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Data is unprocessed information, while processed data is called “information". It’s often referred to as the “new oil” of
the 21st century. Interestingly, 90% of the world's data has been created in just the last two years, compared to the
previous six million years of human existence.
Data science involves studying data to extract valuable insights for businesses. This multidisciplinary field merges
principles and techniques from mathematics, statistics, artificial intelligence, computer engineering, programming,
and analytics to analyse large datasets effectively.
Data science involves analysing and interpreting complex data to make informed decisions, while AI focuses on creating
machines or systems that can perform tasks requiring human intelligence. Each has its strengths and is essential in
different areas of technology and research. Data Science is an interdisciplinary field that combines techniques from
statistics, computer science, and domain expertise to extract valuable insights from data. Data Science is not limited
to AI, but it plays a vital role in the AI landscape. Data Science is crucial for AI because it provides the data required
for training and testing AI models. It helps organisations make data-driven decisions and predictions.
Advantages of Data Science
Data science offers numerous advantages. Some of these are:
• Informed Decision-Making: Data science enables organisations to make data-driven decisions by providing
actionable insights, and identifying trends and patterns in data.
• Predictive Analytics: It helps predict future outcomes based on historical data, allowing businesses to anticipate
changes and plan accordingly.
• Improved Business Operations: By analysing operational data, businesses can streamline processes, reduce costs,
and enhance efficiency.
Applications of Data Science
Data Science has proved its mantle in various fields such as:
• Healthcare Analytics: Data science is used to analyse medical data to improve patient care, predict disease outbreaks,
and optimise treatment plans.
• Fraud Detection: It helps identify fraudulent activities by detecting anomalies in transaction data, and enhancing
security for financial institutions.
• Customer Segmentation: Businesses use data science to segment customers based on their behaviour and
preferences, enabling targeted marketing strategies.
• Financial Forecasting: Data science models help predict financial trends and market movements, thereby assisting
in investment decisions and risk management.
• Recommender Systems: E-commerce and streaming services use data science to analyse user preferences and
provide personalised recommendations, thus, improving user experience.
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