This list provides a structured learning path for SAS Data Mining, from fundamental concepts to advanced techniques and applications.
I. SAS Data Mining Fundamentals (1-20)
- Welcome to SAS Data Mining: Uncovering Insights from Data
- Introduction to Data Mining and its Applications
- Understanding the Data Mining Process (CRISP-DM)
- Introduction to SAS for Data Mining
- Setting up your SAS Environment for Data Mining
- Working with SAS Libraries and Datasets
- Data Exploration and Descriptive Statistics in SAS
- Data Visualization with SAS: Exploring Data Patterns
- Data Preprocessing Techniques in SAS: Cleaning and Transformation
- Handling Missing Values in SAS Datasets
- Data Integration and Transformation with SAS
- Introduction to SAS Enterprise Miner
- Navigating the Enterprise Miner Interface
- Creating a Data Mining Project in Enterprise Miner
- Importing and Managing Data in Enterprise Miner
- Exploring Data with Enterprise Miner
- Building a Simple Data Mining Model in Enterprise Miner
- Evaluating Model Performance in Enterprise Miner
- Deploying Data Mining Models from Enterprise Miner
- Introduction to SAS Programming for Data Mining
II. Statistical Modeling Techniques (21-40)
- Linear Regression in SAS: Predicting Continuous Variables
- Logistic Regression in SAS: Predicting Categorical Variables
- Building Regression Models in Enterprise Miner
- Model Diagnostics and Assessment for Regression
- ANOVA and ANCOVA in SAS: Comparing Groups
- Time Series Analysis and Forecasting in SAS
- Building Time Series Models in Enterprise Miner
- Survival Analysis in SAS: Analyzing Time-to-Event Data
- Building Survival Models in Enterprise Miner
- Introduction to Clustering Techniques
- K-Means Clustering in SAS: Grouping Similar Data Points
- Hierarchical Clustering in SAS: Building a Hierarchy of Clusters
- Clustering with Enterprise Miner
- Evaluating Clustering Performance
- Introduction to Association Rule Mining
- Apriori Algorithm for Association Rule Mining
- Association Rule Mining with Enterprise Miner
- Evaluating Association Rules
- Introduction to Decision Trees
- Building Decision Trees in SAS: CART and C4.5
III. Advanced Modeling Techniques (41-60)
- Decision Tree Implementation in Enterprise Miner
- Random Forests in SAS: Ensemble Learning
- Building Random Forest Models in Enterprise Miner
- Gradient Boosting Machines (GBM) in SAS
- Building GBM Models in Enterprise Miner
- Neural Networks in SAS: Deep Learning
- Building Neural Network Models in Enterprise Miner
- Support Vector Machines (SVM) in SAS
- Building SVM Models in Enterprise Miner
- Model Comparison and Selection Techniques
- Ensemble Modeling: Combining Multiple Models
- Advanced Feature Engineering Techniques
- Feature Selection and Dimensionality Reduction
- Working with Imbalanced Datasets
- Handling High-Dimensional Data
- Text Mining with SAS: Analyzing Text Data
- Sentiment Analysis with SAS
- Building Text Mining Models in Enterprise Miner
- Image Analytics with SAS: Analyzing Image Data
- Building Image Analysis Models in Enterprise Miner
IV. Model Deployment and Evaluation (61-80)
- Model Deployment Strategies in SAS
- Scoring New Data with SAS Models
- Integrating SAS Models with Business Applications
- Model Monitoring and Maintenance
- Performance Monitoring of Deployed Models
- Model Retraining and Updating
- Building a Scoring System with SAS
- Generating Reports and Visualizations from SAS Models
- Communicating Data Mining Results to Stakeholders
- Data Mining Project Management
- Building a Data Mining Team
- Ethical Considerations in Data Mining
- Data Privacy and Security in Data Mining
- Legal and Regulatory Compliance for Data Mining
- SAS Macro Language for Data Mining Automation
- Building Custom SAS Procedures for Data Mining
- Using SAS/IML for Advanced Statistical Modeling
- Integrating SAS with Other Data Mining Tools
- Cloud-Based SAS for Data Mining
- Big Data Analytics with SAS
V. Specialized Topics and Applications (81-100)
- Data Mining for Customer Relationship Management (CRM)
- Data Mining for Marketing Analytics
- Data Mining for Fraud Detection
- Data Mining for Risk Management
- Data Mining for Healthcare Analytics
- Data Mining for Financial Modeling
- Data Mining for Supply Chain Optimization
- Data Mining for Social Media Analytics
- Data Mining for Web Analytics
- Data Mining for Internet of Things (IoT) Data
- Time Series Forecasting with Advanced Techniques
- Spatial Data Mining with SAS
- Building Recommender Systems with SAS
- Deep Learning with SAS: Advanced Architectures
- Reinforcement Learning with SAS
- Bayesian Data Mining with SAS
- Data Mining Case Studies and Best Practices
- Emerging Trends in Data Mining
- The Future of Data Mining with SAS
- Building a Career in SAS Data Mining