Designing and Delivering a Curriculum for Data Science Education across Europe

Data is currently being produced at an incredible rate globally, fuelled by the increasing ubiquity of the Web, and stoked by social media, sensors, and mobile devices. However, as the amount of available data continues to increase, so does the demand for professionals who have the necessary skills to manage and manipulate this data. This paper presents the European Data Science Academy (EDSA), an initiative for bridging the data science skills gap across Europe and training a new generation of world-leading data scientists. The EDSA project has established a rigorous process and a set of best practices for the production and delivery of curricula for data science. Additionally, the project’s efforts are dedicated to linking the demand for data science skills with the supply of learning resources that offer these skills

Establishing expert consensuses on the value of open data in open social innovation ideation

There is little conclusive evidence as to whether OD provides value to social innovation ideation scenarios. Furthermore, OD as a resource is severely contested as to its openness, availability, quality, importance, and usefulness within innovation ideation. Therefore, understanding how Open Data (OD) can be leveraged for innovation ideation practices has become a topic at the mainstream of management literature. However much of the effort thus far has been focused on ideation and innovation for-profit, specifically when in papers examining Open Innovation (OI), even though OD has been depicted as a resource for providing social, economical and entrepreneurial benefit. Therefore this paper presents an initial study of the perceived value of OD, in research phase Open Social Innovation (OSI), amongst academic and professional experts in OI, Innovation Networking and OD. Consequently, a Modified Delphi Study (MDS) is conducted, aimed at forming a convergence of opinion amongst academic and professional experts. From converging expert opinions from both academic and professional perspectives, optimal managerial practices within this field can be shaped. Furthermore, management processes and practices can be justified in collecting and targeting particular datasets that are opportune for a social innovation context. In addition to the primary objectives, and with respect to the paper’s findings, barriers of utilizing and leveraging OD for this purpose are duly noted with proposed methods of overcoming such challenge

The European Data Science Academy: Bridging the Data Science Skills Gap with Open Courseware

As a global society, we are producing data at an incredible rate, fuelled by the increasing ubiquity of the Web, and stoked by social media, sensors, and mobile devices. However, as the amount of produced data continues to increase, so does the demand for practitioners who have the necessary skills to manage and manipulate this data. The European Data Science Academy (EDSA) is looking to bridge the data science skills gap by developing multimodal open courseware tailored to the real needs of data practitioners. The EDSA courseware is implemented as a combination of living learning materials and activities (eBook, online courses, webinars, face-to-face training), produced via a rigorous process and validated by the data science community through continuous feedback

Humour reactions in crisis: a proximal analysis of Chinese posts on Sina Weibo in reaction to the salt panicof March 2011

This paper presents an analysis of humour use in Sina Weibo in reaction to the Chinese salt panic, which occurred as a result of the Fukushima disaster in March 2011. Basing the investigation on the humour Proximal Distancing Theory (PDT), and utilising a dataset from Sina Weibo in 2011, an examination of humour reactions is performed to identify the proximal spread of humourous Weibo posts in relation to the consequent salt panic in China. As a result of this method, we present a novel methodology for understanding humour reactions in social media, and provide recommendations on how such a method could be applied to a variety of other social media, crises, cultural and spatial settings

A case study analysis of the success factors in Web-based and offline social innovation competitions

Social innovation competitions are short-term, non-profit social innovation practises, utilised to drive collaborative effort for encouraging the production of innovations that have a dedicated social impact. Factors such as innovation quality, collaboration potential and social impact are perceived as being central to the success of social innovation competitions. From these three core factors, this thesis determines the strength of these factors amongst Web-based and offline social innovation competition. With reference to these success factors, the definition of social innovation competitions is founded, stating that without these primary factors, one may not be able to operate a successful social innovation competition. Detracting from the typical for-profit innovation and fundraising models, social innovation competitions are focused on obtaining solutions to the challenge rather than profit or finance. Facilitated by the Web, social innovation competitions can be conducted in an online or an offline setting, with innovation managers selecting either method depending upon their particular objectives.This selection is largely because each method of social innovation competition (online or offline) appears to have comparatively different success factors and outcomes as a result. Namely, social innovation competitions conducted in an online setting are potentially subject to higher scalability, through an increase in innovation responses and potential for more participants, but such innovations may indeed lack in the quality necessary to tackle the challenge in any great depth. On the other hand, offline social innovation competitions are understood to be subject to lower scalability, but can provide better methods of collaboration with a few high quality innovations, that are targeted and facilitate the use of multiple sets of skills from a variety of innovators. These factors of social innovation competitions determine that innovation managers and innovation professionals can appropriately leverage the optimum method of social innovation competition dependant upon the aims and objectives of the organisation or challenge.This exploratory study utilises a mixed methods approach to uncovering such success factors and their respective trade-offs. Initiating the line of enquiry with two cases (the PORT social innovation competition and the Microworkers social innovation competition) observations are made as to the format, structure and outputs of each competition, gaining insight from innovators and their innovative endeavours. Furthermore, surveys are conducted to gather perceptions on the success factors in both online and offline social innovation competitions, aiming to understand whether there are trade-offs that occur when either performing an online or an offline version of a social innovation competition. Finally a Delphi study is conducted in order to gather opinions from experts on these topics. This final study supports the triangulation of data for further insight into this new and uncharted field of study. It is concluded that offline social innovation competitions should generally be used for obtaining targeted, product-based innovations of a high quality, whereas Web-based social innovation competitions should be used as a market research method, obtaining surface-level insight into the trends and expectations of consumers and innovators in a particular market.<br/

Bay 13 pecha kucha

The talks are by EA Draffan, Nawar Halabi, Gareth Beeston and Neil Rogers. In 6m40s and 20 slides, each member of Bay 13 will introduce themselves, explaining their background and research interests, so those in WAIS can put a name to a face, and chat after the event if there are common interests

Designing and delivering a curriculum for data science education across Europe

Data is currently being produced at an incredible rate globally, fuelled by the increasing ubiquity of the Web, and stoked by social media, sensors, and mobile devices. However, as the amount of available data continues to increase, so does the demand for professionals who have the necessary skills to manage and manipulate this data. This paper presents the European Data Science Academy (EDSA), an initiative for bridging the data science skills gap across Europe and training a new generation of world-leading data scientists. The EDSA project has established a rigorous process and a set of best practices for the production and delivery of curricula for data science. Additionally, the project’s efforts are dedicated to linking the demand for data science skills with the supply of learning resources that offer these skills