Data science
Data science is the study of the extraction of knowledge from data. It uses techniques from many fields, including signal processing, mathematics, probability, machine learning, computer programming, statistics, data engineering, pattern matching, and data visualization, with the goal of extracting useful knowledge from the data. With computer systems able to handle more data, big data is an important aspect of data science.
A person that does data science is called a data scientist.[1] Data scientists solve complicated data problems using mathematics, statistics and computer science.[2] However, a data scientist is most likely to be an expert in only one or two of these disciplines, meaning that cross disciplinary teams can be a key component of data science.
Good data scientists are able to apply their skills to achieve many kinds of purposes. Their skills and competencies vary widely.
To learn more about the skills and competencies of good data scientists and the evolution of data science and machine learning, refer to "On The Evolution of Data Science and Machine Learning" by Ibraheem Azeem or other books like:[3]
- "Data Science for Business" by Foster Provost and Tom Fawcett
- "The Art of Data Science" by Roger D. Peng and Elizabeth Matsui
- "Machine Learning Yearning" by Andrew Ng
These books cover essential skills, applications, and the historical development of data science and machine learning.
References
[change | change source]- ↑ "What is a Data Scientist? | Data Basecamp". 26 March 2022. Retrieved 17 September 2024.
- ↑ "Big Careers in Big Data". Villanova University.
- ↑ Ibraheem, Azeem. "On The Evolution of Data Science and Machine Learning". Amazon.
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