Statistical Analysis of a dataset
Description:
The following module aims to introduce the basic principles and fundamental techniques of statistics and machine learning for data analysis. After the module they will have knowledge of probability theory, statistics to model uncertainties in engineering problems. The students will be able to do statistical analysis of datasets, and they will apply to their own chosen real-world dataset.
It will be used a blended learning approach including in-class and out-of-class activities, following the STREAM model. A 'feedback loop' will be used, shifting between 'out-of-class' content (self-study, preparatory activities, project assignment) and 'inThe 'out-of-class' activities will be used to provide feedback of the students' learning, and to adjust the 'in-class' activities (lectures, audience response systems, peer feedback).
Intended Learning Outcomes:
- understand the statistics theories for analyzing data
- apply statistical tools to a chosen dataset
- explain to the peers the project assignment
- improve the analyses after peers feedback
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Pre-class activity |
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Literature uploaded on Brightspace |
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Read relevant literature ↓ |
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No Support |
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In-class activity (face-to-face and synchronous) |
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PPT presentation, synchronous with Zoom and IPad |
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Listen lectures, keep notes, ask questions ↓ |
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Teacher |
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Mentimeter |
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Respond to quiz and open-ended questions ↓ |
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Teacher and student feedback |
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Matlab, Pyhton |
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Develop statistical models of example datasets ↓ |
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Teacher and student feedback |
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Mentimeter |
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Respond to quiz through games ↓ |
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Teacher and student feedback |
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Out-of-class activities (online supervision) |
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Zoom, e-mails, forum |
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Develop project assignment of a real-world dataset ↓ |
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Teacher |
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In-class activity (face-to-face and synchronous) |
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powerpoint, zoom, VR, youtube |
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Present the group project (slides, video, etc) ↓ |
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Students provide feedback to their peers. The teacher moderates the discussion |
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Out-of-class activity (online supervision) |
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Teacher and peer feedback |
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Improve the project assignment |
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Teacher |
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Additional information
The in-class lectures of this short teaching module are 2 lectures of 3.5h each.
In-class activity (lecture 1): The first lecture is split into 3 sessions of 1h each, with 15 min breaks. In the first 2 sessions the main theory is described, but lectures and quiz (through Menti) are split every 5-10 minutes. In the last part of the second session a "game-competition" is used to check the level of understanding of the class and increase the level of engagement. In the third session a worked example is developed together teacher and other students.
Out-class activity: The students make a group, choose their own dataset and develop their project assignment. They are assisted on-line by the teacher
In-class activity (lecture 2): the students present their project to the class through presentations. Feedback is provided by peers and teacher.
Out-of-class activities: The students improve the project assignment following the feedback of their presentation. They are assisted on-line from the teacher