Data Science for Construction, Architecture and Engineering from edX

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 you'll 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

What's inside

Learning objectives

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

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

Syllabus

Section 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.

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.

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 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.

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

Section 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 Ananya Joshi, Charlene Tan, Chun Fu, James Zhan, Mahmoud Abdelrahman, Matias Quintana, Miguel Martin, and Vanessa Neo.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores multiple facets of data science as applied to the built environment

Taught by Clayton Miller, who is recognized for his work in energy performance and occupant health in buildings

Develops foundational data science and Python skills relevant to professionals in the construction and engineering industries

Provides numerous learning paths and opportunities for further growth in various built environment-related tasks

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science for Construction, Architecture and Engineering with these activities:

Review basic probability and statistics

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Strengthens foundational knowledge for machine learning.

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Review concepts from textbooks or online resources
Solve practice problems

Create a cheat sheet

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Boosts retention and comprehension through abstraction.

Browse courses on Pandas

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Identify core concepts
Summarize and condense material
Design visuals or diagrams

Complete the Pandas tutorial

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Reinforces foundational data manipulation skills.

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Follow along with the tutorial
Experiment with examples
Troubleshoot errors

Four other activities

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Participate in a study group

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Enhances learning through discussion and exchange of ideas.

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Form or join a study group
Prepare for meetings
Engage in discussions

Practice data visualization with Seaborn

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Enhances visualization skills essential for data analysis.

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Load data into a DataFrame
Create basic visualizations
Customize visualizations

Develop a data analysis dashboard

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Applies skills to create a practical tool for data exploration.

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Identify data sources
Design dashboard layout
Implement visualizations
Deploy dashboard

Analyze data to optimize building performance

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Encourages practical application of data analysis for building design.

Browse courses on Optimization

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Identify building data sources
Clean and prepare data
Apply machine learning algorithms
Evaluate and interpret results

Career center

Learners who complete Data Science for Construction, Architecture and Engineering will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist will analyze data using software and technology to extract meaningful insights. This course may be of particular use to people in the role who work in a construction, architecture, or engineering context. Data Science is an interdisciplinary field that combines computer science, statistics, and domain knowledge. It is increasingly in demand across industries, and the built environment is no exception. This course enhances one's ability to load, process, merge, analyze, and visualize data. These skills are a useful foundation for those working as Data Scientists in the construction industry.

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Building Energy Modeler

A Building Energy Modeler uses computer software to simulate the energy performance of buildings. This simulation can help architects and engineers design more energy-efficient buildings. This course may be useful for people in the role who work on projects in the design, construction, and operations phases of a building's lifecycle. It provides an overview of parametric analysis for the integrated design process, examples of how to process time-series data from IoT sensors, and examples of analysis of thermal comfort data from occupants.

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Facilities Manager

A Facilities Manager is responsible for the maintenance and operation of buildings and other facilities. They work to ensure that buildings are safe, comfortable, and efficient. This course may be particularly useful for those involved in the operations phase of a building's lifecycle. It provides an overview of statistical aggregations and visualization techniques using Pandas and the Seaborn library demonstrated on Operations Phase occupant comfort data from the ASHRAE Thermal Comfort Database II.

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Construction Manager

A Construction Manager plans, coordinates, and supervises construction projects. They work with architects, engineers, and other professionals to ensure that projects are completed on time, within budget, and to the required standards. This course may be particularly useful for people in the role who are responsible for project management. It provides guidance on the loading, processing, and merging of data, as well as examples of parametric analysis for the integrated design process.

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Architect

An Architect designs buildings and other structures. They work with clients to understand their needs and develop plans that meet those needs. This course may be useful for people in the role who are involved in the design phase of a building's lifecycle. It provides an overview of parametric analysis for the integrated design process and examples of how to analyze data from parametric EnergyPlus models.

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Engineer

An Engineer designs, builds, and maintains machines, structures, and systems. They work in a variety of industries, including construction, architecture, and manufacturing. This course may be useful for people in the role who are involved in the design, construction, or operations phases of a building's lifecycle. It provides an overview of basic machine learning concepts applied to building data, examples of how to process time-series data from IoT sensors, and examples of analysis of thermal comfort data from occupants.

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BIM Manager

A BIM Manager is responsible for the implementation and management of Building Information Modeling (BIM) processes. This course may be useful for people in the role who are interested in using data science to improve the efficiency of BIM workflows. It provides an overview of data science skills and techniques that can be used to analyze data from BIM models.

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Digital Construction Manager

A Digital Construction Manager is responsible for the implementation and management of digital construction technologies. This course may be useful for people in the role who are interested in using data science to improve the efficiency of construction processes. It provides an overview of data science skills and techniques that can be used to analyze data from construction sensors and other digital construction tools.

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Project Manager

A Project Manager plans, executes, and closes projects. They work with stakeholders to define project goals, develop project plans, and track project progress. This course may be useful for people in the role who are responsible for managing projects in the construction, architecture, or engineering industries. It provides an overview of data science skills and techniques that can be used to track project progress and identify potential risks.

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Occupant Experience Manager

An Occupant Experience Manager is responsible for improving the experience of occupants in buildings. This course may be useful for people in the role who are interested in using data science to analyze occupant comfort data and identify opportunities for improvement. It provides an overview of statistical aggregations and visualization techniques using Pandas and the Seaborn library demonstrated on Operations Phase occupant comfort data from the ASHRAE Thermal Comfort Database II.

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Data Analyst

A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. This course may be useful for people in the role who work in the construction, architecture, or engineering industries. It provides an overview of data science skills and techniques that can be used to analyze data from buildings and other built environment assets.

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Researcher

A Researcher conducts scientific research to advance knowledge and understanding in a particular field. This course may be useful for people in the role who are interested in using data science to improve the design, construction, and operation of buildings. It provides an overview of data science skills and techniques that can be used to analyze data from buildings and other built environment assets.

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Sustainability Consultant

A Sustainability Consultant helps organizations to reduce their environmental impact. They work with clients to develop and implement sustainability strategies. This course may be useful for people in the role who are interested in learning more about how data science can be used to improve the energy efficiency and sustainability of buildings. It provides an overview of how to analyze data from parametric EnergyPlus models and examples of analysis of thermal comfort data from occupants.

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Energy Auditor

An Energy Auditor assesses the energy efficiency of buildings and makes recommendations for improvements. This course may be useful for people in the role who are interested in using data science to analyze energy consumption data and identify opportunities for improvement. It provides an overview of statistical aggregations and visualization techniques using Pandas and the Seaborn library demonstrated on Operations Phase occupant comfort data from the ASHRAE Thermal Comfort Database II.

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IoT Analyst

An IoT Analyst collects, analyzes, and interprets data from IoT devices. This course may be useful for people in the role who are interested in using data science to analyze data from IoT sensors in buildings. It provides an overview of data science skills and techniques that can be used to analyze time-series data from IoT sensors.

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