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Beginner Topic 17 of 51 Emerging & Frontier Topics

Data Mining

Discovering patterns in large datasets.

How it works

Data mining discovers useful patterns in large datasets. Typical tasks are classification and regression to predict labels or values, clustering to group similar items, association rule mining such as market-basket analysis using support and confidence, anomaly detection for outliers and frequent pattern and sequence mining. Methods include decision trees, k-means, Apriori and neural networks. The process cleans and transforms data, applies algorithms, evaluates patterns for validity and usefulness and interprets them with domain experts. It overlaps with statistics, machine learning and databases.

Where it is used

Retailers find products bought together, banks detect fraud and telecoms predict customer churn.

Common pitfall

Searching enough data always turns up patterns by chance, so spurious findings occur, correlation is not causation and privacy can be violated by combining records. Validate on held-out data, correct for multiple testing and anonymize data.

Where it fits in the course

Data Mining is topic 17 of 51 in Emerging & Frontier Topics, rated Beginner. It follows Big Data. The next topic is Blockchain.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the Emerging & Frontier Topics quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is Data Mining?
Discovering patterns in large datasets. It belongs to Emerging & Frontier Topics and is a beginner-level topic.
Where do people use Data Mining?
Retailers find products bought together, banks detect fraud and telecoms predict customer churn.
What is the most common mistake with Data Mining?
Searching enough data always turns up patterns by chance, so spurious findings occur, correlation is not causation and privacy can be violated by combining records.
What should I learn before this topic?
Start with Big Data, the topic just before it in Emerging & Frontier Topics.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Emerging & Frontier Topics quiz.
Is Data Mining hard to learn?
It is a Beginner topic, so it works as an entry point. If the terms are new, read the explanation twice and try the visualizer before the quiz.
How long does it take to learn Data Mining?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Data Mining?
Big Data, Blockchain, Data Visualization are closely connected. The full list is in the related topics section of this page.
What should I study after Data Mining?
Continue with Blockchain, the next topic in Emerging & Frontier Topics.

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