数据分析实战班
Data Analysis Real Project
（更多资料和具体参加方法）
Advanced Data Analysis
This module covers the topics of Regression, Time Series and Clustering, which are widely used in prediction, forecasting and segmentation. It will be shown that how to do prediction, forecasting and how to organize observed data into meaningful structures through finding the relationship between several independent or predictor variables and a dependent or criterion variable.
• Simple Linear Regression (including diagnostics)
• Multiple Regression (including diagnostics and variable selection)
• Generalized Linear Models
• Logistic Regression
• Cluster Analysis.
• Time Series
Data Analysis Real Project
In the Real Project portion of the course, students will be using SAS and the analytical skills learned in the previous modules to work on real analytical projects. Using real data which presents real data issues and analytical challenges, following common work flow, students will earn real work experience and possess the competitive advantage in the job market.
• Finacial industry project
• Retail industry project
• Telecom industry project
Data Mining in Marketing
This course attempts to make a very complex process of data mining in marketing easy for the novice to understand. In a step-by -step process, it will tell students how we can benefit from modeling, and how to go about it. It provides an introduction and hands-on skills to data mining technologies as they apply to marketing. The substance of the course will be organized around Customer Relationship Management (CRM), the development of marketing programs that target the most profitable consumer segments. Students will learn the details of data mining techniques such as logistic regression, decision tree, artificial neural networks, and clustering methods using SAS/Stat and Enterprise Miner, and how they can be used to obtain information necessary to implement CRM. In short, the course will focus on the uses of data mining in marketing (including customer segmentation, profiling segments, penetration analysis, credit risk management, predictive and descriptive modeling, model validation and reporting) rather than data mining study. In addition, students will learn how marketing science concepts such as consideration sets, multi-attribute utility theory and customer segmentation can be used to direct and interpret data mining studies. Database Marketing process, model reporting and format of model log recommendation will also be introduced.
• Data Mining Methodology and Process
• Modeling Business Cases Through SAS Programming
• Modeling Business Cases Using SAS Enterprise Miner
• Applications of Data Mining to Industries
• Model Monitoring and Management
（更多资料和具体参加方法）
（更多资料和具体参加方法）
Advanced Data Analysis
This module covers the topics of Regression, Time Series and Clustering, which are widely used in prediction, forecasting and segmentation. It will be shown that how to do prediction, forecasting and how to organize observed data into meaningful structures through finding the relationship between several independent or predictor variables and a dependent or criterion variable.
• Simple Linear Regression (including diagnostics)
• Multiple Regression (including diagnostics and variable selection)
• Generalized Linear Models
• Logistic Regression
• Cluster Analysis.
• Time Series
Data Analysis Real Project
In the Real Project portion of the course, students will be using SAS and the analytical skills learned in the previous modules to work on real analytical projects. Using real data which presents real data issues and analytical challenges, following common work flow, students will earn real work experience and possess the competitive advantage in the job market.
• Finacial industry project
• Retail industry project
• Telecom industry project
Data Mining in Marketing
This course attempts to make a very complex process of data mining in marketing easy for the novice to understand. In a step-by -step process, it will tell students how we can benefit from modeling, and how to go about it. It provides an introduction and hands-on skills to data mining technologies as they apply to marketing. The substance of the course will be organized around Customer Relationship Management (CRM), the development of marketing programs that target the most profitable consumer segments. Students will learn the details of data mining techniques such as logistic regression, decision tree, artificial neural networks, and clustering methods using SAS/Stat and Enterprise Miner, and how they can be used to obtain information necessary to implement CRM. In short, the course will focus on the uses of data mining in marketing (including customer segmentation, profiling segments, penetration analysis, credit risk management, predictive and descriptive modeling, model validation and reporting) rather than data mining study. In addition, students will learn how marketing science concepts such as consideration sets, multi-attribute utility theory and customer segmentation can be used to direct and interpret data mining studies. Database Marketing process, model reporting and format of model log recommendation will also be introduced.
• Data Mining Methodology and Process
• Modeling Business Cases Through SAS Programming
• Modeling Business Cases Using SAS Enterprise Miner
• Applications of Data Mining to Industries
• Model Monitoring and Management
（更多资料和具体参加方法）
（更多资料和具体参加方法）
