Big Data Analytics for Smart City Infrastructure - W22
General
- Undergraduate; 4th year
- 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 18, 2022Experience start
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February 6, 2022Project Scope Meeting
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February 13, 2022Project Proposal
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March 6, 2022Project Interim Report
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May 8, 2022Final Submission and Presentation
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May 23, 2022Experience end
Timeline
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January 18, 2022Experience start
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February 6, 2022Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
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February 13, 2022Project Proposal
The students submit a short proposal, indicating their intended method and expected outcomes
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March 6, 2022Project Interim Report
The students will submit a short report on their progress towards solving the problem.
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May 8, 2022Final Submission and Presentation
The students will present their projects to the partner organization.
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May 23, 2022Experience 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
Required questions to apply
Companies must answer the following custom questions in order to apply to this experience:
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.
Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.