13. Applications of Data Science
Data Science transforms raw data into meaningful insights and plays a crucial role across various sectors such as healthcare, education, e-commerce, and entertainment. The chapter outlines the fundamental components of Data Science, its importance, practical applications, the role of Data Scientists, and the tools required to effectively manage and analyze data. Additionally, it addresses the challenges faced in the field, highlighting the growing demand for data-driven decision-making in contemporary society.
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What we have learnt
- Data Science converts raw data into insights for decision-making.
- It is applied in numerous fields including healthcare, agriculture, and finance.
- Key skills and tools for Data Scientists include programming, data visualization, and machine learning.
Key Concepts
- -- Data Collection
- The process of gathering raw data from various sources to be analyzed.
- -- Data Cleaning
- The method of correcting or removing inaccurate records from a dataset.
- -- Machine Learning
- A branch of artificial intelligence that uses algorithms to allow computers to learn from data and make predictions.
- -- Data Visualization
- The graphical representation of information and data to help communicate insights clearly.
- -- Predictive Analytics
- The use of statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
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