Discussion on the use of graph theory/network science in the field of business intelligence.
Hey I would love to start a discussion on the application of graph theory and graph databases in the context of business data. I have identified several applications but it would be interesting to hear your thoughts on these and other applications you can think of!Applications that I have identified are:Airport connectionsDelivery logistics - like the one eBay implementedCo-purchasing networksWebsite navigationBank transfers
Data Science Foundation Consultancy
Contact firstname.lastname@example.org if you have a project you would like to discuss.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.
Investing in AI: an overview
If you want to have an overview on the investors and accelerators who specifically invest only in AI, here there is a good resource for that:https://medium.com/@Francesco_AI/unsupervised-investments-ii-a-guide-to-ai-accelerators-and-incubators-4dc762d57c4b
Blockchain Research and Discussion Group
The Data Science Foundation is in the process of establishing a blockchain group to share research and discuss the latest developments. If you are interested in joining comment here or email email@example.com
The Data Science Foundation is source of information on big data techniques and practices. We review and publish industry news, comment and white papers submitted by our members. We are recruiting editors and contributors to work with us on this site.
Is There Such Thing As Too Much Data?
In today’s business environment, data rules. Because it is used to determine every strategic move at each level of an organization, it makes sense that you should collect as much information as possible right? I argue that this is actually not the case! Like most things in life, too much of anything is bad- data included. The two major reasons why I argue this position are the paradox of choice and its causal relationship to analysis paralysis. If you have found yourself overwhelmed with a data set before and thought, “Where in the world do I even begin?” then this is the article for you. So grab a seat, pour yourself a cup of coffee, and get ready to never look at data the same way again. This article explores the idea that too much data can actually be detrimental to analysts.
Data Science Foundation Consultancy
We are making good progress towards launching a Data Science Foundation consultancy service and are pleased to show the branding associated with this venture.
Why AI Is More Important Than Ever
This article explains the rationale of why ML and AI are so important nowadays, as well as tackle a few relevant issues related to AI development.
Data Science Nomenclature for Managers
This article aims to explain few common concepts and terms in the data science world for an audience that is not as technical as an engineering one.
Alternative Mechanisms of Fundraising
This article describes the alternative mechanisms for doing fundraising nowadays and how Ai and blockchain are affecting this specific aspect of the business
Data will drive every decision brands make
We’ve seen a growing amount of complexity within the digital space in recent years; new mobile devices, new technologies such as virtual reality, the growth of the Internet of Things (IoT), major breakthroughs in the development of artificial intelligence (AI) and an increasing adaptation of machine learning across industries. With all that in mind, it’s clear that 2017 will be even more challenging for marketers than years prior.
Data science and machine learning are often associated with mathematics, statistics, algorithms and data wrangling. While these skills are core to the success of implementing machine learning in an organization, there is one function that is gaining importance – DevOps for Data Science.READ MORE
As the lifesciences sector continues to evolve, importance of optimizing quality related to capital investment decision increases among companies.READ MORE
The Analysis presents the study of Global Big Data in Healthcare facilitating the regional and country wise analysis covering the strategic analysis of each market player and the market share they holdREAD MORE
Amazon is dropping some hints about the kind of skills the company is looking for in its health-care partnership with Berkshire Hathaway and J.P. Morgan.READ MORE
Pareto Intelligence, an analytics solution provider for healthcare organizations, has uncovered a multitude of data mistakes in government health care programs which, say the company, cost taxpayers more than $150 million in 2017.READ MORE
Musicologists are beginning to uncover statistical patterns that govern how trends in musical composition have spread.READ MORE
Real-time data streaming is still early in its adoption, but over the next few years organizations with successful rollouts will gain a competitive advantageREAD MORE
Illustration of the challenges facing digital audiovisual archives, and the potential of new technologies, including AI, to overcome these challenges.READ MORE
Data is definitely priceless. But it is not a cake walk to analyze it as greater things come at a greater cost. With the exponential growth in data, there requires a process to extract meaningful information as conclude to useful insights.READ MORE
Data mining helps discover data patterns that help improve the quality of drug discovery and drug delivery methodsREAD MORE
Model from MIT Lincoln Laboratory Intelligence and Decision Technologies Group sets a new standard for understanding how a neural network makes decisions.READ MORE
We now know how data science works, at least in the tech industry. First, data scientists lay a solid data foundation in order to perform robust analytics.READ MORE
Blossom Academy, a data science talent accelerator company in Ghana that recruits students from several West African universities to participate in its data science training program.READ MORE
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.READ MORE
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.READ MORE
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. To many people, the term “cryptocurrency” might seem pretty self-explanatory, but if you take a deeper look at the subject, you’ll likely find that it’s more complicated than it might seem at first glance. After all, crypto coins have virtually nothing in common with traditional currency, other than acting as a store of value. Even that similarity can be questionable. So, what is a cryptocurrency?READ MORE
