A new Data Science Framework for Analysing and Mining Geospatial Big Data
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.
the full geospatial big data analytics project lifecycle and geospatial data science project lifecycle. - Agree nice framework
Cryptocurrency A Quick Guide to Buying, Using and Investing
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?
how to buy, use and invest with cryptocurrency. - Some great platform and uses. Thanks for sharing
Big data management: How Organizations Create and Implement Data Strategies
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.
Implementing the right process may help companies to efficiently run analytics works and for this reason, - Agree
AI and Insurance
This paper focuses on how AI is disrupting the insurance sector. Starting from the conventional insurance process, we will move through the specific novelties AI is introducing in the field in order to understand how AI is completely changing the way people buy and think of insurance products. Finally, an eight-group classification is proposed for the insurtech ecosystem.
AI is introducing in the field in order to understand how AI is completely changing the way people buy and think of insurance - Agree
Fantastic (data)-Beasts and Where to Find Them: Data Scientists and Data Engineers
This paper discusses the role of the data scientist, what a data scientist is, and the set of skills needed to become one.
Fantastic (data)-Beasts and Where to Find Them: Data Scientists and Data Engineers - Nicely written artcile.
Nonparametric Statistical Test Approaches in Genetics Data
The biggest challenge of genetic research lies in significant and intellectual analysis of the large and complex data sets generated by the cutting edge techniques like massively parallel DNA sequencing and genome wide analysis. Statistical analyses are the most important of such experimental data. When the data are not normally distributed and using non numerical (rank, categorical) data then use the nonparametric test for exact result of research hypothesis. Order statistics are among the most fundamental tools in non-parametric statistics and inference. Non parametric test does not depend upon parameters of the population from which the samples are drawn, no strict assumption about the distribution of the population. Nonparametric tests are known as distribution free test also because their assumptions are less and weaker than those connected with parametric test. Nonparametric test does not follow probability distribution. To analyze microarrays and genomics data several non-parametric statistical techniques are used like Wilcoxon’s signed rank test (pre-post group),Mann-Whitney U test (two groups) or Kruskal-Wallis test (two or more groups).Importance of this paper is to look at the non-parametric test how to use in genetic research and provide the understanding of these test
Order statistics are among the most fundamental tools in non-parametric statistics and inference. - Agree nice point
Big Data: The next frontier for advance, competition, and efficiency
Nowadays organisations are starting to realise the importance of using more data in order to support decision for their strategies. The size of data in the world is growing day by day. Data is growing because of vast use of internet, smart phone and social network. Big data is a collection of data sets which is very large in size as well as complex. Generally size of the data is Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyse this large amount of data. As the internet is growing, the amount of big data continues to grow. Big data analytics provide new ways for businesses and government to analyse unstructured data. Nowadays, big data is one of the most talked about topic in the IT industry. It is going to play an important role in the future. Big data changes the way that data is managed and used. Some of the applications are in areas such as healthcare, defence, traffic management, banking, agriculture, retail, education and so on. Organisations are becoming more flexible and more open. New types of data will give new challenges as well.
Generally size of the data is Petabyte and Exabyte. Traditional database systems are not able to capture, store and analyse this large amount of data - Agree
Architecture of Data Lake
Data can be traced from various consumer sources. Managing data is one of the most serious challenges faced by organizations today. Organizations are adopting the data lake models because lakes provide raw data that users can use for data experimentation and advanced analytics. A data lake could be a merging point of new and historic data, thereby drawing correlations across all data using advanced analytics. A data lake can support the self-service data practices.
Managing data is one of the most serious challenges faced by organizations today. - Problem for almost every organisation
Data Science : Brief understanding of Typical Project Life-cycle, Tools, Techniques and skills
Every step in the lifecycle of a data science project depends on various data scientist skills and data science tools. The typical lifecycle of a data science project involves jumping back and forth among various interdependent data science tasks using variety of tools, techniques (mostly statistical methods and formula), programming etc. Let us try to see what could be a typical life cycle.
he typical lifecycle of a data science project involves jumping back and forth among various interdependent data science tasks using variety - Agree
SURVIVAL PARAMETRIC MODELS TO ESTIMATE THE FACTORS OF UNDER-FIVE CHILD MORTALITY
Exploring parametric survival models in daily practice of child mortality research is challenging. It may be due to many reasons including popularity of Cox regression and lack of knowledge about how to perform it. This paper provides the application of parametric survival models by using available R software with illustration.
Exploring parametric survival models in daily practice of child mortality research is challenging. - A nicely written paper