Data Science Rules the World

2019 ◽  
pp. 143-144
Author(s):  
Stylianos Kampakis
Keyword(s):  
Data Science ◽  
The World

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.

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.

Data Analysis on Global Stratification

2020 ◽  
pp. 355-378
Author(s):  
Kirti Raj Bhatele ◽  
Stuti Singhal ◽  
Muktasha R. Mithora ◽  
Sneha Sharma
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Graphical Representation ◽  
Heat Maps ◽  
Python Language ◽  
Graphical Data ◽  
Sentence Form ◽  
The World ◽  
Table Data ◽  
Numeric Data

This chapter will guide you through the modeling, uses, and trends in data analysis and data science. The authors focus on the importance of pictorial data in replacement of numeric data. In most situations, graphical representation of data can present the information more distinctly, informative, and in less space than the same information requires in sentence form. This chapter provides a brief knowledge about representing data to more understandable form such that any person whether layman or not can understand it without any difficulty. This chapter also deals with the software Tableau which we use to convert the table data into graphical data. This Chapter contains 11 heat maps related to the world economies and their detailed study on several different topics. It will also give light on the basics of Python Language and its various algorithm studies to compare all the world economies based on their development.

scholarly journals Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes

2020 ◽  
pp. 108-116
Author(s):  
Jill S. Barnholtz-Sloan ◽  
Dana E. Rollison ◽  
Amrita Basu ◽  
Alexander D. Borowsky ◽  
Alex Bui ◽  
...  
Keyword(s):  
Best Practices ◽  
Patient Outcomes ◽  
National Cancer Institute ◽  
Data Science ◽  
Cancer Centers ◽  
Face To Face ◽  
The World ◽  
Population Science ◽  
History Of ◽  
National Forum

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute–funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders’ titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was “intersections between informatics, data science, and population science.” We conclude with a discussion on “hot topics” on the horizon for cancer informatics.

scholarly journals DYNAMICS OF THE COVID-19 PANDEMICS: GLOBAL PATTERN AND BETWEEN COUNTRIES VARIATIONS

2020 ◽  
Author(s):  
Julien Krywyk ◽  
Walther Oettgen ◽  
Marc Messier ◽  
Matthieu Mulot ◽  
Laurent Toubiana
Keyword(s):  
Data Science ◽  
Respiratory Infections ◽  
Late Phase ◽  
Science Methods ◽  
Global Pattern ◽  
Expansion Phase ◽  
The World ◽  
Sporadic Cases ◽  
Viral Respiratory Infections ◽  
Viral Respiratory

The COVID-19 pandemic affected 203 countries between December 2019 and July 2020. The early epidemic "wave" affected countries which now report a few sporadic cases, achieving a stable late phase of the epidemic. Other countries are beginning their epidemic expansion phase. The objective of our study is to characterize the dynamics of the COVID-19 spread. Data science methods were applied to pandemic, focusing on the daily fatality in 24 countries with more than 2,000 deaths, our analysis kin the end retaining 14 countries that have completed a full cycle. The analysis demonstrates a COVID-19 dynamic similar in these studied countries. This 3-phase dynamic is like that of common viral respiratory infections. This pattern, however, shows variability and therefore specificity which the method categorizes into clusters of "differentiated epidemic patterns". Among the 5 detected clusters, 2 main ones regroup 11 of these countries, representing 65% of the world deaths (as of June 24, 2020). The pattern seems common to a very large number of countries, and congruent with that of epidemics of other respiratory syndromes, opens the hypothesis that the COVID-19 pandemic would have developed its "natural history" by spreading spontaneously despite the measures taken to contain it. The diversity highlighted by the classification into "formal clusters" suggests explanations involving the notion of demographic and geographic epicenters.

scholarly journals Leveraging Data Science To Combat COVID-19: A Comprehensive Review

2020 ◽  
Author(s):  
Siddique Latif ◽  
Muhammad Usman ◽  
Sanaullah Manzoor ◽  
Waleed Iqbal ◽  
Junaid Qadir ◽  
...  
Keyword(s):  
Data Science ◽  
World Health Organisation ◽  
Mitigation Strategies ◽  
World Health ◽  
Comprehensive Review ◽  
The World ◽  
Growing Body ◽  
Short Span ◽  
Public Datasets ◽  
Science Activities

<div>COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.</div><div>As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.</div>

scholarly journals Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 92
Author(s):  
Nirmalya Thakur ◽  
Chia Y. Han
Keyword(s):  
Public Health ◽  
Open Access ◽  
Data Science ◽  
Fall Detection ◽  
Time Range ◽  
User Interest ◽  
User Interests ◽  
The World ◽  
Research Questions ◽  
Google Search

