Data science life cycle
Definition
The data science life cycle is a series of iterative steps used to analyze and interpret complex data. It typically includes stages like data collection, cleaning, exploration, modeling, and interpretation.
5 Must Know Facts For Your Next Test
- The main stages in the data science life cycle include: Data Collection, Data Cleaning, Data Exploration, Data Modeling, and Data Interpretation.
- Data Cleaning often involves handling missing values and removing outliers to ensure data quality.
- Exploratory Data Analysis (EDA) is crucial for understanding patterns and relationships within the dataset.
- Modeling involves selecting appropriate algorithms to make predictions or classify data based on historical information.
- Interpretation of results should provide actionable insights that inform decision-making processes.
Review Questions
- What are the main stages of the data science life cycle?
- Why is Exploratory Data Analysis important in the data science life cycle?
- How does data cleaning impact the quality of analysis in a data science project?
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Related terms
Exploratory Data Analysis (EDA): A technique used to analyze datasets to summarize their main characteristics often with visual methods.
Data Cleaning: The process of detecting and correcting inaccurate records from a dataset.
Data Modeling: The phase where mathematical models are applied to historical data to make predictions or classifications.
