Data Science for Building Industry
Master data science skills for construction and architecture with Python and Pandas. Learn practical coding for building lifecycle analysis.
Master data science skills for construction and architecture with Python and Pandas. Learn practical coding for building lifecycle analysis.
This comprehensive course bridges the gap between data science and the built environment sector. Designed for architecture, engineering, and construction professionals, it provides hands-on training in Python programming and data analytics. The curriculum focuses on practical applications across building lifecycle phases, from design to operations. Students learn to handle large datasets, perform visualizations, and implement basic machine learning techniques using Python, Pandas, and sci-kit learn. The course emphasizes real-world applications using actual building data, making it especially relevant for industry professionals looking to enhance their analytical capabilities beyond traditional spreadsheets. Through hands-on exercises and practical examples, participants develop skills in processing IoT sensor data, analyzing thermal comfort metrics, and performing parametric analysis for integrated design.
4.4
(19 ratings)
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What you'll learn
Learn why data science is crucial for the built environment sector
Master fundamental Python programming concepts for building analysis
Develop proficiency in using Pandas for data processing and analysis
Create effective visualizations of building performance data
Apply machine learning techniques to building-related datasets
Analyze time-series data from IoT sensors and building systems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This course provides a comprehensive introduction to data science applications in the building industry. Starting with Python fundamentals, it progresses through data analytics using Pandas, time-series analysis of IoT data, and basic machine learning concepts. The curriculum emphasizes practical applications across the building lifecycle, including design phase analysis, construction monitoring, and operations optimization. Real-world examples and datasets from actual buildings are used throughout the course to demonstrate concepts and techniques. The course also covers statistical analysis, visualization techniques, and machine learning applications specific to building performance and occupant comfort.
Introduction to Course and Python Fundamentals
Module 1
Introduction to Pandas Data Analytics Library and Design Phase
Module 2
Pandas Analysis of Time-Series Data from IoT and Construction
Module 3
Statistics and Visualization Basics and Operations Phase
Module 4
Introduction to Machine Learning for the Built Environment
Module 5
Fee Structure
Instructor
Distinguished Building Data Scientist and Urban Environment Expert
Clayton Miller serves as Associate Professor in the Department of the Built Environment at the National University of Singapore, where he leads the Building and Urban Data Science (BUDS) Lab. After earning his doctorate from ETH Zürich, master's from NUS, and architectural engineering degrees from the University of Nebraska-Lincoln, he has built an impressive career advancing building performance analytics and urban sustainability. His research focuses on leveraging data from building management systems, energy information systems, and IoT devices to improve energy efficiency, comfort, and human satisfaction in built environments
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4.4 course rating
19 ratings
Frequently asked questions
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