving the least squares problem. However, existing materials about it may be too abstract for non-mathematicians, especially STEM students, and/or require serious background in linear algebra. The paper describes theoretical background and examples of GNU Octave compatible MATLAB scripts that give relatively…
Descriptors: Mathematics, Algorithms, Data Science, Mathematical Concepts
Jordan, Altricia – ProQuest LLC, 2023
Data science, as a discipline can be used in any area. However, in order to utilize data science techniques, data scientist must be taught domain knowledge, referred to as a partner discipline, in the area with which the techniques are to be utilized. Using a quantitative analysis of publicly available information and survey methodology, this…
Descriptors: Data Science, Training, Scientists, Reliability
Joseph Santalucia – ProQuest LLC, 2022
The research is a descriptive correlational study that investigates new methods for the creation of innovation for institutions. The research addresses the need for organizations to generate a novel form of innovation for their competitiveness and survival. These novel methods in the generation of innovation include using an organization's big…
Descriptors: Data Science, Innovation, Data, Decision Making
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Hairui Yu; Suzanne E. Perumean-Chaney; Kathryn A. Kaiser – Journal of Statistics and Data Science Education, 2024
Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing…
Descriptors: Statistics Education, Data Science, Data Use, Research Problems
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Peralta, Lee Melvin Madayag – TechTrends: Linking Research and Practice to Improve Learning, 2023
The perceived importance of data in society has led to a surge in interest towards data science education. This article seeks to build on existing literature concerned with the sociopolitical, cultural, and ethical dimensions of data science education by considering the salience of two interrelated concepts discussed in Asian and Asian American…
Descriptors: Data Science, Asian Culture, Ethnic Stereotypes, Postcolonialism
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Pieterman-Bos, Annelies; van Mil, Marc H. W. – Science & Education, 2023
Biomedical data science education faces the challenge of preparing students for conducting rigorous research with increasingly complex and large datasets. At the same time, philosophers of science face the challenge of making their expertise accessible for scientists in such a way that it can improve everyday research practice. Here, we…
Descriptors: Philosophy, Science Education, Scientific Principles, Data Science
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Mary Glantz; Jennifer Johnson; Marilyn Macy; Juan J. Nunez; Rachel Saidi; Camilo Velez – Journal of Statistics and Data Science Education, 2023
Two-year colleges provide the opportunity for students of all ages to try new subjects, change careers, upskill, or begin exploring higher education, at affordable rates. Many might begin their exploration by taking a course at a local two-year college. Currently, not many of these institutions in the U.S. offer data science courses. This article…
Descriptors: Two Year Colleges, Data Science, Two Year College Students, Student Experience
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Barb Bennie; Richard A. Erickson – Journal of Statistics and Data Science Education, 2024
Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area…
Descriptors: Undergraduate Students, Statistics Education, Data Science, Earth Science
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Byran J. Smucker; Nathaniel T. Stevens; Jacqueline Asscher; Peter Goos – Journal of Statistics and Data Science Education, 2023
The design and analysis of experiments (DOE) has historically been an important part of an education in statistics, and with the increasing complexity of modern production processes and the advent of large-scale online experiments, it continues to be highly relevant. In this article, we provide an extensive review of the literature on DOE…
Descriptors: Statistics Education, Data Science, Experiments, Teaching Methods
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Gail Burrill; Maxine Pfannkuch – ZDM: Mathematics Education, 2024
The rapidly increasing capacity of technology to collect, organize, and manage data has spurred changes in the practice of statistics: new methods of collecting data, large data sets, new forms of data, different ways to visualize and represent data, and recognition of the importance of being able to understand and to communicate data-based…
Descriptors: Statistics Education, Educational Trends, Data Science, Context Effect
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Bahar Memarian; Tenzin Doleck – Education and Information Technologies, 2024
The development of data science curricula has gained attention in academia and industry. Yet, less is known about the pedagogical practices and tools employed in data science education. Through a systematic literature review, we summarize prior pedagogical practices and tools used in data science initiatives at the higher education level.…
Descriptors: Data Science, Teaching Methods, Literature Reviews, Curriculum Development
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Herfort, Jonas Dreyøe; Tamborg, Andreas Lindenskov; Meier, Florian; Allsopp, Benjamin Brink; Misfeldt, Morten – Educational Studies in Mathematics, 2023
Mathematics education is like many scientific disciplines witnessing an increase in scientific output. Examining and reviewing every paper in an area in detail are time-consuming, making comprehensive reviews a challenging task. Unsupervised machine learning algorithms like topic models have become increasingly popular in recent years. Their…
Descriptors: Mathematics Education, Technology Uses in Education, Artificial Intelligence, Algorithms
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Ihrmark, Daniel; Tyrkkö, Jukka – Education for Information, 2023
The combination of the quantitative turn in linguistics and the emergence of text analytics has created a demand for new methodological skills among linguists and data scientists. This paper introduces KNIME as a low-code programming platform for linguists interested in learning text analytic methods, while highlighting the considerations…
Descriptors: Linguistics, Data Science, Programming, Data Analysis
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Pargman, Teresa Cerratto; McGrath, Cormac; Viberg, Olga; Knight, Simon – Journal of Learning Analytics, 2023
The focus of ethics in learning analytics (LA) frameworks and guidelines is predominantly on procedural elements of data management and accountability. Another, less represented focus is on the duty to act and LA as a moral practice. Data feminism as a critical theoretical approach to data science practices may offer LA research and practitioners…
Descriptors: Learning Analytics, Responsibility, Feminism, Ethics
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