20 Types of Visualisation

Being able to Visualise data is very important in making the information held within easily digestible to us. Being able to see the data represented visually is much easier to understand for us, so we turn to Visualisation solutions to do this for us.

One example of a Big Data Visualisation solution is JupyteR, an open-source project which offers analysis and visualisation. It accepts programming input from a selection of languages and uses said code in order to provide a chosen method of Visualisation. There is also functionality to share and collaborate upon these visualisations.

Another solution is Tableau, which leans into Visualising Big Data in the context of machine learning analysis and what this produces, offering a range of integrations with various Big Data cloud services.

You can read more about visualisation solutions here: https://towardsdatascience.com/top-4-popular-big-data-visualization-tools-4ee945fe207d

Comments

Post a Comment

Popular posts from this blog

18 Types of Problem Suited to Big Data Analysis

There are a lot of problems where Big Data Analysis can provide a solution. One such area is in logistics. Mechanical failures of delivery vehicles represents a problem as it costs money to repair the vehicles, and this wastes the time of both the company and the customer who receive their items late. Big Data Analysis allows logistics companies to take preventative measures by monitoring all of their vehicles and identifying when their components are likely to fail, and by doing so they are able to ensure that their components are replaced at the optimum time for maintaining time and cost efficiency. For other examples of problems Big Data can solve, read here: https://www.forbes.com/sites/gregsatell/2013/12/03/yes-big-data-can-solve-real-world-problems/#416d707a8896
Read more

17 Strategies for Limiting the Negative Effects of Big Data

As mentioned earlier when discussing Big Data's implications for individuals, there is a high potential risk to personal privacy associated with the increase in personal data being stored about us everywhere we go. This cumulative data profile can hold a dangerous amount of personal information, and as such it is important to demonstrate responsibility with the data that is handled. In order to protect the individual from the malicious exploitation of their data, it is common practice to attempt to anonymise stored personal data in order to ensure that this can be used for things like pattern recognition and inferential analytics without exposing the individual to undue risk of their data being compromised. However, this is often insufficient as correlation can still happen by comparing multiple data sources and identification of individuals can still be done. You can read more here: https://www.ft.com/content/105e30a4-2549-11e3-b349-00144feab7de
Read more

08 Characteristics of Big Data Analysis

Analysing Big Data has become massively important in the modern business world, as companies have spent years accumulating data with no practical means of inferring information from it. Analysis of Big Data can fall into one of two categories: Decision-oriented and Action-oriented. Decision-oriented Analysis in general is when we are looking for an answer to a problem, and we look to infer a solution to this problem from our data by noticing patterns or trends to inform our decisions. Action-oriented Analysis lends itself more to monitoring data and watching for some specific circumstances that demand a response. For example, if there is a sudden upturn in purchases for a certain type of product, we are able to recognise this and react. For more information, read here: https://www.dummies.com/programming/big-data/data-science/characteristics-of-big-data-analysis/
Read more