Big Data

Big Data in Science Big Data can be used in science and research. One way it has been used is to build a vast model and gather intelligence for scientists based on wind conditions following Hurricane Sandy. This meant they could more accurately predict future hurricanes and save lives. This is an important factor when considering the effects of climate change and adapting to them. I read that Big Data was able to analyse large databases, including atmospheric conditions, ocean temperatures, terrain elevation, and land coverage for the hazard simulation. This would have been much less accurate without Big Data and would require much more time and resources, which weren't an option. https://www.informationweek.com/machine-learning-ai/hurricane-sandy-big-data-predicted-big-power-outages Another way Big Data can be used in science is to improve efficiency in healthcare. For example, it can recognise patterns in complex data, such as protein folding in biology, or help us understand h...

 Limitations of Traditional Statistics In my last post, I spoke about the use of traditional statistics. Now, I will cover some of the drawbacks of using traditional statistics to make predictions in the real world. Traditional data analysis has been practised with structured data, often in a relational database; however, Big Data isn’t as manageable with this structured format. Traditional statistics is about analysing and summarising data and is suited to processing smaller amounts of linear, repeatable data to draw a conclusion. The easiest environments are when the data has a stable relationship – e.g. regular exercise increases, obesity decreases. Therefore, the practicality of traditional data analysis is limited when applied to the real world. More recently, there is now the availability of new large data sets and cheaper and faster computing power. In addition, there has been a surge in machine learning research and development, which aims to automatically discover re...

 Traditional Statistics Today we learned about types of traditional statistics and how they're used to draw conclusions.  Statistics is the discipline that concerns the collection, organisation, analysis, interpretation, and presentation of data. It can be broken up into descriptive and inferential statistics. Descriptive Statistics Only the subjects that have been analysed have findings implied upon them. If we had the political views of 20 people, we may be interested in the voting intentions of these people or the distribution or spread of the votes, but the findings are limited to only these 20 people. Inferential Statistics If you don’t have access to the whole population you’re interested in, but only a small portion. If you want to study the political voting intentions of everybody in Scotland. From what I've learnt about the two types, I would say that descriptive is more accurate as conclusions are drawn from what the data had been directly collected from. Howev...