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Create strategies for management to reduce employee attrition. This week is designed to integrate critical thinking and strategy development with data-driven decision making.
Get the Ultimate Python Cheat Sheet that makes learning data science with Python quick and efficient.
This week will extend what you learned from the Expected Value by performing an optimization and sensitivity analysis.
Learn everything you need to know about the Expected Value Framework. The Expected Value Framework is way to apply an expected value to a classification model - it connects a machine learning classification model to ROI for the business.
Learn how to explain machine learning models with LIME. LIME stands for Local Interpretable Model-Agnostic Explanations and is used to understand which model features have the most predictive impact.
This article uses a Kaggle competition as an opportunity to show how data science can be used in digital marketing to answer a specific question, and take what is learned from the data and apply it to marketing strategies.
In Data Science With R Course Series - Week 6, learn how to analyze model performance and communicate machine learning model results to stakeholders.
The culmination of the previous weeks has been preparation for machine learning modeling. At this stage, we have defined the business problem, we’ve explored and understood how the data relates to the business problem, and we’ve preprocessed the data in preparation for modeling.
This week in the Data Science With R Course Series we’ll cover Data Preparation, where we structure the data in preparation for modeling. This week’s modules will teach you: