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踦 ߰ų ̼Ͽ ǹ Ϸ ֽϱ? ͺ̽ 100% Ȱ ϰ ʴϱ? ̴ ý ϰ ʴϱ? м ݿ ϰ ֽϱ? м  ϰ ʴϱ?  ο м ̴ ΰ ϰ ʴϱ?

Data Mining м Ʈ ȹ м process ϰ ü м ʺм ޱ ʿ Tip, ħ մϴ. Data mining м Ͽ CRM, , ܰ ø ʽÿ.


  • м Ұ ƴ Ʈ ȹ ޱ ϰ м
  • м ؼ
  • ̴ پ м ʺ ޱ Ұ
  • ſ, Ż, ķ Ÿ, ȭ B2C Ǵ м
  • Ͻ м ̽ ذ Ұϴ ÿ м Skill ֵ SAS Enterprise Miner Hands-On ǽ
  • м Ȱ ü



  • ̳
  • ͸ ϴ м
  • OLAP Ǵ 踦 Ͽ м ۼϴ , CRM, , ͺ̽ μ ǹ
  • CRM Ǵ ι Manager


[Day 1]
Session α
⺻ [1H] м ⺻ м
- ̴ Ұ

- ľ м
ʺм [4H] ó
EDA
- ڷغ
- data integration data cleaning
- ġ ó
- ڷ Ž
- ̻ġ Ž ó
- dimension reduction (principal component analysis, variable clustering)
- Data Transformation/ȭ
ǽ [1H] SAS/Insight SAS Enterprise Miner ̿ ǽ
- ó ǽ
- ̻ġ ġ ó
- EDA ؼ ó

[Day 2]
Session α
𵨸 [4H] Clustering Techniques
- clustering
- Basic Techniques Overview(hierarchical clustering, nonhierarchical clustering)
- hierarchical clustering
- nonhierarchical clustering (k-means, PAM)
- Clustering Issues & Tips
- ؼ ̿
ǽ [1H] SAS/STAT ̿ Clustering ǽ
- ׷ ̿ clustering Ž
- hierarchical nonhierarchical clustering
- cluster
- Clustering ̿ Ļ
𵨸 [1H] Predictive Modeling Techniques
- Modeling Algorithm (Regression, Decision Tree, Neural Network, GAM )
- Predictive Modeling Issues & Tips

[Day 3]
Session α
𵨸 [3H] Predictive Modeling Techniques
- Decision tree
- GAM(Generalized additive model)
- regression
- Ű
- Ensemble Modeling (bagging)
-
ǽ [1H] SAS/STAT, SAS Enterprise Miner ̿ Predictive Modeling ǽ
- Decision tree, GAM ̿ Ļ
- bagging ̿
-
𵨸 [1H] м
- Basic Techniques Overview (Association Rules)
- Association Rules Issues & Tips
- Model ؼ
ǽ [1H] SAS Enterprise Miner
̿ ǽ
- ٱ м 𵨸/

- Ģ ϸ
- м 𵨸



  • Ϲ: û (SAS Korea Ȩ/ȭ/e-mail) Ϸ
  • : 940,000 (VAT 10% )
    (: 344-01-200250 : ѱؽƮ)
  • : SAS Korea ̶
  • ȭ: 02-2191-7002, 7018 / e-mail: kor.educ@sas.com

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