scholarly journals Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum

2021 ◽  
Author(s):  
Marlena Duda ◽  
Kelly L Sovacool ◽  
Negar Farzaneh ◽  
Vy Kim Nguyen ◽  
Sarah E Haynes ◽  
...  
Keyword(s):  
Data Science ◽  
Independent Practice ◽  
Real World Data ◽  
Instructional Format ◽  
Local Chapter ◽  
Education Community ◽  
Who Code ◽  
Interactive Curriculum ◽  
The University ◽  
Practice Time

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational material on GitHub (https://github.com/GWC-DCMB).

scholarly journals Piloting a Community of Student Data Consultants that Supports and Enhances Research Data Services

2020 ◽  
pp. 1-11
Keyword(s):  
Best Practices ◽  
Real World ◽  
Data Science ◽  
Research Process ◽  
Research Data ◽  
Real World Data ◽  
Student Centered ◽  
Skill Sets ◽  
Student Data ◽  
The University

Research ecosystems within university environments are continuously evolving and requiring more resources and domain specialists to assist with the data lifecycle. Typically, academic researchers and professionals are overcommitted, making it challenging to be up-to-date on recent developments in best practices of data management, curation, transformation, analysis, and visualization. Recently, research groups, university core centers, and Libraries are revitalizing these services to fill in the gaps to aid researchers in finding new tools and approaches to make their work more impactful, sustainable, and replicable. In this paper, we report on a student consultation program built within the University Libraries, that takes an innovative, student-centered approach to meeting the research data needs in a university environment while also providing students with experiential learning opportunities. This student program, DataBridge, trains students to work in multi-disciplinary teams and as student consultants to assist faculty, staff, and students with their real-world, data-intensive research challenges. Centering DataBridge in the Libraries allows students the unique opportunity to work across all disciplines, on problems and in domains that some students may not interact with during their college careers. To encourage students from multiple disciplines to participate, we developed a scaffolded curriculum that allows students from any discipline and skill level to quickly develop the essential data science skill sets and begin contributing their own unique perspectives and specializations to the research consultations. These students, mentored by Informatics faculty in the Libraries, provide research support that can ultimately impact the entire research process. Through our pilot phase, we have found that DataBridge enhances the utilization and openness of data created through research, extends the reach and impact of the work beyond the researcher’s specialized community, and creates a network of student “data champions” across the University who see the value in working with the Library. Here, we describe the evolution of the DataBridge program and outline its unique role in both training the data stewards of the future with regard to FAIR data practices, and in contributing significant value to research projects at Virginia Tech. Ultimately, this work highlights the need for innovative, strategic programs that encourage and enable real-world experience of data curation, data analysis, and data publication for current researchers, all while training the next generation of researchers in these best practices.

What is the Business with AI? Preparing Future Decision Makers and Leaders

2020 ◽  
Author(s):  
Helena S. Wisniewski
Keyword(s):  
Data Science ◽  
Decision Makers ◽  
Management Skills ◽  
Business Courses ◽  
College Of Business ◽  
College Of Engineering ◽  
Investment Firm ◽  
Business Curricula ◽  
The University ◽  
The Impact

With companies now recognizing how artificial intelligence (AI), digitalization, the internet of things (IoT), and data science affect value creation and the maintenance of a competitive advantage, their demand for talented individuals with both management skills and a strong understanding of technology will grow dramatically. There is a need to prepare and train our current and future decision makers and leaders to have an understanding of AI and data science, the significant impact these technologies are having on business, how to develop AI strategies, and the impact all of this will have on their employees’ roles. This paper discusses how business schools can fulfill this need by incorporating AI into their business curricula, not only as stand-alone courses but also integrated into traditional business sequences, and establishing interdisciplinary efforts and collaborative industry partnerships. This article describes how the College of Business and Public Policy (CBPP) at the University of Alaska Anchorage is implementing multiple approaches to meet these needs and prepare future leaders and decision makers. These approaches include a detailed description of CBPP’s first AI course and related student successes, the integration of AI into additional business courses such as entrepreneurship and GSCM, and the creation of an AI and Data Science Lab in partnership with the College of Engineering and an investment firm.

Set up and management of a Data Science faculty at Shiga University

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 18-20
Author(s):  
Akimichi Takemura
Keyword(s):  
Data Science ◽  
Science Faculty ◽  
Knowledge And Skills ◽  
International Trends ◽  
Leading Position ◽  
Global Understanding ◽  
Set Up ◽  
The University ◽  
Do So ◽  
Undergraduate Class

Shiga University opened the first data science faculty in Japan in April 2017. Beginning with an undergraduate class of 100 students, the Department has since established a Master's degree programme with 20 students in each annual intake. This is the first data science faculty in Japan and the University intends to retain this leading position, the Department is well-placed to do so. The faculty closely monitors international trends concerning data science and Artificial Intelligence (AI) and adapt its education and research accordingly. The genesis of this department marks a change in Japan's attitudes towards dealing with information and reflects a wider, global understanding of the need for further research in this area. Shiga University's Data Science department seeks to produce well-trained data scientists who demonstrate a good balance of knowledge and skills in each of the three key areas of data science.

scholarly journals The Real-Time REF Review: A Pilot Study to Examine the Feasibility of a Longitudinal Evaluation of Perceptions and Attitudes Towards REF 2021

