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Cloudera Introduction to Data Science: Building Recommender Systems (CIDS)

Course Description Schedule Course Outline

Detailed Course Outline

  • Data Science Overview
    • What Is Data Science?
    • The Growing Need for Data Science
    • The Role of a Data Scientist
  • Use Cases
    • Finance
    • Retail
    • Advertising
    • Defense and Intelligence
    • Telecommunications and Utilities
    • Healthcare and Pharmaceuticals
  • Project Lifecycle
    • Steps in the Project Lifecycle
    • Lab Scenario Explanation
  • Data Acquisition
    • Where to Source Data
    • Acquisition Techniques
    • Evaluating Input Data
    • Data Formats
    • Data Quantity
    • Data Quality
  • Data Transformation
    • Anonymization
    • File Format Conversion
    • Joining Datasets
  • Data Analysis and Statistical Methods
    • Relationship Between Statistics and Probability
    • Descriptive Statistics
    • Inferential Statistics
  • Fundamentals of Machine Learning
    • Overview
    • The Three Cs of Machine Learning
    • Spotlight: Naïve Bayes Classifiers
    • Importance of Data and Algorithms
  • Recommender Overview
    • What Is a Recommender System?
    • Types of Collaborative Filtering
    • Limitations of Recommender
  • Systems Fundamental Concepts
  • Introduction to Apache Mahout
    • What Apache Mahout Is (and Is Not)
    • A Brief History of Mahout
    • Availability and Installation
    • Demonstration: Using Mahout’s Item-Based Recommender
  • Implementing Recommenders with Apache Mahout
    • Overview
    • Similarity Metrics for Binary Preferences
    • Similarity Metrics for Numeric Preferences
    • Scoring
  • Experimentation and Evaluation
    • Measuring Recommender Effectiveness
    • Designing Effective Experiments
    • Conducting an Effective Experiment
    • User Interfaces for Recommenders
  • Production Deployment and Beyond
    • Deploying to Production
    • Tips and Techniques for Working at Scale
    • Summarizing and Visualizing Results
    • Considerations for Improvement
    • ext Steps for Recommenders
  • Appendix A: Hadoop Overview
  • Appendix B: Mathematical Formulas
  • Appendix C: Language and Tool Reference
 

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