Data analytics and data science in economics and social science
CM provides data analytics in the areas of economics, business administration, technology, social science and other areas.
Analytics is about past and present data patterns in order to generate insights. This insights allow clients to
make business processes more efficient
provide faster and better decision-making
improve the competitive advantage of businesses
help in understanding consumer preferences
In order to find data patterns analytics involves a variety of techniques including
Data Mining: CM understands data mining as a form of basic data discovery. Businesses administer large data bases or collect thousands of excel spread sheets, but often do not know how to use them. Data mining is a technique that connects and manage data bases, finds basic data patterns and illustrates relationships between variables or items. These techniques provide some idea of how the data can be actually used. The analysis of these patterns and relationships usually requires tools such as clustering, factor analysis, ranking or regression trees.
Statistical modelling: Statistical models provide further insights into the data patterns. This usually involves hypothesis testing, probabilistic modelling or statistical inference. The statistical models can include correlation analysis, linear regressions, logistic regression
Causal analysis: Causal analysis usually requires sound theoretical considerations in order to explain the causal relationships of data patterns. However, there are also a variety of techniques that can find causal explanations such as time series or difference in differences (DID) models.