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Data Science Foundation
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Data Forecast 5 2019
We are asking our members to predict the most likely developments in data use and data technology in 2019. We will identify five key trends in the answers submitted and publish them on the Data Science Foundation website. You could think along the following lines.

Which fringe technologies will become mainstream?
How will organisations change the way they manage and use their data?
What will be the driving influences on data analytics?
Are we likely to see huge advances created by the convergence of technologies?
Should the public at large be concerned about potential technical advances or data usage in 2019?

Please submit your comments as comments or via email to info@datascience.foundation with the subject line Data Forecast 5.

Members of the Data Science Foundation can comment directly on the Data Forecast 5 article on the website or via the forum.
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This paper is focusing on insurance and how AI is disrupting the sector. Starting from the conventional insurance process, we will move through specific novelties AI is introducing in the field to understand how insurance is completely changing the way people buy and think of insurance products. Finally, an eight-groups classification is proposed for the insurtech ecosystem.

https://datascience.foundation/sciencewhitepaper/ai-and-insurance
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Big data is often approached as a mere technical problem, while many times projects fail because of lack of strategic focus. Implementing the right process may help companies to efficiently run analytics works and for this reason, the paper proposes a few concepts that can support the organizational development of a big data blueprint. Three main ideas will be outlined here: the data lean approach, the maturity map, and different organization models for a data team.

https://datascience.foundation/sciencewhitepaper/big-data-management-how-organizations-create-and-implement-data-strategies
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We are making good progress towards launching a Data Science Foundation consultancy service and are pleased to show the branding associated with this venture.

Data Science Foundation Consultancy will operate as the profit-making side of the Data Science Foundation with profits generated being reinvested in the foundation. This will help support the work we are doing on behalf of the data science and analytics sector and will enable us to fund additional membership services.

We have a fabulous team of experienced and highly qualified data scientists, engineers and database specialists ready to take on both large and small projects. We will support the work of in-house data teams and will introduce advanced analytics to organisations new to the data driven world.

A new section will be created on the website within the next few weeks, on which we will publish information about the services being provided. If you would like to receive information about Data Science Foundation Consultancy prior to publication, please email a request to info@datascience.foundation

https://datascience.foundation/datatalk/data-science-foundation-consultancy
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The reason why we are studying AI right now more actively is clearly because of the potential applications it might have, because of the media and general public attention it received, as well as because of the incredible amount of funding investors are devoting to it as never before.

Machine learning is being quickly commoditized, and this encourages a more profound democratization of intelligence, although this is true only for low-order knowledge. If from one hand a large bucket of services and tools are now available to final users, on the other hand, the real power is concentrating into the hands of few major incumbents with the data availability and computational resources to really exploit AI to a higher level.

https://datascience.foundation/datatalk/why-ai-is-more-important-than-ever
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Unless you’ve been living under that proverbial rock, you’ve at least heard of cryptocurrency – those digital, non-corporeal monetary units that have the world abuzz these days. Bitcoin is probably the most obvious such crypto coin, but there are plenty of others that have sprung up in the wake of bitcoin’s success, such as Ethereum and Ripple.

However, chances are good that unless you’re a financial expert, and possibly even if you are such an expert, you’re not all that clear on what cryptocurrency is, how it works, the risks involved, or even why or how to accept it as payment. Within this guide, we’ll explore those topics and more to help you understand how to buy, use and invest with cryptocurrency.


https://datascience.foundation/sciencewhitepaper/cryptocurrency-a-quick-guide-to-buying-using-and-investing
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The Data Science Foundation will be launching a professional consultancy service within the next couple of weeks and we need your help to spread the word.

We have a fabulous team of experienced and highly qualified data scientists, engineers and database specialists ready to take on both large and small projects. We have the capability to support the work of in-house data teams and the ability to introduce advanced analytics to organisations new to the data driven world.

The profits generated by the data science consultancy service will be reinvested in the foundation. This will help support the work we are doing on behalf of the data science and analytics sector and enable us to fund additional membership services.

Contact the foundation if you have a project you would like to discuss and please pass this information on to colleagues and acquaintances who may be interested. Your support here is appreciated and will help the continued development of the Data Science Foundation. I will keep you posted.
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This paper presents, new geospatial big data mining and machine learning framework for geospatial data acquisition, data fusion, data storing, managing, processing, analysing, visualising and modelling and evaluation. Having a good process for data analysis and clear guidelines for comprehensive analysis is always a plus point for any data science project. It also helps to predict required time and resources early in the process to get a clear idea of the business problem to be solved.

https://datascience.foundation/sciencewhitepaper/a-new-data-science-framework-for-analysing-and-mining-geospatial-big-data
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The hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to try to bring some clarity to what is happening on the investment side of the artificial intelligence industry.

https://datascience.foundation/sciencewhitepaper/investing-in-ai-an-overview #artificialintelligence #funding #venturecapital
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The objective of this paper is to provide a comprehensive perspective on the advancements in the area of Artificial Intelligence and how the infusion of cognitive customer engagement can positively impact the infamously unpredictable consumer cyclical retail industry.

https://datascience.foundation/sciencewhitepaper/the-future-of-artificial-intelligence-in-retail
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