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)
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.
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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.
In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.enroll for free
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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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.
Section 2: Introduction to the Pandas Data Analytics Library and Design Phase Application Example
The foundational functions of Pandas are demonstrated in the context of the integrated design process through the processing of data from parametric EnergyPlus models.
Further future learning path examples are introduced for the Design Phase, including building information modeling (BIM) using Revit or Rhino, spatial analytics, and building performance modeling Python libraries.enroll for free
Section 3: Pandas Analysis of Time-Series Data from IoT and Construction Phase Application Example
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.enroll for free
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.