What is big data in analytics?

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Looking for the best Data Science training in Hyderabad? Quality Thought offers industry-focused Data Science training designed to help professionals and freshers master machine learning, AI, big data analytics, and data visualization. Our expert-led course provides hands-on training with real-world projects, ensuring you gain in-depth knowledge of Python, R, SQL, statistics, and advanced analytics techniques.

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.

Big Data in analytics refers to extremely large and complex datasets that are too big for traditional data-processing tools to handle efficiently — but when analyzed, they reveal patterns, trends, and insights that help in decision-making.

Key Characteristics — The “3 Vs” (often expanded to 5 Vs)

  1. Volume – Massive amounts of data (terabytes, petabytes, or more).

  2. Velocity – Data generated and processed at high speed (e.g., social media streams, IoT sensors).

  3. Variety – Many data types: text, images, videos, audio, logs, transactions, etc.

  4. Veracity (extra V) – The reliability and quality of the data.

  5. Value (extra V) – The usefulness of the data for decision-making.

Sources of Big Data

  • Social media platforms (posts, likes, comments)

  • IoT devices and sensors

  • Online transactions and e-commerce logs

  • Healthcare records and medical imaging

  • Satellite and weather data

Why It Matters in Analytics

  • Enables predictive analysis (forecasting trends).

  • Supports real-time decision-making (fraud detection, traffic routing).

  • Helps personalize experiences (recommendation systems like Netflix, Amazon).

  • Improves operational efficiency by finding hidden patterns.

In short: Big Data is all about handling massive, fast-moving, and varied data sources to uncover insights that would be impossible to see with smaller datasets and traditional tools.

Read More

 How does machine learning work?

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