12. Introduction to Data Science
Data science is a pivotal field that combines statistics, computer science, and domain knowledge to glean insights from data. The data science lifecycle guides the process from problem definition to model monitoring. Various tools and applications span multiple industries, emphasizing the significance of ethical considerations in data handling.
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What we have learnt
- Data Science transforms raw data into meaningful insights.
- The Data Science Lifecycle includes problem identification, data collection, cleaning, analysis, model building, evaluation, deployment, and monitoring.
- Python, R, and specific libraries are integral tools utilized in data science.
- Data science plays a crucial role in numerous fields, from healthcare to finance.
- Adhering to ethical standards is vital for responsible data science practices.
Key Concepts
- -- Data Science
- An interdisciplinary field that uses statistics, computer science, and domain expertise to extract meaningful insights from structured and unstructured data.
- -- Data Science Lifecycle
- A structured approach followed in data science projects encompassing stages from problem definition to monitoring.
- -- Model
- A mathematical representation trained on data to make predictions.
- -- Algorithm
- A procedure or method used to perform a task such as prediction.
- -- Visualization
- Graphical representations of data for easier understanding and insights.
Additional Learning Materials
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