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1. Cloudera Introduction to Data Science: Building Re
   ? Cloudera Introduction to Data Science: Building Recommender Systems培訓(xùn)
   培訓(xùn)大綱
   1. Data Science?
   What is Data Science??
   Growing Need for Data Science?
   Role of a Data Scientist?
   2. Use Cases?
   Finance?
   Retail?
   Advertising?
   Defense and Intelligence?
   Telecommunications and Utilities?
   Healthcare and Pharmaceuticals?
   3. Project Life Cycle?
   Steps in the Project Life Cycle?
   4. Data Acquisition?
   Where to Source Data?
   Acquisition Techniques?
   Evaluating Input Data?
   Data Formats?
   Data Quantity?
   Data Quality?
   5. Data Transformation?
   Anonymization?
   File Format Conversion?
   Joining Datasets?
   6. Data Analysis and Statistical Methods?
   Relationship Between Statistics and Probability?
   Descriptive Statistics?
   Inferential Statistics?
   7. Fundamentals of Machine Learning?
   Three Cs of Machine Learning?
   Spotlight: Na?ve Bayes Classifiers?
   Importance of Data and Algorithms?
   8. Recommender?
   What is a Recommender System??
   Types of Collaborative Filtering?
   Limitations of Recommender?
   9. Systems Fundamental Concepts?
   10. Apache Mahout?
   What Apache Mahout is (and is not)?
   History of Mahout?
   Availability and Installation?
   Demonstration: Using Mahout's Item-Based Recommender?
   11. Implementing Recommenders with Apache Mahout?
   Similarity Metrics for Binary Preferences?
   Similarity Metrics for Numeric Preferences?
   Scoring?
   12. Experimentation and Evaluation?
   Measuring Recommender Effectiveness?
   Designing Effective Experiments?
   Conducting an Effective Experiment?
   User Interfaces for Recommenders?
   13. Production Deployment and Beyond?
   Deploying to Production?
   Tips and Techniques for Working at Scale?
   Summarizing and Visualizing Results?
   Considerations for Improvement?
   Next Steps for Recommenders?
   14. Appendix A: Hadoop?
   15. Appendix B: Mathematical Formulas?
   16. Appendix C: Language and Tool Reference
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