Data Science Curriculum

2020 ◽  
pp. 75-108
Author(s):  
Tomasz Wiktorski ◽  
Yuri Demchenko ◽  
Juan J. Cuadrado-Gallego
Keyword(s):  
Data Science ◽  
Science Curriculum

Data Science Curriculum Design: A Case Study

2021 ◽  
Author(s):  
Ismail Bile Hassan ◽  
Thanaa Ghanem ◽  
David Jacobson ◽  
Simon Jin ◽  
Katherine Johnson ◽  
...  
Keyword(s):  
Curriculum Design ◽  
Data Science ◽  
Science Curriculum

scholarly journals Enhancing Quantitative and Data Science Education for Graduate Students in Biomedical Science

2021 ◽  
Author(s):  
Louis J. Gross ◽  
Rachel Patton McCord ◽  
Sondra LoRe ◽  
Vitaly V. Ganusov ◽  
Tian Hong ◽  
...  
Keyword(s):  
Quantitative Methods ◽  
Data Science ◽  
Science Curriculum ◽  
Biomedical Science ◽  
Discrete Mathematics ◽  
Modern Biology ◽  
Graduate Curriculum ◽  
Novel Approach ◽  
Quantitative Skills ◽  
Faculty Input

AbstractSubstantial guidance is available on undergraduate quantitative training for biologists, including reports focused on biomedical science, but far less attention has been paid to the graduate curriculum. In this setting, we propose an innovative approach to quantitative education that goes beyond recommendations of a course or set of courses or activities. Due to the diversity of quantitative methods, it is infeasible to expect that biomedical PhD students can be exposed to more than a minority of the quantitative concepts and techniques employed in modern biology. We developed a novel prioritization approach in which we mined and analyzed quantitative concepts and skills from publications that faculty in relevant units deemed central to the scientific comprehension of their field. The analysis provides a prioritization of quantitative skills and concepts and could represent an effective method to drive curricular focus based upon program-specific faculty input for biological science programs of all types. Our results highlight the disconnect between typical undergraduate quantitative education for life science students, focused on continuous mathematics, and the concepts and skills in graphics, statistics, and discrete mathematics that arise from priorities established by biomedical science faculty.One Sentence SummaryWe developed a novel approach to prioritize quantitative concepts and methods for inclusion in a graduate biomedical science curriculum based upon approaches included in faculty-identified key publications.

scholarly journals An integrated, modular approach to data science education in microbiology

2021 ◽  
Vol 17 (2) ◽  
pp. e1008661 ◽  
Author(s):  
Kimberly A. Dill-McFarland ◽  
Stephan G. König ◽  
Florent Mazel ◽  
David C. Oliver ◽  
Lisa M. McEwen ◽  
...  
Keyword(s):  
Data Science ◽  
High Throughput Sequencing ◽  
Information Science ◽  
Science Curriculum ◽  
Life Sciences ◽  
Biological Research ◽  
Data Generation ◽  
Public And Private ◽  
Cocurricular Activities ◽  
Public And Private Sector

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

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