# Data Science for Finance: Best-Suited Methods and Enterprise Architectures
## 4 Findings
### Relationships between Data Analytics Methods and Disciplines of Informatics  
Next, we organize the methods used and describe their relation to different disciplines of informatics. All the main data analytics methods are within the broader domains of computational intelligence, artificial intelligence, decision support, and data science. We provide an overview of the data methods and disciplines of informatics to which they belong in Table 2, based on [41]. The categorization of the various methods is not strictly adhered to, and some methods may fit into more than one scientific discipline. In an enterprise environment, in financial institutions, and in financial services technology companies, the decision-making is traditionally grounded in data analysis. The rapid development in data management over the past decades has led to a set of tools that are classified into various disciplines of information technology. In data management and capture, there has been an evolution in the data-handling systems' overarching network and hierarchical, relational, and NoSQL database management systems, then data warehouses, data marts, and data lakes. This progress makes it possible for an enormous volume of data to be collected in databases and accessed on the web by companies. Decision support has been an interdisciplinary area between management, computer sciences, and business administration. It utilizes the methods of operational research in the mathematical background. _Business intelligence_ is perceived as data modeling and retrieval that are based on well-organized data warehouses storing high-quality data so that business intelligence creates standard reports. The business requirements for forecasting and in-depth analysis stepped over the services of reporting. The exploratory data analysis along with the _multidimensional_ databases offered approaches that provided techniques for such complex analysis as univariate, bivariate, and multivariate statistical investigation [42; 43], i.e., a tool set for multidimensional analysis. The development of interrelated fields such as computational intelligence, artificial intelligence, and data science produced algorithms that combined with the exploratory data analysis laid the foundation for methods that can tackle complex business problems to forecast, predict, and prescribe the de \begin{table}
\begin{tabular}{l l} \hline \hline
**Methods of Data Analytics** & **Scientific Discipline within Informatics** \\ \hline Unsupervised machine learning & CI (AI), DSc \\ Reinforcement learning & CI (AI), DSc \\ Business reporting and decision support & BI \\ Multidimensional analysis & BI \\ Natural language processing, querying & BI-AI-CI \\ Data management, administration & BI-DS-CI-AI \\ Big Data management & BI-DS-CI-AI-DSc \\ Inferential statistics & DS \\ Predictive modeling & DS-DSc \\ Supervised machine learning & DS-CI (AI), DSc \\ Descriptive statistics & DS-BI \\ Data discovery/query/retrieval & DS-BI \\ Data visualization & DS-BI \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationships between methods of data analytics and disciplines of informatics [41]. Supervised machine learning algorithms are employed for problems of classification and prediction. The precondition to using supervised learning is to own data that represent the outcomes of interest. These data can be used for \"training\" the algorithm. Simple linear regression is the simplest example where there is one outcome variable, in which the training data values are known, and there is one predictor (explanatory) variable. Unsupervised machine learning algorithms can be applied where no outcome variable can be forecast or categorized; therefore, there is no training set and no chance to learn from available cases. Typical techniques that fall into this genre of algorithms are as follows: association rules, dimension reduction methods, and clustering. Data visualization can be used for graphical representation in tandem with data exploration.