Introduction Data Mining for Business Analytics (3rd ed.)

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Presentation transcript:

Introduction Data Mining for Business Analytics (3rd ed.) Shmueli, Bruce & Patel © Galit Shmueli and Peter Bruce 2010

Core Ideas in Data Mining Business Analytics Data Mining Related Terms Big Data Data Science So many different methods

Business Analytics Practice and art of bringing quantitative data to bear on decision making Visualization / Business Intelligence Understanding Predicting Optimizing

Credit scoring Future purchases Tax evasion Card fraud Wafer quality

Data Mining Beyond Statistics + Machine Learning Counts Descriptive techniques Reporting Methods based on business rules Statistics + Machine Learning

Related Terms Machine Learning Pattern Recognition Computer vision Artificial Intelligence

Related Terms 인공지능 AI (1946~) 빅데이터 (2000~) 머신 러닝 (1956, 1986, 2005~) IoT (2010~)

Related Terms Machine Learning vs Statistics Theory rich, data poor vs Data rich, theory poor Distribution assumption Mathematical rigor (management science)

Related Terms Machine Learning vs Statistics Neural networks, Deep Learning vs (Nonlinear) Regression

Related Terms Machine Learning vs Statistics Understanding vs Predicting What has happened? vs What will happen? Descriptive vs Predictive

Related Terms Machine Learning vs Statistics Population through sample vs population Overall behavior vs individual behavior Preventive vs predictive maintenance

Data Science A mix of skills in Statistics Machine learning Mathematics Programming Business IT Database, SQL Operating system Data manipulation

So many methods Pros and cons No champion

Who’s on?

Who’s who?

Data  Insight  Value 분석 Data Miner 액션 Decision Makers 데이터마이닝 인공지능/머신러닝 액션 Decision Makers 엔지니어 마케터, 투자자, 인사관리

Value  Insight  Data 기획

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