53. Data Analysis

Data analysis is a process of gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data processing is any computer process that converts data into information or knowledge. The processing is usually assumed to be automated and running on a computer. Because data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems to emphasize their practicality.

Nevertheless, both terms are roughly synonymous, performing similar conversions; data-processing systems typically manipulate raw data into information, and likewise information systems typically take raw data as input to produce information as output. In the context of data processing, data are defined as numbers or characters that represent measurements from observable phenomena. A single datum is a single measurement from observable phenomena. Measured information is then algorithmically derived and/or logically deduced and/or statistically calculated from multiple data. (Evidence).

Information is defined as either a meaningful answer to a query or a meaningful stimulus that can cascade into further queries. Someone who is skilled at analyzing data. An expert who studies financial data (on credit or securities or sales or financial patterns etc.) and recommends appropriate business actions. A licensed practitioner of psychoanalysis. A financial professional who has expertise in evaluating investments and puts together buy, sell, and hold recommendations on securities. Also known as a financial analyst or security analyst.

The quality of the data can be assessed in several ways. First of all the distribution of the variables before data cleaning is compared to the distribution of the variables after data cleaning to see whether data cleaning has had unwanted effects on the data. Second, the missing observations in the data are analyzed to see whether they are missing at random and whether some form of imputation (statistics) is needed. Third, extreme observations in the data are analyzed to see if they seem to disturb the distribution. If that is the case, robust techniques can be applied.