In my previous articles I have discussed MDSV or Multidimensional Data Science. This methodology is based on the study of many data points in many different fields such as business, medical, law and social sciences. The process of extracting meaningful information from large volumes of data is a crucial part of data science, so I'm not sure why MDSV is still in the shadow of MDSC.
Multidimensional Data Science is a more refined version of the traditional ML and is used mainly in scientific and business applications. As a matter of fact, one of the most commonly used tools in scientific research is Multidimensional Data Science (MDS). Although MDS is a relatively recent field of research, it is gaining immense popularity because of its powerful applications in various fields.
Many researchers, however, are still not aware of the importance of data mining, so they have misconceptions about this technology. One of them is that data mining is an inappropriate way of obtaining data. This is absolutely wrong! When you are looking for a solution to a problem, whether it is medical financial, legal or even a social problem, data mining is the best tool to find it.
Data mining is the process of using the power of computers and other technology to extract meaningful information from large volumes of data. There are several techniques used for this purpose, and the most popular ones today include the following:
– Fisher Scatter – This is a statistical method that is based on a mathematical formula. It can be applied to a number of types of data and can provide some useful insights. In fact, a Fisher Scatter plot is very useful in identifying relationships between variables, particularly in the case of regression analysis. It is also very useful in performing tests of relationships and hypothesis generation.
– Correlation – This is basically the relationship between two variables. One can identify a set of relationships in which each variable follows the other closely. For example, when analyzing the relationship between temperature and humidity, a correlation plot can show the relationship between the two variables. A similar type of plot can be made when analyzing the relationship between income and expenditure. As you can see, the correlation can be an important tool when trying to determine the causes of data trends.
– Relationship Matrix – Another type of correlation plot is known as the relationship matrix. A good example of this is a relationship matrix of a sample of students from a university will display the relationship between the performance of all the students over time. This plot can also be used for training purposes, when it comes to training new employees in a particular subject. The main advantage of a relationship matrix is that it provides a great way to visualize data.
– Hypothesis – A hypothesis can be stated as a prediction on how a data can help to . . . . . . answer a particular question. As mentioned above, there are several types of hypothesis, and each one of them has its own purpose. A simple example is a regression or mixed effects analysis.
– Final Step – Finally, if you have successfully determined the most useful data points by applying these methods, you can use the information from these sources to make a final analysis. Some examples of this are the following: A P values, Confidence limits, Monte Carlo simulations, and meta-regression. You can find more detailed information in the following article.