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Data Science Foundations: Data Assessment for Predictive Modeling
Data Science Foundations: Data Assessment for Predictive Modeling
CRISP-DM, the cross-industry standard process for data mining, is composed of six phases. Most new data scientists rush to modeling because it's the phase in which they have the most training. But whether the project succeeds or fails is actually determined far earlier. This course introduces a systematic approach to the data understanding phase for predictive modeling. Instructor Keith McCormick teaches principles, guidelines, and tools, such as KNIME and R, to properly assess a data set for its suitability for machine learning. Discover how to collect data, describe data, explore data by running bivariate visualizations, and verify your data quality, as well as make the transition to the data preparation phase. The course includes case studies and best practices, as well as challenge and solution sets for enhanced knowledge retention. By the end, you should have the skills you need to pay proper attention to this vital phase of all successful data science projects.


  • Introduction
  • 1. What Is Data Assessment?
  • 2. Collect Initial Data
  • 3. First Look at the Data
  • 4. Data Loading and Unit of Analysis
  • 5. Describe Data
  • 6. Data Description Case Studies
  • 7. Explore Data Basics
  • 8. Explore Data Tips and Tricks
  • 9. Verify Data Quality
  • 10. Missing Data Case Study
  • 11. Explore and Verify Case Studies
  • 12. Making the Transition to Data Preparation
  • Conclusion


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