Skip to main content

Post #6: Traditional Statistics: Descriptive and Inferential in Big Data

Two types of Traditional statistics in Big data include Descriptive and Inferential. Descriptive means averages and working on sets of numbers. Descriptive statistics is a type of statistic in which a data set is summarised and the characteristics are described. This descriptive data is usually displayed through the use of tables, charts etc. but is most commonly reported as a measure of a central tendency. A central tendency is a typical value for a distribution, it is also been known to be called a location or centre of the distribution. The arithmetic mean is the most common measure of a central tendency, this is the median and the mode. The mean is the average of all the values, the median is the exact middle of the data set while the mode is the most frequent value in the data set. 

The goal of traditional statistics is analysing and summarising data, providing tight assumptions about the problem and data distributions as well as using conservative techniques and approaches.  

Comments

Popular posts from this blog

FutureLearn Week 2: Post 1 of 4

Open data has been increasing for some time now with data being made open on various sites globally. There are many advantages to having open data, these advantages include being able to share public data sets so that they can be compared. These open data sources can also be used for environmental purposes or even health issues. Disadvantages of open data would include the fact that the site providing the data would be inherently biased and formed in the opinion of the creator.

Post 11: (Question 7) Limitations of traditional data analysis

As with all things there will always come limitations to data analysis due to the fact that it is created by humans and is subsequently subject to human error. Some of the limitations that you may come across would be that the data may be incomplete, whether it be missing values, or lack of a section of necessary data. This could severely limit the data's usability. Survey data can also be scrutinised due to the human component. People do not always provide accurate information through surveys and many are likely to not answer truthfully. For example if a person were asked how much alcohol they consume within a week they are likely to say less than their actual intake. 

Post #2: History of Big Data

Big Data has been described by some Data Management pundits (with a bit of a snicker) as “huge, overwhelming, and uncontrollable amounts of information.” In 1663, John Graunt dealt with “overwhelming amounts of information” as well, while he studied the bubonic plague, which was currently ravaging Europe. Graunt used statistics and is credited with being the first person to use statistical data analysis. In the early 1800's, the field of statistics expanded to include collecting and analysing data. The evolution of Big Data includes a number of preliminary steps for its foundation, and while looking back to 1663 isn’t necessary for the growth of data volumes today, the point remains that “Big Data” is a relative term depending on who is discussing it. Big Data to Amazon or Google is very different than Big Data to a medium-sized insurance organisation, but no less “Big” in the minds of those contending with it. Such foundational steps to the modern conception of...