Welcome to CANIS Data Analysis Hackathon!

The dataset which needs to be analyzed can be found here: https://www.kaggle.com/datasets/stevenpeutz/misinformation-fake-news-text-dataset-79k?resource=download

Our hackathon is a collaborative event that aims to increase awareness about misinformation and outliers in the data analysis world. Our goal is to bring together data scientists, statisticians, and other experts to analyze and visualize real-world data sets and uncover instances of misinformation and outliers.

Participants will work in teams to analyze the provided data sets and present their findings in a final presentation. The winning team will be chosen based on the quality of their analysis and presentation, as well as the impact of their findings on the data analysis community.

Throughout the hackathon, participants will have the opportunity to learn about the challenges of data analysis, network with others in the field, and contribute to the broader effort to promote data literacy and prevent the spread of misinformation. We encourage participants to use their skills and expertise to identify sources of misinformation, develop strategies for mitigating its impact, and use data visualization techniques to communicate their findings effectively.

The hackathon is open to all levels of experience, and we welcome participants from various backgrounds. Whether you are a seasoned data scientist or just starting your journey in data analysis, we believe that this hackathon will be a valuable learning experience for you.

We look forward to seeing you at the CANIS Data Analysis Hackathon, where together, we can make a difference in the fight against misinformation and outliers in data analysis.

Additionally, we would like to inform you that the CANIS Data Analysis Hackathon is hosted and managed by Schulich Ignite, a student-run club at the University of Calgary. Schulich Ignite is dedicated to promoting innovation, and technology across the University of Calgary community. Ignite focuses on promoting programming among high school students from all over Canada with a fun learning experience and providing insights into the field of Software Engineering.

As a student-run club, we believe in fostering an inclusive environment that encourages creativity, collaboration, and learning. We are excited to bring together students, faculty members, and industry professionals to work towards a common goal of promoting data literacy and preventing the spread of misinformation.

We would like to thank Schulich Ignite for their support in making this hackathon possible, and we look forward to collaborating with them to create an event that will have a lasting impact on the data analysis community.

We hope to see you at the CANIS Data Analysis Hackathon, hosted and managed by Schulich Ignite at the University of Calgary.

Prize Pool of 10,000$

- Free Gift Card for participants

- First Prize: 4,000$

-Second Prize: 2,500$

-Third Prize: 1,500$

- Four Special 250$ Prize

https://schulichignite.com/

https://canis-network.ca/

Requirements

The submission for a hackathon data analysis is an opportunity for teams to showcase their skills in data analysis and presentation. The submission should include a detailed analysis of a dataset, focusing on insights and trends that can be derived from the data. The analysis should also include data visualizations that effectively communicate the insights and trends to the judges and audience.

The submission should be in the form of a presentation, which can include slides, interactive dashboards, and/or other visual aids. The presentation should be concise and well-organized, highlighting the most important findings and insights. The submission should also include a brief summary of the methodology used in the analysis, including any tools or techniques that were employed.

In addition, the code used in the analysis should be included as part of the submission. This will allow judges to review the technical proficiency and creativity of the team's approach to analyzing the data.

Overall, the submission should demonstrate a clear understanding of the dataset and its potential applications, as well as the ability to effectively analyze and communicate insights from the data. Judges will be looking for creativity, innovation, and technical proficiency in both the analysis and presentation of the data.

Hackathon Sponsors

Prizes

$10,250 in prizes

First Prize

Second Prize

Third Prize

Special Award (5)

Judges

Dr. Jean-Christophe Boucher
Associate Professor at the School of Public Policy and at the Department of Political Science, University of Calgary

Adnan Raja
Sr. Analyst, Government of Canada

Judging Criteria

Data
·The data was properly processed and cleaned ·Visualizations accurately represent the data and are easy to understand
Creativity
Creativity ·Participants use an innovative approach (showcase creative design techniques) ·The visualization implements storytelling, ·The presentation and visuals are well-organized and appealing
Impact
·Visualization helps the audience better understand the issue of misinformation and raise awareness about the topic, Types of misinformation are clearly identified and address an important issue related to misinformation

CANIS Hackathon

Data Analysis Hackathon

Who can participate