# Data-Sourced Analysis Task

A Data-Sourced Analysis Task is an analysis task whose input is a dataset (to report constituent data patterns).

**Context:**- It can (often) be preceded by a Data Collection Task.
- It can range from being an Exploratory Data Analysis Task, to being a Confirmatory Data Analysis Task, to being a Functional Data Analysis.
- It can range from being a Categorical Data Analysis Task to being an Ordinal Data Analysis Task to being a Numerical Data Analysis Task.
- It can range from being a Univariate Data Analysis Task to being a Multivariate Data Analysis Task.
- It can range from being a Tabular Data Analysis Task to being a Multi-Relational Analysis Task (such as a graph analysis task).
- It can range from being a Small-Scale Data Analysis Task to being a Large-Scale Data Analysis Task.
- It can range from being a Batch Data Analytics Task to being an Interactive Data Analytics Task.
- It can range from being a Retrospective Data Analytics Task to being a Predictive Data Analytics Task.
- It can be solved by a Data Analysis System (that applies a data analysis algorithm).
- It can support a Predictive Analytics Task.

**Example(s):****Counter-Example(s):**- a Data Processing Task, such as data cleaning.
- Archeological Analysis.

**See:**Data Analysis Discipline, Data Analysis Ontology, Business Intelligence, Descriptive Statistics, Exploratory Data Analysis, Confirmatory Data Analysis, Text Analytics, Unstructured Data, Data Integration, Data Visualization.

## References

### 2014

- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/data_analysis Retrieved:2014-9-20.
**Analysis of data**is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering 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 mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.

Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination. The term

*data analysis*is sometimes used as a synonym for data modeling.

### 2009

- Master's Degree in Statistics at the University of Chicago. http://www.stat.uchicago.edu/admissions/ms-degree.html
**Data Analysis:**This is the core of the subject, teaching you the principles and methods for analyzing data and designing experiments. Provides a broad background for working as a statistician in industry or government.