建筑、建筑和工程数据科学

Data Science for Construction, Architecture and Engineering

This course is an Introduction to Data Science for Built Environment Professions. You will learn practical skills targeted at building industry professionals with an emphasis on basic Python programming, the Pandas and scikit-learn libraries, and Colaboratory Juptyer notebooks.

274 次查看
新加坡国立大学
edX
  • 完成时间大约为 5
  • 初级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

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

课程概况

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.

课程大纲

Week 1: Introduction to Course and Python Fundamentals – In this introduction, an overview of key Python concepts is covered as well as 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.

Week 2: Introduction to the Pandas Data Analytics Library and Design Phase Application Examples – 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.

Week 3: Pandas Analysis of Time-Series Data from IoT and Construction Phase Application Examples – Time-series analysis Pandas functions are demonstrated in the Construction Phase through the analysis of 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.

Week 4: Statistics and Visualization Basics and Operations Phase Application Examples – Various statistical aggregations and visualization techniques using Pandas and the Seaborn library 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, and occupant detection and reinforcement learning.

Week 5: Introduction to Machine Learning for the Built Environment – This concluding section gives an overview of the motivations and opportunities for the use of prediction in the built environment. Prediction, classification, and clustering using the sci-kit learn library is demonstrated on electrical meter and occupant comfort data. The course is concluded with suggestions on more in-depth Python, Data Science, and Statistics courses on EDx.

Development of this curriculum was led by Dr. Clayton Miller with support from NUS students Charlene Tan, Chun Fu, James Zhan, Matias Quintana, and Vanessa Neo.

千万首歌曲。全无广告干扰。
此外,您还能在所有设备上欣赏您的整个音乐资料库。免费畅听 3 个月,之后每月只需 ¥10.00。
Apple 广告
声明:MOOC中国十分重视知识产权问题,我们发布之课程均源自下列机构,版权均归其所有,本站仅作报道收录并尊重其著作权益。感谢他们对MOOC事业做出的贡献!
  • Coursera
  • edX
  • OpenLearning
  • FutureLearn
  • iversity
  • Udacity
  • NovoEd
  • Canvas
  • Open2Study
  • Google
  • ewant
  • FUN
  • IOC-Athlete-MOOC
  • World-Science-U
  • Codecademy
  • CourseSites
  • opencourseworld
  • ShareCourse
  • gacco
  • MiriadaX
  • JANUX
  • openhpi
  • Stanford-Open-Edx
  • 网易云课堂
  • 中国大学MOOC
  • 学堂在线
  • 顶你学堂
  • 华文慕课
  • 好大学在线CnMooc
  • (部分课程由Coursera、Udemy、Linkshare共同提供)

© 2008-2020 MOOC.CN 慕课改变你,你改变世界