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Introduction to Data Science: Building Recommender Systems

Course content updated by LearnQuest
2,495 USD
3 Days
Classroom Training, Online Training
Cloudera Training
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Course Description


Data scientists build information platforms to ask and answer previously unimaginable questions. Learn how data science helps companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities. Cloudera Universityâ??s three-day course helps participants understand what data scientists do and the problems they solve. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field.



  • The role of data scientists, vertical use cases, and business applications of data science
  • Where and how to acquire data, methods for evaluating source data, and data transformation and preparation
  • Types of statistics and analytical methods and their relationship
  • Machine learning fundamentals and breakthroughs, the importance of algorithms, and data as a platform
  • How to implement and manage recommenders using Apache Mahout and how to set up and evaluate data experiments
  • Steps for deploying new analytics projects to production and tips for working at scale
  • Audience






      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 Mahoutâ??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
      • Next Steps for Recommenders


        Appendix A : Hadoop Overview

          Appendix B: Mathematical Formulas

            Appendix C : Language and Tool Reference

            • HBase for Developers

              • Duration: 3 Days
              • Delivery Format: Classroom Training, Online Training
              • Price: 2,100.00 USD
            2020 Top 20 Training Industry Company - IT Training

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