Developing CF for video data fit as multivariate functional data. Phase 3

positions available: 2 This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects). To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.

Developing CF for video data fit as multivariate functional data. Phase 2

positions available: 2 This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects). To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.

Developing CF for video data fit as multivariate functional data. Phase 1

positions available: 2 This project develops filters for functional data, specifically video data, that can be used to extend functional neural networks to convolutional neural networks. Filters work for data with relationships. These have been developed for photos and work for relationships in time series data. We plan to link these two filters into video data to filter unique properties of video (movement, colour change, and effects). To implement this project, we plan to use Python's functional data analysis package, pillow, and pandas. We will produce and distribute a package using GitHub so that interested teams can implement the filters. We will also produce this framework as there is no current format for turning videos into functional data.

Level UP- Effect of COVID-19 pandemic on education Phase 3

This project aims to determine the impact that COVID-19 has had on Alberta teacher retention. Over the last two Phases, a survey was distributed to every public and Catholic school division across Alberta, querying the state of school staff hiring, retention, resignation, and retirement before and after the COVID-19 pandemic. This Phase will conclude the survey collection process, focusing on analyzing the data collected. Many data mining analysis methods will be employed to determine common factors across these teacher retention results, including clustering, decision tree analysis, correlation tables, and more. The R coding language will be used for the analysis, which will be run in the RStudio development environment. Following this analysis, a research paper will be composed, including related works, background information, and an in-depth explanation of the study and the results.

Analysing the effect of industrial environmental damage in Canada. Phase 2

positions available: 2 This project aims to quantify/discover environmental damage due to industrial activity in Canada. This will be achieved by combining various datasets from the Canadian government and other certified sources. In this project, we will Look at environmental indicators such as infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons within amphibians as well as the overall ecosystems. Apply functional data clustering and model-based clustering algorithms. Show the current trajectory of these ecosystems towards recovery and regrowth.

Analysing the effect of industrial environmental damage in Canada. Phase 1

positions available: 2 This project aims to quantify/discover environmental damage due to industrial activity in Canada. This will be achieved by combining various datasets from the Canadian government and other certified sources. In this project, we will Look at environmental indicators such as infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons within amphibians as well as the overall ecosystems. Apply functional data clustering and model-based clustering algorithms. Show the current trajectory of these ecosystems towards recovery and regrowth.

Level UP- Effect of COVID-19 pandemic on education Phase 2

To conduct a province-wide study of school divisions’ experiences with teacher retention before and after the COVID-19 pandemic. This will be achieved by distributing a survey inquiring about how staff employing, retention, and dismissal have changed since the pandemic. The survey will also identify hiring practices and division properties such as rural/urban. Once complete, this survey will be submitted to the College of Alberta School of Superintendents (CASS), Alberta’s educational leaders that oversee the success of school systems in Alberta. Survey data will be used with data analysis techniques; unsupervised learning techniques such as clustering will identify key factors associated with higher or lower staff hiring, retention, and other quantifiers. Patterns associated with hiring techniques will be analyzed, and conclusions about population geographical factors’ relation to the survey results will be drawn.

Level UP- Effect of COVID-19 pandemic on education Phase 1

To conduct a province-wide study of school divisions’ experiences with teacher retention before and after the COVID-19 pandemic. This will be achieved by distributing a survey inquiring about how staff employing, retention, and dismissal have changed since the pandemic. The survey will also identify hiring practices and division properties such as rural/urban. Once complete, this survey will be submitted to the College of Alberta School of Superintendents (CASS), Alberta’s educational leaders that oversee the success of school systems in Alberta. Survey data will be used with data analysis techniques; unsupervised learning techniques such as clustering will identify key factors associated with higher or lower staff hiring, retention, and other quantifiers. Patterns associated with hiring techniques will be analyzed, and conclusions about population geographical factors’ relation to the survey results will be drawn.

Level UP-Analyzing factors that cause Car speeding Phase 2

positions available: 2 This analysis used data from speed zones and traffic signs using methods such as functional data clustering algorithms. In this project, we will 1. Determine indicators associated with a car speeding such as known police zones, how enforcement is done in these areas and unknown information about the day (weather, holiday, sporting events) that may or may not affect the data. 2. Apply functional data clustering and model-based clustering algorithms

Level UP-Analyzing factors that cause Car speeding Phase 1

positions available: 2 This analysis used data from speed zones and traffic signs using methods such as functional data clustering algorithms. In this project, we will 1. Determine indicators associated with a car speeding such as known police zones, how enforcement is done in these areas and unknown information about the day (weather, holiday, sporting events) that may or may not affect the data. 2. Apply functional data clustering and model-based clustering algorithms

Level UP-Building online tutorials for a web development project (Phase 2)

positions available: 3 In this project, we will need to build 6 online tutorials for building web applications. Tutorials include: 1. Building Backend server tutorials 2. Build frontend tutorials 3. creating a video recording of the tutorials

Level UP-Building online tutorials for a web development project

positions available: 3 In this project, we will need to build 6 online tutorials for building web applications. Tutorials include: 1. Building Backend server tutorials 2. Build frontend tutorials 3. creating a video recording of the tutorials

Level UP-Analyzing factors impacting COVID-19 vaccination rates Phase 1

positions available: 3 This analysis used COVID-19 vaccination data, and country indicators from the World Bank to 1. Determine indicators that are associated with vaccination rate 2. Create indices to measure the Vaccine Utilization and Vaccination Motivation per country 3. Apply Decision trees and Pearson correlations to determine relationships with country indicators and these indices

Level UP-Analyzing factors impacting COVID-19 vaccination rates Phase 2

positions available: 4 This analysis used COVID-19 vaccination data, and country indicators from the World Bank to 1. Determine indicators that are associated with vaccination rate 2. Create indices to measure the Vaccine Utilization and Vaccination Motivation per country 3. Apply Decision trees and Pearson correlations to determine relationships with country indicators and these indices

Level UP-Analyzing factors impacting COVID-19 vaccination rates Phase 1

positions available: 3 This analysis used COVID-19 vaccination data, and country indicators from the World Bank to 1. Determine indicators that are associated with vaccination rate 2. Create indices to measure the Vaccine Utilization and Vaccination Motivation per country 3. Apply Decision trees and Pearson correlations to determine relationships with country indicators and these indices

Level UP-Prediction of functional COVID data

positions available: 2 The students will implement the KNN algorithm for Covid data in Python based on existing libraries.