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FutureLearn Week 2: Post 2 of 4

With big data becoming much more important in recent years the need for data scientists has also increased. These data scientists are educated in discovering, collecting, processing, analysing and presenting information gained from these large amounts of data and often compare and combine it with different sources.

The skills these data scientists must have are data visualisation skills, statistical skills, data processing and systems engineering skills, they must have an understanding or have a sufficient level of skill with programming languages as well as mathematical and analytical skills.

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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 #1: Definition of Big Data

Big Data  is a term that is used to describe a massive volume of both structured and unstructured  data  that is so  large  it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of  data  is too  big  or it moves too fast or it exceeds current processing capacity. Big Data  comes from text, audio, video, and images.  Big Data  is analysed by organisations and businesses for reasons like discovering patterns and trends related to human behaviour and our interaction with technology, which can then be used to make decisions that impact how we live,  work , and play. This Big Data can also  be analysed for insights that lead to better decisions and strategic business moves.

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.