Data Analysis Task

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

• Context:
  • Task Input: datasets, data collections
    • Optional Input: analysis parameters, domain knowledge, analysis constraints
  • Task Output: analysis results, data patterns, insight reports
  • Task Performance Measure: analysis quality metrics such as accuracy, completeness, and reproducibility
  • It can (typically) process Raw Data through data preparation steps.
  • It can (typically) generate Data Patterns through pattern detection algorithms.
  • It can (typically) validate Analysis Results through statistical tests.
  • It can (typically) produce Analysis Reports through reporting frameworks.
  • It can (often) be preceded by a Data Collection Task.
  • It can (often) be followed by a Data Visualization Task.
  • It can be performed by a Data Analyst (and be described in a data analyst JD).
  • 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 implements a data analysis algorithm).
  • It can support Data Stewardship Tasks, Predictive Analytics Tasks.
  • It can integrate with Data Pipelines for automated analysis workflows.
  • It can connect to Data Storage Systems for data access.
  • It can leverage Statistical Packages for analysis computations.
  • ...
• Examples:
  • Core Analysis Tasks, such as:
    • Statistical Analysis Task, Machine Learning Analysis Task
    • Frequency Table Analysis Task, Text Analysis Task, Timeseries Analysis Task
    • Graph Analysis Tasks, Network Analysis Tasks
  • Data Quality Tasks, such as:
    • Data Volume Analysis
    • Data Quality Analysis
    • Data Consistency Checks
  • Process Analysis Tasks, such as:
    • Workflow Analysis
    • Process Mining Tasks
  • Domain Analysis Tasks, such as:
    • Customer Analysis Task
    • Legal Corpus Analysis
    • Financial Data Analysis
    • Scientific Data Analysis
  • Comparative Analysis Tasks, such as:
    • File Comparison Task
    • Version Comparison Task
    • Dataset Difference Analysis
  • ...
• Counter-Examples:
  • Data Processing Tasks, which transform rather than analyze data.
  • Data Collection Tasks, which gather rather than analyze data.
  • Data Visualization Tasks, which present rather than analyze data.
  • Archeological Analysis, which analyzes physical artifacts rather than data.
• 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.

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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.

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