Blog For Big Data

The power of visualising data The power of visualising data is its ability to reduce complexity but continuing to retain the information. This will allow decision making to be made in a shorter time and can be used as simple representations. The simple representation makes it easier for casual users to perceive the information much more easily.

Real-life examples of environmental analytics A real life example of citizen science analysing the environment is that it can contribute to better and more accurate evaluations of human infrastructures in wildlife such as recording roadkill accidents so that it can be made available to emphasise how much roads can impact wildlife.

Citizen science can be applied to any and all kinds of scientific observations which require scientific monitoring data. Weather phenomena, climate change impacts in the environment, monitoring of local air quality and also monitoring the effects and impact of climate change on different species of animal. Citizen science for climate simulation and ecology can be incredibly helpful in monitoring many things such as monitoring microplastics in water so that people can be more aware of what products contribute to this and lead efforts to reduce or change the process in which manufacturers produce these products to eliminate it from water sources. 

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. 

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....

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.

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.

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. 

The best example of environmental big data is that of weather forecasting, as discussed in the video on futurelearn. The use of drones to monitor and measure atmospheric pollutants is another form of environmental big data. This can all be used to reduce carbon footprint by making use of the data to reduce or introduce more efficient transport in areas with high levels of pollution.

Through day to day use of the internet, tracking cookies, social media postings and searches through Google or any other search engine for topics that interest me as a person this will all contribute to big data. 

Part A Dr. Charles W. Stryker covered a high number of points within his TedTalk, the main points that he made were that big data encompasses everything. He compared the job hunting of people today versus the job hunting of those who would look for a job before 1993, he discussed the same comparison when it came to people searching for relationships, how people communicate and stay in touch with friends as well as investments and business. Big data is now usable by analysing the data as it comes in. The medical industry is using data for cancer to track cases, Dr. Stryker stated that the best oncologist in the United States tracks roughly 6-8 cases and that if they request data they can easily find better care for their patients 85% of the time based solely on data analytics. Big data will soon be able to track the food that people consume over the course of years and through big data a diabetes drug was prescribed by a doctor who noticed a correlation between his patients on ...

Making use of big data analysis within the healthcare sector has had an incredibly positive outcome and has also saved many lives over the years. When big data is applied to the healthcare sector it uses the specific health data of the population or individuals to potentially aid in preventing epidemics, curing diseases as well as cutting costs to healthcare in general. The most common and widespread use of big data in medicine is in the use of patient records as each patient has their own individual record which will include the patients medical history, their demographic, allergies, test results received from the labs to name a few. These records are shared through incredibly secure information systems and are available to those in the private and public sectors. The files can be modified by a doctor via a modifiable file which ensures that there's no issue with data replication. Another use of these patient records are that they can also alert or trigger warnings/reminders when...

Big Data as been an integral part of many companies beginning to beat their competition and vastly outperform them. In various industries there are new companies and seasoned competitors that use data based strategies to combat their competition with new innovations in their respective fields. This usage of big data can be seen in almost every sector of every industry whether it be the IT industry or even healthcare.  In regard to healthcare, data scientists have been continuing to analyse the outcomes of pharmaceutical products and companies have become focused on discovering the risk and benefits that were absent during clinical trials. Big data has been vastly important in analysing the trials and has made the outcomes of future trials more predictable. Those who adopted this method early on have been using data that they have taken from sensors which were embedded in various products from industrial apparatus to kid's toys. This use of big data aids companies determine which...

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...