Graph Analytics and Big Data

Graph analytics, which is an analytics alternative that uses an abstraction called a graph model. The simplicity of this model allows for rapidly absorbing and connecting large volumes of data from many sources in ways that finesse limitations of the source structures (or lack thereof, of course). Graph analytics is an alternative to the traditional data warehouse model as a framework for absorbing both structured and unstructured data from various sources to enable analysts to probe the data in an undirected manner. Big data analytics systems should enable a platform that can support different analytics techniques that can be adapted in ways that help solve a variety of challenging problems. This suggests that these systems are high performance, elastic distributed data environments that enable the use of creative algorithms to exploit variant modes of data management in ways that differ from the traditional batch-oriented approach of traditional approaches to data warehousing.

Big Data Analytics and Predicting Election Results

The best way to predict the future is to study past behavior. This is the underlying idea behind Big Data Analytics. The 2008 Obama election campaign was one of the first to take advantage of data-driven methods in the race to an elected office. The Obama campaign had a data analytics team of 100 people. This shows how deeply data analytics impacts the world.

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.

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.

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

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.