Predictive Analytics

Welcome to Thayer School of Engineering at Dartmouth’s Predictive Analytics for Digital Transformation. This course equips you with the tools and knowledge to turn raw data into actionable insights, helping you lead data-driven innovation in your field. Whether you aim to enhance organizational efficiency, improve customer experiences, or drive transformative solutions, this course provides a solid foundation in predictive analytics techniques. You’ll start with essential linear and logistic regression methods and progress to advanced modeling techniques that solve real-world business challenges. Using Python and cloud-based tools, you’ll gain hands-on experience building, training, and evaluating predictive models. The curriculum covers diagnosing common issues such as overfitting and underfitting, selecting meaningful features, working with skewed datasets, and employing cross-validation methods to ensure robust and generalizable models. This course blends theoretical concepts with practical applications. You’ll explore predictive analytics' role in digital transformation initiatives through case-based projects, reflection exercises, and guided activities. You’ll also develop critical skills in identifying opportunities to integrate analytics into decision-making processes, ensuring your insights drive measurable outcomes. This course, led by Professors Vikrant Vaze and Reed Harder, provides a supportive yet challenging environment for learners at all levels. Whether a seasoned professional or new, you’ll learn to think critically, code effectively, and apply your skills to meaningful, data-centric problems. By the end of the course, you’ll have the expertise to lead predictive analytics projects and contribute to digital transformation efforts in any industry. 3b:T44a,

• ● 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.● Diagnose and Improve Model Performance : Identify issues like overfitting and underfitting, apply cross-validation, and select optimal features to ensure robust, generalizable results.● Leverage Advanced Techniques : Explore neural networks, regularization, and cloud-based tools to scale and optimize predictive analytics for complex business challenges.● Integrate Analytics into Decision-Making : Translate data-driven insights into actionable strategies to drive innovation and efficiency in digital transformation initiatives.

• Course: Fundamentals of Digital TransformationSkills: Basic knowledge of Python