Data Science Discipline

A data science discipline is a scientific discipline that explores data science subject area to create data mining knowledge.

• AKA: Data Mining Field.
• Context:
  • It must include:
    • data mining academic institutions.
    • data mining research acts, that can produce Data Mining Algorithms and reported at a Data Mining Conference or Data Mining Journal.
    • data mining education acts, that can produces Data Mining Researchers and Data Mining Practitioners.
  • It can support a Data Mining Practice.
  • It can advance a Data Science Concepts (represented in data science terminology).
  • It can develop prototype data mining systems, such as SVMlight, MALLET, and Weka.
  • It can be related to: a Statistics Discipline, a Machine Learning Discipline.
  • …
• Counter-Example(s):
  • an Engineering Discipline.
• See: Data Mining Algorithm, Data Mining System.

---

---

References

2009

• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Data_mining
  • Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every three years,[1] data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.

2001

• (Hand et al., 2001) ⇒ David J. Hand, Heikki Mannila, and Padhraic Smyth. (2001). “Principles of Data Mining." MIT Press. ISBN:026208290X
  • Data mining is fundamentally an applied discipline, and with this in mind we make frequent references to case studies and specific applications where the basic theory can (or has been) applied.

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.

---