The aim of the study is to find most relevant factors that influence the salaries of Data Scientists. In this paper we will use association rules for discovering frequent patters of occurrence.

The aim of this article is to use dimension reduction techniques as a pre-step method for clustering analysis. The purpose is to extract intrinsic dimension from footballers data set. Initially the data set has more than 50 variables but most of them seem to be correlated. Therefore we will see how many dimensions do we really need to explain greater share of the variance.

The case of this paper is to divide the properties into clusters. Using clustering methods we obtain groups of similar objects which as expected will be dissimilar between groups. Clustering assists in market segmentation, dividing the overall property market into distinct groups. This can be valuable for targeted marketing strategies, as different clusters may have varying needs, preferences, and price sensitivities.On the other hand, clustering can be used for assessing risk. Properties within the same cluster are likely to share similar risks and vulnerabilities. This information is valuable for investors, allowing them to diversify their portfolios and manage risk more effectively. Finally, this allows for a deeper understanding of the competitive landscape, helping property professionals make informed decisions about pricing, marketing, and overall strategy.