A fresh look at introductory data science

• URL: http://arxiv.org/abs/2008.00315v1
• Date: Sat, 1 Aug 2020 18:39:34 GMT
• Title: A fresh look at introductory data science
• Authors: Mine \c{C}etinkaya-Rundel and Victoria Ellison
• Abstract summary: We present a case study of an introductory undergraduate course in data science that is designed to address these needs. This course has no pre-requisites and serves a wide audience of aspiring statistics and data science majors as well as humanities, social sciences, and natural sciences students. We discuss the unique set of challenges posed by offering such a course and in light of these challenges, we present a detailed discussion into the pedagogical design elements, content, structure, computational infrastructure, and the assessment methodology of the course.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of vast quantities of available datasets that are large and complex in nature has challenged universities to keep up with the demand for graduates trained in both the statistical and the computational set of skills required to effectively plan, acquire, manage, analyze, and communicate the findings of such data. To keep up with this demand, attracting students early on to data science as well as providing them a solid foray into the field becomes increasingly important. We present a case study of an introductory undergraduate course in data science that is designed to address these needs. Offered at Duke University, this course has no pre-requisites and serves a wide audience of aspiring statistics and data science majors as well as humanities, social sciences, and natural sciences students. We discuss the unique set of challenges posed by offering such a course and in light of these challenges, we present a detailed discussion into the pedagogical design elements, content, structure, computational infrastructure, and the assessment methodology of the course. We also offer a repository containing all teaching materials that are open-source, along with supplemental materials and the R code for reproducing the figures found in the paper.

Related papers

• SciER: An Entity and Relation Extraction Dataset for Datasets, Methods, and Tasks in Scientific Documents [49.54155332262579]
  We release a new entity and relation extraction dataset for entities related to datasets, methods, and tasks in scientific articles. Our dataset contains 106 manually annotated full-text scientific publications with over 24k entities and 12k relations.
  arXiv  Detail & Related papers  (2024-10-28T15:56:49Z)
• SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature [80.49349719239584]
  We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks. SciRIFF is the first dataset focused on extracting and synthesizing information from research literature across a wide range of scientific fields.
  arXiv  Detail & Related papers  (2024-06-10T21:22:08Z)
• Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice [0.6597195879147557]
  We present insights and experiences from the Data Science Education Community of Practice (DSECOP) DSECOP brings together graduate students and physics educators from different institutions to share best practices and lessons learned from integrating data science into undergraduate physics education. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world.
  arXiv  Detail & Related papers  (2024-03-01T20:21:42Z)
• A compendium of data sources for data science, machine learning, and artificial intelligence [17.857341127079305]
  Recent advances in data science, machine learning, and artificial intelligence are leading to an increasing demand for data. Data sources are application-specific, and it is impossible to produce an exhaustive list of such data sources. The goal of this publication is to provide just such an (inevitably incomplete) list -- or compendium -- of data sources across multiple areas of applications.
  arXiv  Detail & Related papers  (2023-09-10T19:15:22Z)
• Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction [104.29108668347727]
  This paper proposes an innovative knowledge graph generation approach that leverages the potential of the latest generative large language models. The approach is conveyed in a pipeline that comprises novel iterative zero-shot and external knowledge-agnostic strategies. We claim that our proposal is a suitable solution for scalable and versatile knowledge graph construction and may be applied to different and novel contexts.
  arXiv  Detail & Related papers  (2023-07-03T16:01:45Z)
• Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data [91.52783572568214]
  Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
  arXiv  Detail & Related papers  (2023-04-07T16:38:40Z)
• The interdisciplinary quantum information classroom: Themes from a survey of quantum information science instructors [0.0]
  Interdisciplinary introduction to quantum information science (QIS) courses are proliferating at universities across the US. We report on the findings of a survey of instructors teaching introduction to QIS courses at institutions across the US.
  arXiv  Detail & Related papers  (2022-02-12T00:37:58Z)
• CateCom: a practical data-centric approach to categorization of computational models [77.34726150561087]
  We present an effort aimed at organizing the landscape of physics-based and data-driven computational models. We apply object-oriented design concepts and outline the foundations of an open-source collaborative framework.
  arXiv  Detail & Related papers  (2021-09-28T02:59:40Z)
• Toward Knowledge Discovery Framework for Data Science Job Market in the United States [1.7205106391379024]
  This paper introduces a framework to analyze the job market for data science-related jobs within the US. The proposed framework includes three sub-modules allowing continuous data collection, information extraction, and a web-based visualization dashboard. The current version of this application is deployed on the web and allows individuals and institutes to investigate skills required for data science positions.
  arXiv  Detail & Related papers  (2021-06-14T21:23:15Z)
• Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley [81.01051375191828]
  The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses. These courses emphasize computational thinking, inferential thinking, and working on real-world problems. These courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.
  arXiv  Detail & Related papers  (2021-02-13T22:51:24Z)
• Computational Skills by Stealth in Secondary School Data Science [16.960800464621993]
  We discuss a proposal for the stealth development of computational skills in students' first exposure to data science. The intent of this approach is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners.
  arXiv  Detail & Related papers  (2020-10-08T09:11:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.