The science of statistics versus data science: What is the future?

2021 ◽  
Vol 173 ◽  
pp. 121111
Author(s):  
Hossein Hassani ◽  
Christina Beneki ◽  
Emmanuel Sirimal Silva ◽  
Nicolas Vandeput ◽  
Dag Øivind Madsen
Keyword(s):  
Data Science ◽  
Versus Data ◽  
The Future

The sharing economy: a hedge against recession setbacks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Reza Habibi ◽  
Chiranjeev S. Kohli
Keyword(s):  
Best Practices ◽  
Design Methodology ◽  
Data Science ◽  
Science And Technology ◽  
Sharing Economy ◽  
Economic Sector ◽  
Content Type ◽  
Key Characteristics ◽  
The Future ◽  
Conceptual Paper

Purpose This paper aims to provide lessons from the emergence of the sharing economy after the 2008 recession and helps managers prepare more effectively for recessions in the future. Design/methodology/approach In this conceptual paper, the authors build on research on the sharing economy and study the best practices contributing to the sharing economy’s emergence and growth after the 2008 recession. The authors identify the key characteristics of this new economic sector and share lessons that can be used by other companies. Findings The authors recommend five major takeaways: seeking a more flexible supply; actively watching the trends; leveraging customers like employees; using advanced data science and technology like the sharing economy companies; and proactively avoiding panicked responses. This will help companies succeed during recessionary times – and the boom times that follow. Originality/value This is the first paper that, to the best of the authors’ knowledge, investigates the interplay between the sharing economy and recession and highlights practical lessons.

Big Data, Data Science, and Career Pathways

2020 ◽  
pp. 239-254
Author(s):  
David W. Dorsey
Keyword(s):  
Big Data ◽  
Data Science ◽  
Career Pathways ◽  
Unstructured Data ◽  
Future Application ◽  
The Internet ◽  
The Future ◽  
Skill Requirements ◽  
Enormous Number ◽  
Over Time

With the rise of the internet and the related explosion in the amount of data that are available, the field of data science has expanded rapidly, and analytic techniques designed for use in “big data” contexts have become popular. These include techniques for analyzing both structured and unstructured data. This chapter explores the application of these techniques to the development and evaluation of career pathways. For example, data scientists can analyze online job listings and resumes to examine changes in skill requirements and careers over time and to examine job progressions across an enormous number of people. Similarly, analysts can evaluate whether information on career pathways accurately captures realistic job progressions. Within organizations, the increasing amount of data make it possible to pinpoint the specific skills, behaviors, and attributes that maximize performance in specific roles. The chapter concludes with ideas for the future application of big data to career pathways.

scholarly journals Addressing Misinformation in Online Social Networks: Diverse Platforms and the Potential of Multiagent Trust Modeling

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 539
Author(s):  
Robin Cohen ◽  
Karyn Moffatt ◽  
Amira Ghenai ◽  
Andy Yang ◽  
Margaret Corwin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Networking ◽  
Older Adult ◽  
Online Social Networks ◽  
Social Behaviour ◽  
Data Science ◽  
Science Studies ◽  
Trust Modeling ◽  
The Future

In this paper, we explore how various social networking platforms currently support the spread of misinformation. We then examine the potential of a few specific multiagent trust modeling algorithms from artificial intelligence, towards detecting that misinformation. Our investigation reveals that specific requirements of each environment may require distinct solutions for the processing. This then leads to a higher-level proposal for the actions to be taken in order to judge trustworthiness. Our final reflection concerns what information should be provided to users, once there are suspected misleading posts. Our aim is to enlighten both the organizations that host social networking and the users of those platforms, and to promote steps forward for more pro-social behaviour in these environments. As a look to the future and the growing need to address this vital topic, we reflect as well on two related topics of possible interest: the case of older adult users and the potential to track misinformation through dedicated data science studies, of particular use for healthcare.

