Data-Driven Engineering Course Introduction

Digitalization and data are changing the world. In this course, we explore how data is used to inform decisions and drive engineering. We cover topics such as data collection and analysis, predictive analytics, data visualization, and prepare for additional courses on machine learning, control, and optimization. We review how to use data to improve the quality and efficiency of engineering systems and how to make decisions that are informed and guided by data.

Course: https://apmonitor.com/dde

By the end of the course, you will have a solid understanding of how to apply data-driven engineering principles in the workplace. You will be prepared to use data to make decisions with confidence, and to optimize engineering processes for greater efficiency.

Data engineering is the process of collecting and refining data. Clean and accessible data is an important preparatory step for many use cases that extract information and value from data. Data engineering is foundational to visualization, clustering, classification, regression, and other machine learning methods. This introductory course is on science and engineering applications of data science with particular emphasis on practical applications and specific examples. The course combines mathematical details with several case studies that provide an intuition for data engineering solutions. The engineering problems and theory guide the student towards a working fluency in state-of-the-art methods implemented in Python.

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