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+# ProjectDataViz
+
+**Class:** BSc Data Science for Responsible Business -- Year 1 Semester 2
+**Date:** February 2025  
+#**IMPORTANT NOTE: This description may slightly change during the current week. The last version will be posted on 21-02-2025**
+## Interactive Data Visualization Project
+
+### Year 1 BSc Data Science for Responsible Business
+
+### Project Overview
+
+This project aims at creating an interactive data visualization using `D3.js`. The goal is to explore, analyze, and present data in a visually engaging and informative way. Students will work in teams of two to develop a web-based visualization that addresses one of the following topics:
+
+- Personal Data: You can use your personal data from your personal device
+- Mobility and Climate Change. 
+- Environmental Impact of Digital Technology
+
+The project **MUST** be hosted on GitHub, and the final visualization must be publicly accessible via GitHub Pages or another hosting platform. The project accounts for **55% of the final grade** for this course.
+
+## Key Dates and Deliverables
+
+- Teams constitution and creation of the GitHub repo by **20/02/2025**
+- Choice of the dataset by **25/02/2025**
+- Exploratory data analysis by **01/03/2025**
+- Submission of the scoping document and the peer evaluation on **28/03/2025**
+- Final presentation on **31/03/2025**
+
+## Project Requirements
+
+### Data Source
+- You can use  **your** personal data (e.g., Spotify, Facebook, smartphone usage) or public datasets (e.g.,[ Open Data Grand Lyon ](https://www.data.gouv.fr/fr/organizations/grand-lyon/#/datasets), Data Is Plural, [kaggle](https://www.kaggle.com/datasets/), check out this [Link](https://github.com/awesomedata/awesome-public-datasets?tab=readme-ov-file)).
+- If necessary, create simulated data or combine multiple datasets.
+- You will need to conduct a data wrangling, cleansing and exploratory data analysis to understand the data set and make a plan for the visulaization phase. 
+
+### Technical Stack
+- **D3.js is mandatory** for creating the visualization. Other libraries (e.g., Tableau, python libraries such as plotly, matplotlib or seaborn, etc.) are **not allowed**.
+- The project must be hosted on GitHub and include a **GitHub Pages** deployment.
+
+### Documentation
+- Maintain a **project wiki** or **progress log** on GitHub to track your work. This will account for 20% of the project mark.
+- Include **sketches, design iterations, and key decisions** in the documentation.
+
+### Final Deliverables
+- A **functional web-based visualization** with clear context and storytelling.
+- A **scoping document** outlining the problem, target audience, data sources, and design choices.
+- A `README.md` file in the GitHub repository containing:
+  - Project title and description
+  - Team members and roles
+  - Links to the live visualization and documentation
+  - Credits for data sources and inspirations
+
+## Scoping Document Structure
+
+### Problem Statement
+- What problem are you addressing? 
+
+### Target Audience
+- Who is your primary audience? 
+- What tasks will they perform using your visualization?
+- How your visulaization will be helpful for them?
+
+### Related Work
+- Identify **three related projects** or visualizations. Explain how they inspire or differ from your approach.
+
+### Data Sources
+- List the dataset(s) you plan to use. Highlight their strengths and limitations.
+- What is your backup plan if the data is unavailable or incomplete? Are you planning to simulate data or choose another alternative?
+- Which type/format of data have you chosen? Structured (SQL) or not (CSV, Json, etc.)
+- Exploratory data analyis
+- Data cleansing and wrangling (highlight the issues with your dataset and how did you manage to solve them)
+
+
+### Team Organization
+- How will your team communicate and collaborate? Note that the project grade is not necessarily the same for the team members.
+- What roles have you assigned (e.g., design, development, data preprocessing)? Note that this has to be clearly stated in the `README.md`file. 
+Also note that the push operations will serve to assess the active participations of the team members.
+
+
+## Presentation Guidelines
+
+The final presentation (10 minutes) should cover:
+
+- **Context and Motivation:** Why did you choose this topic?
+- **Target Audience:** Who is your visualization for?
+- **Demo:** Showcase your interactive visualization.
+- **Design and Technical Choices:** Explain your design decisions and technical implementation.
+- **Innovation:** What makes your project unique?
+- **Limitations:** What challenges did you face, and how did you address them?
+
+## Evaluation Criteria
+
+The project will be graded based on:
+
+- **Peer evaluation (10%)**
+- **Scoping Document and Progress Tracking (20%)**
+- **Presentation and Demo (25%)**
+- **Technical Implementation (45%)**
+
+## Examples and Inspiration
+
+Here are some examples of past projects and resources for inspiration:
+
+- [MBTA Visualization](http://mbtaviz.github.io)
+- [Team Data Hub](http://teamdatahub.github.io)
+- [Harvard Visualization Course Examples](https://online.hbs.edu/blog/post/data-visualization-examples)
+- Other examples 
+    - [1](https://odsc.medium.com/7-cool-data-visualizations-using-d3-and-vega-3ff082d502cb)
+    - [2](https://qz.com/296941/interactive-graphic-every-active-satellite-orbiting-earth)
+
+
+## Submission Instructions
+
+### GitHub Repository
+- Name your repository meaningfully (e.g., `MobileData-Visualization`). 
+- Include a `README.md` file with project details and links.
+
+### Images
+- Add a **teaser image** (`teaser.png`) and a **thumbnail** (`thumbnail.png` or `.gif`) to the repository.
+
+
+## Useful links to learn D3.js
+- [freecodecamp](https://www.freecodecamp.org/learn/data-visualization/#data-visualization-with-d3)
+- [tutorials](https://www.tutorialsteacher.com/d3js)
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