Falls, which are increasing at an unprecedented rate in the global elderly population, are associated with a multitude of needs such as healthcare, medical, caregiver, and economic, and they are posing various forms of burden on different countries across the world, specifically in the low- and middle-income countries. For these respective countries to anticipate, respond, address, and remedy these diverse needs either by using their existing resources, or by developing new policies and initiatives, or by seeking support from other countries or international organizations dedicated to global public health, the timely identification of these needs and their associated trends is highly necessary. This paper addresses this challenge by presenting a study that uses the potential of the modern Internet of Everything lifestyle, where relevant Google Search data originating from different geographic regions can be interpreted to understand the underlining region-specific user interests towards a specific topic, which further demonstrates the public health need towards the same. The scientific contributions of this study are two-fold. First, it presents an open-access dataset that consists of the user interests towards fall detection for all the 193 countries of the world studied from 2004–2021. In the dataset, the user interest data is available for each month for all these countries in this time range. Second, based on the analysis of potential and emerging research directions in the interrelated fields of Big Data, Data Mining, Information Retrieval, Natural Language Processing, Data Science, and Pattern Recognition, in the context of fall detection research, this paper presents 22 research questions that may be studied, evaluated, and investigated by researchers using this dataset.

scholarly journals Data science and analytics in agricultural development

2021 ◽  
Vol 22 (SE) ◽  
pp. 9-19
Author(s):  
Karamveer Singh Sidhu ◽  
Ramandeep Singh ◽  
Snehdeep Singh ◽  
Gunjot Singh
Keyword(s):  
Data Analytics ◽  
Agricultural Development ◽  
Data Science ◽  
Data Driven ◽  
High Population ◽  
Food And Agriculture ◽  
Current Population ◽  
Food And Agriculture Organization ◽  
Agricultural Produce ◽  
The World

The consistent advancement of innovation has implied information and data being created at a rate, not at all like ever previously, and it's just on the ascent. The world makes an extra 2.5 quintillion bytes of information every year. The demand for individuals talented in investigating, deciphering, and utilising this information is now high and is set to become exponential over the coming years. The total populace is relied upon to arrive at 9.7 billion by 2050 from the current population of 7.8 billion. The Food and Agriculture Organization (FAO) has predicted that the development of farming must be expanded by 70% to provide for the extended interest. Data-driven agriculture choices can be a potent technology to manage the needs of this much high population, as this technology gives higher efficiency, rehearses support-ability, and even assists with giving straightforwardness to purchasers and consumers needing to find out about their food as reported in the studies. The current and future interests will require more data researchers, data engineers, data specialists, and chief data Officers.  This paper tries to examine the need, use, role, and issues faced by data science and data analytics to improve the quality as well as quantity of Agricultural produce thereby leading to an increase in production, a decrease in costs, and overall sustainability.

scholarly journals Big data processing using Open Source Software- A Questionnaire on the data science

2016 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Andrew McCullum
Keyword(s):  
Big Data ◽  
Data Processing ◽  
World Trade Organization ◽  
Central Asia ◽  
Open Source ◽  
Open Source Software ◽  
World Trade ◽  
Data Science ◽  
Customs Union ◽  
The World

In 2015, Central Asia made some vital enhancements in nature for cross-fringe e-business: Kazakhstan's promotion to the World Trade Organization (WTO) will help business straightforwardness, while the Kyrgyz Republic's enrollment in the Eurasian Customs Union grows its buyer base. Why e-business? Two reasons to begin with, e-trade diminishes the expense of separation. Focal Asia is the most elevated exchange cost locale on the planet: unlimited separations from real markets make discovering purchasers testing, shipping merchandise moderate, and fare costs high. Second, e-business can pull in populaces that are customarily under-spoke to in fare markets, for example, ladies, little organizations and rustic business visionaries.

scholarly journals A standards organization for Open and FAIR neuroscience: the International Neuroinformatics Coordinating Facility

2019 ◽  
Author(s):  
Mathew Abrams ◽  
Jan G. Bjaalie ◽  
Samir Das ◽  
Gary F. Egan ◽  
Satrajit S Ghosh ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Science ◽  
Small Data ◽  
Sustainable Infrastructure ◽  
The World ◽  
Formal Procedure ◽  
Fair Principles ◽  
Community Standards ◽  
Diverse Data ◽  
Independent Organization

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), established in 2005, is an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards. INCF has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

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