Data science
Definition
Data science involves extracting insights and knowledge from data using various scientific methods, algorithms, and systems. It combines aspects of statistics, computer science, and domain expertise to analyze complex datasets.
5 Must Know Facts For Your Next Test
- Data science uses programming languages like Python for data manipulation and analysis.
- It encompasses several stages including data collection, cleaning, exploration, modeling, and interpretation.
- Popular libraries in Python for data science include Pandas, NumPy, Matplotlib, and Scikit-learn.
- Machine learning is a crucial component of data science that focuses on building predictive models.
- Understanding statistical concepts is essential for making sense of the patterns and trends in the data.
Review Questions
- What are the main stages involved in a data science workflow?
- Name at least three Python libraries commonly used in data science.
- How does machine learning relate to data science?
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Related terms
Machine Learning: A subset of artificial intelligence that uses statistical techniques to enable computers to learn from and make predictions based on data.
Pandas: A Python library used for data manipulation and analysis, particularly well-suited for working with structured data.
NumPy: A fundamental package for scientific computing with Python that provides support for large multi-dimensional arrays and matrices.
