scholarly journals Bringing the World to the Classroom: Teaching Statistics and Programming in a Project-Based Setting

2021 ◽  
pp. 1-5
Author(s):  
Cosima Meyer
Keyword(s):  
Data Management ◽  
Data Science ◽  
Classroom Teaching ◽  
Statistical Software ◽  
Inverted Classroom ◽  
Typical Data ◽  
The World ◽  
Virtual Classes ◽  
Teaching Statistics ◽  
Introductory Class

ABSTRACT This article introduces how to teach an interactive, one-semester-long statistics and programming class. The setting also can be applied to shorter and longer classes as well as introductory and advanced courses. I propose a project-based seminar that also encompasses elements of an inverted classroom. As a result of this combination, the seminar supports students’ learning progress and also creates engaging virtual classes. To demonstrate how to apply a project-based seminar setting to teaching statistics and programming classes, I use an introductory class to data wrangling and management with the statistical software program R. Students are guided through a typical data science workflow that requires data management and data wrangling and concludes with visualizing and presenting first research results during a simulated mini-conference.

Supporting open data: the key role of data managers

2021 ◽  
Author(s):  
Alice Fremand
Keyword(s):  
Data Management ◽  
Data Science ◽  
New Technologies ◽  
Open Data ◽  
Research Data ◽  
Complex Data ◽  
Data Set ◽  
Data Publication ◽  
World Data ◽  
The World

<p>Open data is not a new concept. Over sixty years ago in 1959, knowledge sharing was at the heart of the Antarctic Treaty which included in article III 1c the statement: “scientific observations and results from Antarctica shall be exchanged and made freely available”. ​At a similar time, the World Data Centre (WDC) system was created to manage and distribute the data collected from the International Geophysical Year (1957-1958) led by the International Council of Science (ICSU) building the foundations of today’s research data management practices.</p><p>What about now? The WDC system still exists through the World Data System (WDS). Open data has been endorsed by a majority of funders and stakeholders. Technology has dramatically evolved. And the profession of data manager/curator has emerged. Utilising their professional expertise means that their role is far wider than the long-term curation and publication of data sets.</p><p>Data managers are involved in all stages of the data life cycle: from data management planning, data accessioning to data publication and re-use. They implement open data policies; help write data management plans and provide advice on how to manage data during, and beyond the life of, a science project. In liaison with software developers as well as scientists, they are developing new strategies to publish data either via data catalogues, via more sophisticated map-based viewer services or in machine-readable form via APIs. Often, they bring the expertise of the field they are working in to better assist scientists satisfy Findable, Accessible, Interoperable and Re-usable (FAIR) principles. Recent years have seen the development of a large community of experts that are essential to share, discuss and set new standards and procedures. The data are published to be re-used, and data managers are key to promoting high-quality datasets and participation in large data compilations.</p><p>To date, there is no magical formula for FAIR data. The Research Data Alliance is a great platform allowing data managers and researchers to work together, develop and adopt infrastructure that promotes data-sharing and data-driven research. However, the challenge to properly describe each data set remains. Today, scientists are expecting more and more from their data publication or data requests: they want interactive maps, they want more complex data systems, they want to query data, combine data from different sources and publish them rapidly.  By developing new procedures and standards, and looking at new technologies, data managers help set the foundations to data science.</p>

scholarly journals LearnTeamPlenum – A Pragmatic Approach for Inverted Teaching

2017 ◽  
Author(s):  
Juliane Siegeris
Keyword(s):  
Teaching Experience ◽  
Classroom Teaching ◽  
Pragmatic Approach ◽  
Study Program ◽  
Inverted Classroom ◽  
Organizational Settings ◽  
The World ◽  
Competency Based ◽  
The Subject ◽  
The Common

The paper proposes a new teaching approach, that has been adapted from the LearnTeamCoaching - a method from the inverted classroom catalog. In comparison with other inverted teaching scenarios, it requires less preparation effort, i.e. no videos and scripts. Instead the students are asked to investigate the subject autonomously using provided papers and the World Wide Web. Another adaption concerns the format of the common lecture. Here the reflection of the learning matter is based on posters, that have been prepared as a result of the investigation. The paper introduces the method and provides details regarding the experience gained during its application at the HTW Berlin in the study program computer science and business administration. In the end, the application of the method for different subjects and different organizational settings is discussed. Keywords: inverted classroom; teaching experience, competency-based learning, active learning

scholarly journals Ten Simple Rules for Writing Dockerfiles for Reproducible Data Science

2020 ◽  
Author(s):  
Daniel Nüst ◽  
Vanessa Sochat ◽  
Ben Marwick ◽  
Stephen Eglen ◽  
Tim Head ◽  
...  
Keyword(s):  
Data Science ◽  
Scholarly Communication ◽  
Computational Science ◽  
Data Dependencies ◽  
Typical Data ◽  
Simple Rules ◽  
Scientific Papers

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow’s reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container’s image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.

scholarly journals CONTEMPORARY DATA SCIENCE FOR FINANCE STUDENTS. ESSENTIAL FEATURES OF COMMONLY USED STATISTICAL SOFTWARE - A COMPARATIVE STUDY