Lessons from the Great Recession

2017 ◽  
Vol 32 (1) ◽  
pp. 57-61 ◽  
Author(s):  
Brian K. Fitzgerald ◽  
Steve Barkanic ◽  
Isabel Cardenas-Navia ◽  
Janet Chen ◽  
Ursula Gross ◽  
...  
Keyword(s):  
Higher Education ◽  
Great Recession ◽  
Data Science ◽  
The Great Recession ◽  
The Us ◽  
The Future

This essay reviews the work of the US Business–Higher Education Forum (BHEF) in data science and analytics and offers a brief review of how BHEF catalysed responses from its academic members to meet the talent needs of its business members, highlighting implications for business and higher education in the future.

Digital Epidemiology of Innovation (Preprint)

2021 ◽  
Author(s):  
Ivan Triana ◽  
LUIS PINO ◽  
Dennise Rubio
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Management ◽  
Medical Practice ◽  
Data Science ◽  
Medical Literature ◽  
The Future ◽  
Digital Epidemiology ◽  
Definition Of ◽  
And Control

UNSTRUCTURED Bio and infotech revolution including data management are global tendencies that have a relevant impact on healthcare. Concepts such as Big Data, Data Science and Machine Learning are now topics of interest within medical literature. All of them are encompassed in what recently is named as digital epidemiology. The purpose of this article is to propose our definition of digital epidemiology with the inclusion of a further aspect: Innovation. It means Digital Epidemiology of Innovation (DEI) and show the importance of this new branch of epidemiology for the management and control of diseases. In this sense, we will describe all characteristics concerning to the topic, current uses within medical practice, application for the future and applicability of DEI as conclusion.

The Education of the Human Factors Engineer in the Age of Data Science

2020 ◽  
Vol 64 (1) ◽  
pp. 480-484
Author(s):  
Daniel Hannon ◽  
Esa Rantanen ◽  
Ben Sawyer ◽  
Ashley Hughes ◽  
Katherine Darveau ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Factors ◽  
Data Science ◽  
Panel Discussion ◽  
Quantitative Skills ◽  
The Future ◽  
Relevant Role ◽  
Machine Learning Applications

The continued advances in artificial intelligence and automation through machine learning applications, under the heading of data science, gives reason for pause within the educator community as we consider how to position future human factors engineers to contribute meaningfully in these projects. Do the lessons we learned and now teach regarding automation based on previous generations of technology still apply? What level of DS and ML expertise is needed for a human factors engineer to have a relevant role in the design of future automation? How do we integrate these topics into a field that often has not emphasized quantitative skills? This panel discussion brings together human factors engineers and educators at different stages of their careers to consider how curricula are being adapted to include data science and machine learning, and what the future of human factors education may look like in the coming years.

scholarly journals Use Python Data Analysis to Gain Insights from Airbnb Hosts

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zichun Tian
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Data Science ◽  
Compelling Evidence ◽  
External Data ◽  
Depth Analysis ◽  
The Future ◽  
The Many

This paper uses Python and its external data processing package to conduct an in-depth analysis machine study of Airbnb review data. Increasingly, travelers are now using Airbnb instead of staying in traditional hotels. However, in such a growing and competitive Airbnb market, many hosts may find it difficult to make their listings attractive among the many. With the development of data science, the author can now analyse large amounts of data to obtain compelling evidence that helps Airbnb hosts find certain patterns in some popular properties. By learning and emulating these patterns, many hosts may be able to increase the popularity of their properties. By using Python to analyse all data from all aspects of Airbnb listings, the author proposes to test and find correlations between certain variables and popular listings. To ensure that the results are representative and general, the author used a database containing many multidimensional details and information about Airbnb listings to date. To obtain the desired results, the author uses the Pandas, NLTK, and matplotlib packages to better process and visualize the data. Finally, the author will make some recommendations to Airbnb hosts based on the evidence generated from the data in many ways. In the future, the author will build on this to further optimize the design.

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