Big Data and Data Science
What is Big Data?
Big data describes datasets whose size, rate of arrival, or complexity exceed the capability of a single machine or simple tools to store, process and analyze them efficiently.
Simple idea: Data that is too large, too fast, or too complex for a normal computer to handle easily.
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Example:
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Your laptop can analyze a few thousand rows in Excel.
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But can it handle all WhatsApp messages sent in a day or all YouTube videos uploaded in a week? → That’s big data.
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The 5 Vs of Big Data :
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Volume – huge amount of data (terabytes, petabytes).GB → TB → PB → EB
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Ex: Facebook stores billions of photos.
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Velocity – data comes in very fast,speed of generation and the need for low-latency processing
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Ex: Stock market prices update every second.
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Variety – different forms of data.heterogeneous formats: structured tables, semi-structured logs/JSON, text, images, audio, video, graphs.
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Ex: Text, video, images, sensor signals.
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Veracity – data may not be clean or reliable.
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Ex: Wrong entries, fake reviews.noise,, missingness
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Value – the useful information hidden inside.
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Ex: Netflix learns what movies to recommend from your watch history.
What is Data Science?
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Data Science is the process of turning raw data into useful insights.
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It combines:
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Statistics (understanding data patterns)
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Programming (Python, R, SQL, etc.)
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Domain knowledge (knowing the field like healthcare, business, or social media)
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Example:
From data about student attendance and marks, a data scientist can predict which students may need extra help
How is Big Data Related to Data Science?
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Big Data is the raw material.
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Data Science is the toolset to extract knowledge from it.
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Relation:
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Data Science asks questions (e.g., “Why are sales dropping?”).
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Big Data provides huge amounts of evidence (e.g., customer transactions, online clicks).
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Together → help make better decisions, predictions, and recommendations.
Why is Big Data Important in Data Science?
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Without big data, data science would only work on small, simple problems.
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Today’s real-world problems are big:
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Google search engine → processes billions of queries daily.
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Amazon → recommends products using millions of shopping records.
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Healthcare → analyzing MRI images and patient history for disease prediction.
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Tools and Technologies
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For small data: Python (pandas, scikit-learn).
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For big data: Spark, Hadoop, Dask (used in industries)
Sources & types of big data
Common sources you can show students:
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Web/server logs and click streams (user behavior analytics).
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Sensor / IoT telemetry (time-series at high frequency).
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Social media (text, images, metadata).
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Transactional systems / financial trades (high volume, low-latency).
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Scientific data (genomics, astronomy — very large files).
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Multimedia (video streams).
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Graphs (social networks, knowledge graphs).
Emphasize structure: whether data is tabular, nested, binary (images), or graph-shaped changes how you store and process it.
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