Data Science for Architecture, Engineering and Construction (AEC)

Last Updated on March 24, 2022 by Admin

This FREE course on Data Science for Architecture, Engineering, and Construction (AEC) industry introduces data science skills targeting applications in buildings’ design, construction, and operations. You will learn practical coding within this context, emphasizing basic Python programming and the Pandas library. This course is offered by the National University of Singapore (NUS).

About this course

Course Length: 6 Weeks
Effort: 3–5 hours per week
Price: FREE
Course Type: Instructor-led on a course schedule
Course Offered By National University of Singapore (NUS)

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The building industry is exploding with data sources that impact the energy performance of the built environment and the health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole construction analytics tool for professionals in this field.

Participating in mainstream data science courses might provide skills such as programming and statistics; however, the applied context to buildings is missing, which is essential for beginners.

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This course focuses on developing data science skills for professionals, specifically in the built environment sector. It targets architects, engineers, construction, and facilities managers with little or no previous programming experience.

An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from buildings’ design, construction, and operations to learn and practice data science techniques.

Essentially, this course adds new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and essential machine learning using Python, Pandas data analytics, construction analytics, sci-kit learn machine learning libraries and the web-based Colaboratory environment.

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In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.

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What you’ll learn

Why data science is essential for the built environment
Why building industry professionals should learn how to code
A jump start in the Python Programming Language
Overview of the Pandas data analysis library
Guidance in the loading, processing, and merging of data
Visualization of data from buildings
Basic machine learning concepts applied to build data
Examples of parametric analysis for the integrated design process
Examples of how to process time-series data from IoT sensors
Examples of analysis of thermal comfort data from occupants
Numerous starting points for using data science in other building-related tasks

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Syllabus

Section 1: Introduction to Course and Python Fundamentals

This introduction covers an overview of crucial Python concepts and the motivating factors for building-industry professionals to learn to code. The NZEB at the NUS School of Design and Environment is introduced as an example of a building that uses various data science-related technologies in its design, construction, and operations.

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Time-series analysis Pandas functions are demonstrated in the Construction Phase by analyzing hourly IoT data from electrical energy meters.

Further future learning path examples are introduced for the Construction Phase, including project management, building management system (BMS) data analysis, and digital construction such as robotic fabrication.

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Section 4: Statistics and Visualization Basics and Operations Phase Application Example

Using Pandas and the Seaborn library, various statistical aggregations and visualization techniques are demonstrated on Operations Phase occupant comfort data from the ASHRAE Thermal Comfort Database II.

Further future learning path examples are introduced for the Operations Phase, including energy auditing, IoT analysis, occupant detection, and reinforcement learning.

Section 5: Introduction to Machine Learning for the Built Environment

This concluding section gives an overview of the motivations and opportunities for predicting the built environment.

Prediction, classification, and clustering using the sci-kit learn library are demonstrated on the electrical meter and occupant comfort data. The course is concluded with suggestions on more in-depth Python, Data Science, and Statistics courses on EDx.

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Dr. Clayton Miller led the development of this curriculum with support from NUS students Ananya Joshi, Charlene Tan, Chun Fu, James Zhan, Mahmoud Abdelrahman, Matias Quintana, and Vanessa Neo.