Data Science Foundations: Data Assessment for Predictive Modeling
With Keith McCormick
Liked by 352 users
Duration: 4h 3m
Skill level: Intermediate
Released: 9/11/2020
Course details
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.
Skills you’ll gain
Meet the instructor
Learner reviews
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Khaled M. Ali Aklan Albanna
Khaled M. Ali Aklan Albanna
IT Advisor | AI (Expert Systems & Data Science) | Banking and ERP Systems
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Zunaid Mohidin
Zunaid Mohidin
Global Consultant | Executive Coach | Digital & AI Transformation | Technology Risk & Compliance | Innovation Catalyst | MBA in Digital Business
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Contents
What’s included
- Practice while you learn 1 exercise file
- Learn on the go Access on tablet and phone
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