Big Data Analytics for Smart City Infrastructure
General
- 50 learners; teams of 4
- 75 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 1 projects wanted
- Anywhere
- Academic experience
- Any
- Any industries
Categories
Project timeline
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January 29, 2021Experience start
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February 6, 2021Project Scope Meeting
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February 13, 2021Project Proposal
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March 6, 2021Project Interim Report
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May 8, 2021Final Submission and Presentation
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May 22, 2021Experience end
Timeline
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January 29, 2021Experience start
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February 6, 2021Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
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February 13, 2021Project Proposal
The students submit a short proposal, indicating their intended method and expected outcomes
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March 6, 2021Project Interim Report
The students will submit a short report on their progress towards solving the problem.
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May 8, 2021Final Submission and Presentation
The students will present their projects to the partner organization.
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May 22, 2021Experience end
Overview
- Details
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Student-consultants will analyze city data sets (normally available through open city portals, etc.) through state-of-the-art Machine Learning and Data Mining technologies, to: identify trends, and/or create predictive models. Their models are used to create solutions for infrastructure sectors (transportation, building, energy, urban water/drainage, etc.) which can be deployed using digitalization in smart cities.
- Learner skills
- Machine learning, Data analysis, Sustainabilty, Rapidminer, Infastructure engineering
- Deliverables
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The student will deliver the following:
- A 10 - 15 page report, explaining their problem statement and objectives, the methods they followed, The model they developed, and Their results;
- A 10-15 minute presentation
- The model(s) developed (in form of RapidMiner processes), as well as the pre-processed data they used
Project Examples
Student-consultants will analyze urban data sets using data mining and machine learning technologies to improve city efficiency, sustainability and resilience.
Some past project examples include:
- Road Condition Assessment through Data Mining
- Real Estate Price Forecast through Data Mining
- Predictions for Available Parking Spots in Various North American Cities
- Analysis of Road Safety and Road Accidents
- Improving Building Thermal Comfort and Energy Performance using Machine Learning
- Analysis and Prediction of Energy Consumption Behavior at Building, District and City Level
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.
Be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.