Fundamentals of Data Science for Future Data Scientists

2018 ◽  
pp. 167-194
Author(s):  
Jiangping Chen ◽  
Brenda Reyes Ayala ◽  
Duha Alsmadi ◽  
Guonan Wang
Keyword(s):  
Data Science ◽  
Future Data

scholarly journals Collaboration between Government and Research Community to Respond to COVID-19: Israel’s Case

2021 ◽  
Vol 7 (4) ◽  
pp. 208
Author(s):  
Mor Peleg ◽  
Amnon Reichman ◽  
Sivan Shachar ◽  
Tamir Gadot ◽  
Meytal Avgil Tsadok ◽  
...  
Keyword(s):  
Health Policy ◽  
Data Science ◽  
Research Community ◽  
Remote Access ◽  
Data Driven ◽  
Multidisciplinary Teams ◽  
Science Methods ◽  
Research Environments ◽  
Future Data ◽  
The Government

Triggered by the COVID-19 crisis, Israel’s Ministry of Health (MoH) held a virtual datathon based on deidentified governmental data. Organized by a multidisciplinary committee, Israel’s research community was invited to offer insights to help solve COVID-19 policy challenges. The Datathon was designed to develop operationalizable data-driven models to address COVID-19 health policy challenges. Specific relevant challenges were defined and diverse, reliable, up-to-date, deidentified governmental datasets were extracted and tested. Secure remote-access research environments were established. Registration was open to all citizens. Around a third of the applicants were accepted, and they were teamed to balance areas of expertise and represent all sectors of the community. Anonymous surveys for participants and mentors were distributed to assess usefulness and points for improvement and retention for future datathons. The Datathon included 18 multidisciplinary teams, mentored by 20 data scientists, 6 epidemiologists, 5 presentation mentors, and 12 judges. The insights developed by the three winning teams are currently considered by the MoH as potential data science methods relevant for national policies. Based on participants’ feedback, the process for future data-driven regulatory responses for health crises was improved. Participants expressed increased trust in the MoH and readiness to work with the government on these or future projects.

A cross-institutional analysis of data-related curricula in information science programmes: A focused look at the iSchools

2018 ◽  
Vol 44 (6) ◽  
pp. 768-784 ◽  
Author(s):  
Virginia Ortiz-Repiso ◽  
Jane Greenberg ◽  
Javier Calzada-Prado
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Information Science ◽  
Institutional Analysis ◽  
Big Data Analytics ◽  
Master's Level ◽  
Digital Curation ◽  
Future Data ◽  
Nature Of Information ◽  
Information Professionals

Our rapidly growing, data-driven culture is motivating curriculum change in nearly every discipline, not the least of which is information science. This article explores this change specifically within the iSchool community, in which information science is a major unifying discipline. A cross-institutional analysis of data-related curricula was conducted across 65 iSchools. Results show that a majority of iSchools examined (37 out of 65, 56.9%) currently offer some form of data-related education, particularly at the master’s level, and that approximately 15% of their formal degree offerings have a data focus. Overall, iSchools have a greater emphasis on data science and big data analytics, with only a few programmes providing focused curricula in the area of digital curation. Recommendations are made for iSchools to leverage the interdisciplinary nature of information science, publish curricula and track graduate success so that iSchools may excel in educating information professionals in the data area. Future data education in iSchools may benefit from further interdisciplinary data education, including data curation curricula.

scholarly journals A Review of Data Science and Big Data Computing

2020 ◽  
pp. 1-11
Author(s):  
Wajid Ali ◽  
Muhammad Usman Shafique ◽  
Muhammad Arslan Majeed ◽  
Muhammad Faizan ◽  
Ahmad Raza
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Science ◽  
General Concept ◽  
Future Data ◽  
Big Data Computing ◽  
Abundant Data

Data Science emerged as an important discipline and its education is essential for success in almost every aspect of life.  Here comes the age of Big data. Big data impacts all aspects of our lives and society is admitting it. Data processing and other techniques are combined to convert abundant data into valuable information for society, organizations, and people. Specific strategies and approaches are needed to provide better to educate future data scientists to overcome the challenges of Big data. In this paper, we discussed the general concept of data science, Big data, and areas of Big data computing.

Integration of Multimodal Data for Deciphering Brain Disorders

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jingqi Chen ◽  
Guiying Dong ◽  
Liting Song ◽  
Xingzhong Zhao ◽  
Jixin Cao ◽  
...  
Keyword(s):  
Human Brain ◽  
Data Science ◽  
Annual Review ◽  
Publication Date ◽  
Biomedical Data ◽  
Brain Disorders ◽  
Multimodal Data ◽  
Future Data ◽  
The Brain ◽  
Shed Light

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Data Science Lab at LMU Munich: Leveraging Knowledge Transfer, Implementing Collaborative Projects, and Promoting Future Data Science Talents

2017 ◽  
pp. 549-556 ◽  
Author(s):  
Thomas Seidl ◽  
Peer Kröger ◽  
Tobias Emrich ◽  
Matthias Schubert ◽  
Gregor Jossé ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Data Science ◽  
Future Data

Practical Realization of a Reference System for Earth Dynamics by Satellite Methods

1975 ◽  
Vol 26 ◽  
pp. 341-380 ◽  
Author(s):  
R. J. Anderle ◽  
M. C. Tanenbaum
Keyword(s):  
Reference Frame ◽  
Polar Motion ◽  
Pole Position ◽  
Control Points ◽  
Significant Information ◽  
Crustal Motion ◽  
Average Reference ◽  
Future Data ◽  
Existing Data

AbstractObservations of artificial earth satellites provide a means of establishing an.origin, orientation, scale and control points for a coordinate system. Neither existing data nor future data are likely to provide significant information on the .001 angle between the axis of angular momentum and axis of rotation. Existing data have provided data to about .01 accuracy on the pole position and to possibly a meter on the origin of the system and for control points. The longitude origin is essentially arbitrary. While these accuracies permit acquisition of useful data on tides and polar motion through dynamio analyses, they are inadequate for determination of crustal motion or significant improvement in polar motion. The limitations arise from gravity, drag and radiation forces on the satellites as well as from instrument errors. Improvements in laser equipment and the launch of the dense LAGEOS satellite in an orbit high enough to suppress significant gravity and drag errors will permit determination of crustal motion and more accurate, higher frequency, polar motion. However, the reference frame for the results is likely to be an average reference frame defined by the observing stations, resulting in significant corrections to be determined for effects of changes in station configuration and data losses.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

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