Data Science Task

A Data Science Task is a science task that optimizes data-driven decision-making tasks.

• Context:
  • It can be solved by a Data Science System.
  • It can be designed by a Data Scientist.
  • It can (often) involve A-B Testing, Data Mining, Predictive Modeling, ...
  • It can be analyzed by a Data Science Discipline.
  • …
• Example(s):
  • “Prototype a pCTR() predictive model. What performance measure should we use?”
  • “How do we evaluate the impact of this change on revenue? Could we use a cross-over experiment?”
  • Data Mining Task.
  • …
• Counter-Example(s):
  • a Data Engineering Task.
  • a Data Modeling Task.
  • a Software Programming Task.
• See: Machine Learning Task, Statistical Analysis Task, Business Intelligence Task.

---

---

References

2023

• chat
  • Q: What is the difference between data science and data mining?
  • A: Data science is a broad term that encompasses many aspects of processing and analyzing data. It includes data mining, but also includes machine learning, predictive analytics, statistics, and more. Data mining is a specific subdomain of data science that focuses on extracting data from large datasets. It is the process of finding patterns and trends from large datasets using algorithms and statistical models.

2021

• (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/data_science Retrieved:2021-2-22.
  • Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology” and the data deluge.

---