Data Science for Construction, Architecture and Engineering

Clayton Miller, NUS

This course introduces data science skills targeting applications in the design, construction, and operations of buildings. You will learn practical coding within this context with an emphasis on basic Python programming and the Pandas library.

The building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole 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 the most important part for beginners.

This course focuses on the development of 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 the design, construction, and operations of buildings to learn and practice data science techniques.

Essentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and 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.

What will you learn

  • Why data science is important 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 building 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

Dates:
  • 8 July 2020
Course properties:
  • Free:
  • Paid:
  • Certificate:
  • MOOC:
  • Video:
  • Audio:
  • Email-course:
  • Language: English Gb

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