Skip to main content

Post 7: (Question 8): Characteristics of big data analysis

Within big data there is always a will to manage this data and to do this the data first needs to be characterised and to organise our understanding of this big data. Due to this Big Data can and is defined by more than one characteristic. There are in fact 3 characteristics that need to be taken into account and these are Volume, Velocity and Variety. Volume refers to the size of the data that is continuously growing within the world of computing and this raises the question of the quantity of the data itself. Velocity refers to the speed at which the data is processed and this can also be questioned within itself. Variety however refers to the varying types of data, this allows us to question just how each data format differs from one another. 
These characteristics also raise some very important questions that allow us and aid us in deciphering Big Data but they also aid us in learning how to deal with massive and varying data at a manageable pace and within a reasonable time frame so that the value of the data can be deciphered, be analysed and a subsequent response can be provided as swiftly as possible. 

Comments

Popular posts from this blog

FutureLearn Week 2: Post 3 of 4

Two of the biggest challenges of big data is Analysing and Visualising the data. Firstly with analysing the data, the size of big data files can sometimes be substantial, there are many things that must be considered before downloading the data, for example the file size, how long the data file will take to download, will all of it be necessary or will part of the file suffice and is there enough storage space within the system itself. Visualisation is way to represent the data in a way that is easier to understand such as word clouds and things of the like. This will aid users in seeing the prominent and key terms from the analysis of the data sets. The first step after downloading the data would be to quality check it to ensure that each field had the appropriate data types in each field and to ensure that the user understood the meaning of each field. Keeping a copy of the original data would be essential as well as each documented version change for each stage of visualisation....

FutureLearn Week1: Post 3 of 3

In the video for futurelearn activity 3 it was discussed how big data can help in industries. With regard to the retail industry, retailers around the world are making use of big data to aid them in understanding their customer base and managing the supply. The advertising industry makes use of big data for online advertising, real-time buying and selling. 

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....