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Data Science is the science of data — how to gather it, manage it, analyze it, and utilize it in order to make sounder decisions.
It is a convergence of statistics, computer science, and domain expertise to obtain useful insights from unprocessed data.
In Simple Terms:
Data Science is all about transforming raw data into actionable information that gets to the bottom of real-world problems.
Example:
Netflix uses Data Science to recommend shows based on what you’ve watched.
Banks use it to detect fraud and assess credit risk.
Businesses use it to predict sales and understand customer behavior.

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Key Components of Data Science:
Data Collection – Gathering data from various sources (websites, sensors, databases, etc.)
Data Cleaning – Removing errors or missing values from data.
Data Analysis – Using statistics and tools to find patterns and trends.
Data Visualization – Displaying insights in the form of charts and graphs.
Machine Learning – Applying algorithms to make predictions or automate choice.
Disciplines Merged in Data Science:
Mathematics & Statistics → for analysis and modeling
Computer Science & Programming → for processing and handling big data
Domain Expertise → problem area knowledge (such as finance, healthcare, etc.)
In Brief:
Data Science = Data + Statistics + Machine Learning + Business Knowledge
It enables organizations to take data-driven decisions, forecast future trends, and innovate wiser solutions.
Would you also like me to provide a brief 3–4 line definition for exams or interviews as well?

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