While practical benefits of the Real-time data analytics over historic data analytics are obvious, it is also essential to understand and cater for its risks, challenges and limitations. Read More

This paper examines whether the so-called ‘London effect’, in which London’s schools improved rapidly and outperformed the rest of England on key performance measures between 2003 and 2013, has persisted through the high levels of change that have continued to characterise the school system in England since 2013. Read More

The paper explores the transformational shift in educational outcomes in London between 2003 and 2013. London’s schools have improved rapidly over the past decade, with primary and secondary schools now out-performing the rest of the country, at Key Stages 2 and 4, respectively. Improvements in many London boroughs have been staggering. Read More

As researchers push the boundaries of Natural Language Processing (NLP), the technology is becoming more embedded in our everyday lives. These advances will result in significant changes to the way we live. As an important facet of artificial intelligence, natural language processing is going to contribute to the proverbial invasion of robots in the workplace, so if companies are going to remain competitive, they must start to prepare. This document will throw some light... Read More

Quantitative analysis is no panacea from mistakes and discrepancies. The purpose of this White Paper is to consider contemporary challenges of the Quantitative Big Data analysis activities. Read More

Raw Data (also known and often referred to as Primary Data) collection is the starting point of any data analysis. Once the RD (Raw Data) is collected, it is processed to turn it into Information that can be converted into Knowledge further down the analysis track. The purpose of this White Paper is to explain the key RD collection principles and challenges and how these principles and challenges. Read More

Data Analytics is the study of breaking down information to change over data to helpful information. This information could assist us with understanding our reality better, and in numerous settings empower us to settle on better choices. Read More

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. Read More

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 stat... Read More