Learn to turn data into actionable insights with Predictive Analytics for Digital Transformation. This hands-on course equips you with Python skills, predictive modeling techniques, and analytics strategies to drive innovation and efficiency in digital transformation with Dartmouth Thayer School of Engineering faculty Vikrant Vaze and Reed Harder. What you'll learn: 1. Build Predictive Models Using Python: Gain hands-on experience with Scikit-learn to develop and refine regression and classification models, applying them to real-world scenarios. 2. Diagnose and Improve Model Performance: Identify issues like overfitting and underfitting, apply cross-validation, and select optimal features to ensure robust, generalizable results. 3. Leverage Advanced Techniques: Explore neural networks, regularization, and cloud-based tools to scale and optimize predictive analytics for complex business challenges. 4. Integrate Analytics into Decision-Making: Translate data-driven insights into actionable strategies to drive innovation and efficiency in digital transformation initiatives.
What is Predictive Analytics?
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Skills You'll Learn
Scikit Learn (Machine Learning Library), Regression Analysis, Predictive Analytics, Data-Driven Decision-Making, Classification And Regression Tree (CART), Business Ethics, Performance Metric, Data Quality, Artificial Neural Networks, Feature Engineering, Statistical Machine Learning, Applied Machine Learning, Cloud Computing, Digital Transformation, Python Programming
Taught By
Reed H. Harder
Course Facilitator
Vikrant S. Vaze
Associate Professor