2019 ◽  
Vol 9 (2) ◽  
pp. 14-20
Author(s):  
Mădălina Viorica ION (MANU) ◽  
◽  
Ilie VASILE ◽  
Keyword(s):  
Comparative Study ◽  
Programming Languages ◽  
Data Science ◽  
Relevant Literature ◽  
Global Scale ◽  
Statistical Software ◽  
Business Excellence ◽  
Romanian Literature ◽  
Basic Features ◽  
Python Programming

This paper inventories some of the essential traits of the software preferred by researchers, students and professors, such as R or RStudio, or Matlab and also their possible utilizations. In order to fill the gap in the Romanian literature and help finance students in choosing proper tools according to the research purpose, this comparative study aims at bringing a fresh, useful perspective in the relevant literature. In Romania, the use of R was the focus of several international conferences on official statistics held in Bucharest, and others having business excellence, innovation and sustainability as purpose. In this time, at global scale, R and Python programming languages are considered the lingua franca of data science, as common statistical software used both in corporations and academia. In this paper, I analyze basic features of such software, with the purpose of application in finance.

scholarly journals Evergreen Library Penetrating into Classroom Teaching: Applying and Expanding – Take Tianzhu No.1 High School in Northwest China as a Sample

2021 ◽  
Author(s):  
Zhou Wenjie
Keyword(s):  
High School ◽  
Information Quality ◽  
School Library ◽  
Northwest China ◽  
School Libraries ◽  
Vital Role ◽  
Literacy Skills ◽  
Classroom Teaching ◽  
The World

This paper provides a study of the school library programs sponsored by the Evergreen Education Foundation (EEF) and identifies the vital role of school libraries in improving students’ information quality. Based on analyzes Strategies for building literacy skills in the library of Tianzhu No.1 High School, the study confirmed the program developed reading and literacy skills among students. As EEF programs continue to expand into other locations in China, it is the authors’ hope that this study may provide useful information and analysis based upon which decisions about future programs can be made. It is also their hope that this study provides impetus for more studies on the rural library programs in other areas of China or programs in other underdeveloped regions of the world.

scholarly journals NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

2020 ◽  
Vol 6 ◽  
Author(s):  
Christoph Steinbeck ◽  
Oliver Koepler ◽  
Felix Bach ◽  
Sonja Herres-Pawlis ◽  
Nicole Jung ◽  
...  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Open Data ◽  
Research Data ◽  
Data Standards ◽  
Data Repositories ◽  
Data Infrastructure ◽  
Research Data Management ◽  
Wide Range ◽  
Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

Opportunities and Implementation of Big Data Management in Academic Libraries

2021 ◽  
pp. 35-47
Author(s):  
Mahesh G. T. ◽  
Nandeesha B.
Keyword(s):  
Big Data ◽  
Data Management ◽  
Academic Libraries ◽  
Strong Association ◽  
Karnataka State ◽  
The World ◽  
The Government ◽  
Associated Data

Data has changed the world in an unbelievable way and made an impact on our lifestyles at an exceptional rate. Big data is now the latest science of exploring and forecasting human-machine behavior dealing with a massive amount of associated data. The study is intended to understand the intensity and the competencies of librarians in implementing big data initiative project in academic libraries by the Government of Karnataka State. The study also tries to understand the application of big data in these libraries; 68 (87.17%) librarians completed the survey out of 78 respondents. The results of the study showed a strong association, that is, 72 (92.30%) respondents had the essential competencies and 58 (75.64%) librarians ability, intensity, readiness in implementing big data in academic libraries.

Hilbert Index-based Outlier Detection Algorithm in Metric Space

2016 ◽  
Vol 8 (4) ◽  
pp. 34-54
Author(s):  
Honglong Xu ◽  
Haiwu Rong ◽  
Rui Mao ◽  
Guoliang Chen ◽  
Zhiguang Shan
Keyword(s):  
Big Data ◽  
Data Management ◽  
Outlier Detection ◽  
Metric Space ◽  
Detection Algorithm ◽  
Analysis Framework ◽  
Distance Calculation ◽  
The World ◽  
Speed Up ◽  
Detection Speed

Big data is profoundly changing the lifestyles of people around the world in an unprecedented way. Driven by the requirements of applications across many industries, research on big data has been growing. Methods to manage and analyze big data to extract valuable information are the key of big data research. Starting from the variety challenge of big data, this dissertation proposes a universal big data management and analysis framework based on metric space. In this framework, the Hilbert Index-based Outlier Detection (HIOD) algorithm is proposed. HIOD can handle all datatypes that can be abstracted to metric space and achieve higher detection speed. Experimental results indicate that HIOD can effectively overcome the variety challenge of big data and achieves a 2.02 speed up over iORCA on average and, in certain cases, up to 5.57. The distance calculation times are reduced by 47.57% on average and up to 89.10%.

How Should Data Science Education Be?

2020 ◽  
Vol 9 (2) ◽  
pp. 25-36
Author(s):  
Necmi Gürsakal ◽  
Ecem Ozkan ◽  
Fırat Melih Yılmaz ◽  
Deniz Oktay
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Science Education ◽  
Data Science ◽  
Doctoral Programs ◽  
Time Data ◽  
High Demand ◽  
The Core ◽  
The World ◽  
The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

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