2019 ◽  
Author(s):  
Netta Weinstein ◽  
James Wilsdon ◽  
Jennifer Chubb ◽  
Geoff Haddock
Keyword(s):  
Pilot Study ◽  
Real Time ◽  
Research Assessment ◽  
Research Assessment Exercise ◽  
Perceptions And Attitudes ◽  
Education Community ◽  
Stern Review ◽  
The Uk ◽  
The University ◽  
Uk Higher Education

The UK first introduced a national research assessment exercise in 1986, and methods of assessment continue to evolve. Following the 2016 Stern Review and further rounds of technical consultation, the UK higher education community is now preparing for the next Research Excellence Framework – REF 2021.Despite its importance in shaping UK research cultures, there is limited systematic and nuanced evidence about how academics across the sector view the REF, and which aspects are viewed favourably or unfavourably. The aims of this pilot study were twofold: first, it was designed to gather initial data to address this evidence gap; second, it was aimed at testing the feasibility of conducting a longitudinal study into academic and managerial attitudes towards the REF. We argue that further research to better understand the effects of the REF on research cultures, institutions, and individuals should be part of the evidence used to inform the development of future iterations of the exerciseThe Real Time REF Review Pilot Study was developed and delivered by a research team from Cardiff University and the University of Sheffield, in collaboration with Research England.

Haptic-Based Virtual Reality Dental Simulator as an Educational Tool

2011 ◽  
pp. 724-735
Author(s):  
Maxim Kolesnikov ◽  
Arnold D. Steinberg ◽  
Miloš Žefran
Keyword(s):  
Virtual Reality ◽  
Clinical Skills ◽  
Educational Tool ◽  
University Of Illinois ◽  
Training Techniques ◽  
Instructor Interaction ◽  
Haptic Simulation ◽  
The University ◽  
Practice Time ◽  
Student Instructor

This chapter describes the haptic dental simulator developed at the University of Illinois at Chicago. It explores its use and advantages as an educational tool in dentistry and examines the structure of the simulator, its hardware and software components, the simulator’s functionality, reality assessment, and the users’ experiences with this technology. The authors hope that the dental haptic simulation program should provide significant benefits over traditional dental training techniques. It should facilitate students’ development of necessary tactile skills, provide unlimited practice time and require less student/instructor interaction while helping students learn basic clinical skills more quickly and effectively.

Haptic-Based Virtual Reality Dental Simulator as an Educational Tool

2010 ◽  
pp. 219-231
Author(s):  
Maxim Kolesnikov ◽  
Arnold D. Steinberg ◽  
Milos Zefran
Keyword(s):  
Virtual Reality ◽  
Clinical Skills ◽  
Educational Tool ◽  
University Of Illinois ◽  
Training Techniques ◽  
Instructor Interaction ◽  
Haptic Simulation ◽  
The University ◽  
Practice Time ◽  
Student Instructor

This chapter describes the haptic dental simulator developed at the University of Illinois at Chicago. It explores its use and advantages as an educational tool in dentistry and examines the structure of the simulator, its hardware and software components, the simulator’s functionality, reality assessment, and the users’ experiences with this technology. The authors hope that the dental haptic simulation program should provide significant benefits over traditional dental training techniques. It should facilitate students’ development of necessary tactile skills, provide unlimited practice time and require less student/instructor interaction while helping students learn basic clinical skills more quickly and effectively.

Conceptual Assessment in the Biological Sciences: a National Science Foundation-sponsored workshop

2007 ◽  
Vol 31 (4) ◽  
pp. 389-391 ◽  
Author(s):  
Joel Michael
Keyword(s):  
Biological Sciences ◽  
Conceptual Understanding ◽  
National Science Foundation ◽  
Meaningful Learning ◽  
Biology Teachers ◽  
University Of Colorado ◽  
Education Community ◽  
National Science ◽  
Biological Concepts ◽  
The University

Twenty-one biology teachers from a variety of disciplines (genetics, ecology, physiology, biochemistry, etc.) met at the University of Colorado to begin discussions about approaches to assessing students' conceptual understanding of biology. We considered what is meant by a “concept” in biology, what the important biological concepts might be, and how to go about developing assessment items about these concepts. We also began the task of creating a community of biologists interested in facilitating meaningful learning in biology. Input from the physiology education community is essential in the process of developing conceptual assessments for physiology.

Multiclass Contour-Preserving Classification with Support Vector Machine (SVM)

2017 ◽  
Vol 26 (2) ◽  
pp. 323-334 ◽  
Author(s):  
Piyabute Fuangkhon
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
University Of California ◽  
Support Vector ◽  
Data Sets ◽  
Feed Forward Neural Network ◽  
Real World Data ◽  
Data Set ◽  
The University ◽  
Training Sets

AbstractMulticlass contour-preserving classification (MCOV) has been used to preserve the contour of the data set and improve the classification accuracy of a feed-forward neural network. It synthesizes two types of new instances, called fundamental multiclass outpost vector (FMCOV) and additional multiclass outpost vector (AMCOV), in the middle of the decision boundary between consecutive classes of data. This paper presents a comparison on the generalization of an inclusion of FMCOVs, AMCOVs, and both MCOVs on the final training sets with support vector machine (SVM). The experiments were carried out using MATLAB R2015a and LIBSVM v3.20 on seven types of the final training sets generated from each of the synthetic and real-world data sets from the University of California Irvine machine learning repository and the ELENA project. The experimental results confirm that an inclusion of FMCOVs on the final training sets having raw data can improve the SVM classification accuracy significantly.

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