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Data Science at Scale using Spark and Hadoop Overview

  • Introduction

    • About This Course

    • About Cloudera

    • Course Logistics

    • Introductions

  • 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

    • File Format Conversion

    • Joining Data Sets

    • Anonymization

  • Data Analysis and Statistical Methods

    • Relationship Between Statistics and Probability

    • Descriptive Statistics

    • Inferential Statistics

    • Vectors and Matrices

  • Fundamentals of Machine Learning

    • Overview

    • The Three C’s of Machine Learning

    • Importance of Data and Algorithms

    • Spotlight: Naive Bayes Classifiers

  • Recommender Overview

    • What is a Recommender System?

    • Types of Collaborative Filtering

    • Limitations of Recommender Systems

    • Fundamental Concepts

  • Introduction to Apache Spark and MLlib

    • What is Apache Spark?

    • Comparison to MapReduce

    • Fundamentals of Apache Spark

    • Spark’s MLlib Package

  • Implementing Recommenders with MLlib

    • Overview of ALS Method for Latent Factor Recommenders

    • Hyperparameters for ALS Recommenders

    • Building a Recommender in MLlib

    • Tuning Hyperparameters

    • Weighting

  • Experimentation and Evaluation

    • 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

  • Conclusion


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