Data Science Practice

A Data Science Practice is an applied practice that is focused on solving real-world data science tasks.

• AKA: Data Mining Applied Practice.
• Context:
  • It can be performed by a Data Mining Practitioner.
  • It can overlap with:
    • a Machine Learning Practice/
    • a Statistics Practice.
  • It has a Data Mining Terminology that can be partially represented in a Data Mining Glossary.
  • …
• Counter-Example(s):
  • a Data Mining Academic Discipline.
• See: Data Mining Subject Area.

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References

2009

• http://searchsqlserver.techtarget.com/sDefinition/0,,sid87_gci211901,00.html
  • DEFINITION - Data mining is sorting through data to identify patterns and establish relationships.
  • Data mining parameters include:
    • Association - looking for patterns where one event is connected to another event
    • Sequence or path analysis - looking for patterns where one event leads to another later event
    • Classification - looking for new patterns (May result in a change in the way the data is organized but that's ok)
    • Clustering - finding and visually documenting groups of facts not previously known
    • Forecasting - discovering patterns in data that can lead to reasonable predictions about the future (This area of data mining is known as predictive analytics.)
• http://www.twocrows.com/glossary.htm
  • data mining: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis.
• Quotes of usage
  • A working definition of data mining is the discovery of interesting, unexpected, or valuable structures in large datasets.
  • Data mining is the discovery of interesting, unexpected or valuable structures in large datasets.
  • We examine how data mining is used and outline some of its methods.
  • Data mining is defined as the identification of interesting structure in data.
  • On the data-mining front we have ...
  • The recession has boosted the importance of 'data mining as more businesses search for clues to increase revenues and decrease expenses.
  • How Europeans Are Using Data Mining
  • Using data mining to find out the most vulnerable
  • This area of data mining is known as predictive analytics.
  • … the basis of data mining is to compress the given data by ...
  • The promise of data mining is compelling, and convinces many.
  • The goal of data mining is to extract ...
  • Much of data mining is about leveraging existing data to make useful predictions.
  • The third family line of data mining is machine learning, which …

2000

• (Han & Kamber, 2000) ⇒ Jiawei Han, and Micheline Kamber. (2000). “Data Mining: Concepts and Techniques, 1st ed." Morgan Kaufmann. ISBN:1558604898

1999

• (Sukumar, 1999) ⇒ Rajagopal Sukumar. (1999). “Data Mining." Overview Presentation.

1998

• (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
  • Data mining: The term data mining is somewhat overloaded. It sometimes refers to the whole process of knowledge discovery and sometimes to the specific machine learning phase.

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