Geospatial Big Data analytics are changing the way that businesses operate in many industries. Although a good number of research works have reported in the literature on geospatial data analytics and real-time data processing of large spatial data streams, only a few have addressed the full geospatial big data analytics project lifecycle and geospatial data science project lifecycle. Big data analysis differs from traditional data analysis primarily due to the volume, velocity and variety characteristics of the data being processed. One motivation in introducing a new framework is to address these big data analysis challenges. Geospatial data science projects differ from most traditional data analysis projects because they could be complex and in need of advanced technologies in comparison to the traditional data analysis projects. For this reason, it is essential to have a process to govern the project and ensure that the project participants are competent enough to carry on the process. To this end, 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.READ MORE
The hype about AI makes really difficult for experienced investors to understand where the real value and innovation are. I would like then to humbly try to bring some clarity to what is happening on the investment side of the artificial intelligence industry.READ MORE
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.READ MORE
Data security has never been a more important consideration for consumers or for the businesses they patronize and partner with. It’s also never been more difficult to ensure. Every single day, consumers’ personal, health and financial information is at risk of theft. This can lead to identity theft, and to serious financial ramifications. While military-grade encryption on websites and through smartphone and tablet apps can be an important precaution, it’s just a Band-Aid. More must be done. Blockchains may hold the key to ensuring data security, but how does the technology that underpins bitcoin and other cryptocurrencies ensure data security for consumers and businesses?READ MORE
Blockchains, a technology behind the success of cryptocurrency, symbolize an great application of cryptography and technology to one of the biggest problems of record-keeping for financial institutions, and they may create some far-reaching changes in method of transactions. Many big institutions of the industry had invested billions in the new technology, and many other industries have proposed new methods of transactions and ownership shift by use of the blockchains. This article studies the possible implications of the changes for stakeholders into the business like investors, employees, auditors, external shareholders, customers and other parties. The greater transparency, better transaction, accuracy, greater liquidity and lower cost offered by the technology may considerably turn over the uses and way of working of the industries.READ MORE
This article is about the future of AI. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world.READ MORE
Unless you’ve been stuck under that proverbial rock for a few years, you’ve at least heard of the Internet of Things (IoT) and how it is connecting us in new and interesting ways. The rise of the smart home is one way that the IoT is changing things for people around the world – homes filled with devices that can communicate with one another, with people living in the home, and even with outside third parties. However, this technology is not constrained just to our homes. It’s growing in terms of both scope and capabilities. Enter the smart city, where the Internet of Things will impact everything from lighting to the flow of traffic through urban centresREAD MORE
Data is everything. In fact, thieves are now targeting consumer and business information rather than financial assets, simply because there is so much more value in data. Find out more about how GDPR is changing the way we all work with data in the midst of this Information age.READ MORE
Traditional sources of energy are in decline and the world is shifting towards renewable forms of energy. There are around 50 solar power plants in India. These power plants incorporate solar power grids, inverters, batteries and various sensors for monitoring and control. There is a requirement for efficiency and to detect system faults; to this end the data generated by sensors must be constantly monitored and analysed. With cloud service technology, solar grids sensors can be connected to remote monitoring facilities by utilizing Internet of Things (IOT) technology. The huge volumes of data produced by sensors is now manageable, and with Big Data techniques and MapReduce algorithm, it is not only possible to store and analyse data, but predict potential problems. This paper investigates ways to store, analyze and visualize the data produced by solar sensors.This paper aims to analyze following things: 1) Detecting Flaws in solar power plants/farms 2) Analyzing Power generated 3) Visualizing the resultsREAD MORE
This paper describes the advantages and costs of patenting, as well as what can be patented today in the AI domain and what instead needs or should be kept undisclosed.READ MORE
With recent breakthroughs in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), financial companies are beginning to pass the task of identifying and executing trades on to automated systems. Algorithmic Trading (AT), smart stock advisors, and ‘self-learning’ Reinforcement Learning (RL) agents now powerful tools in the financial decision-making process. In this white paper, we’ll discuss how financial companies can benefit from the ongoing AI revolution in their investment and technical analysis, portfolio management and diversification, and other important areas of investment decision-making. Hopefully, by the end of the paper, you’ll have a better understanding of how AI can boost a financial investment strategy.READ MORE
This article provides a series of forecasts regarding the development of AI and robotics. We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. The interesting aspect of those is that are predictions made one year ago, and many turned out to be true.READ MORE
The Data Science Foundation is your source of information on big data techniques and practices. We review and publish industry news, comment and white papers submitted by our members. Our information includes various data science courses available and offered by different universities and online data science course administrators as well as information on data science training & certification. We are recruiting editors and contributors to work with us on this site. Get in touch.
We encourage everyone who works in big data or with data scientists to join us and share their knowledge and experiences with the community. We provide support and network opportunities to data science practitioners and managers buying advanced analytical services.