class: center, middle, inverse, title-slide .title[ # Projects Q&A ] .subtitle[ ## <br><br> Introduction to Data Science ] .author[ ### University of Edinburgh ] .date[ ### <br> 2024/2025 ] --- layout: true <div class="my-footer"> <span> University of Edinburgh </span> </div> --- ## Reminders - The project presentations on Friday Nov 29th will be in different rooms: * **9:00-10:30** in JCMB 6206 * **10:40-12:00** in JCMB 6206 * **14:10-15:30** in JCMB 6206 and JCMB 6301 * **15:30-17:00** in JCMB 6206 and JCMB 6301 - Check the [timetable](https://www.learn.ed.ac.uk/ultra/courses/_116960_1/outline/file/_10861167_1) on Learn (Assessment > Timetable presentations) -- - Lecture on Wednesday "Recap and Looking Beyond IDS" + some extra time for Q&A. - Q&A session on Wednesday and Piazza for last minute questions. --- ## General Presentation tips - **Engaging Opening**: Start with an interesting fact, question, or story coming from your analysis to grab the audience’s attention and set the tone for your presentation. - **Clear and Logical Flow**: Structure your presentation logically. Start with a brief introduction, follow with the main content, and conclude with a summary. - **Use Visual Aids**: Enhance your presentation with slides, charts, and visuals when they are necessary. Make sure they are clear, simple, and support what you’re saying. Use plots / tables rather than lots of text. - **Prepare for Questions**: Anticipate questions and prepare answers. This shows depth of knowledge and preparation. --- ## Extra Presentation tips - Practice with your team. - Each presentation will last 7 minutes + some Q&A. There is a penalty for going overtime. -- - Focus on the key points, don't include too much text on slides or irrelevant plots. Get your key findings across. - If you have investigated multiple sub-questions, it's probably better to select not more than 2 of them. You will have the opportunity on the report to describe all that you have done. -- - Points for teamwork: make sure that everyone participates in the presentation. --- ## Practical aspects - You will connect your laptop with a HDMI cable. - Check that you can load your slides correctly beforehand! - If you don't have an HTMI port, you should email your slides to me (both html and the folder(s) associated to it, compressed in a zip file). --- ## Data Science life cycle <img src="img/data-science-cycle/data-science-cycle.001.png" width="90%" style="display: block; margin: auto auto auto 0;" /> --- ## Report Structure - **Introduction** – What is your data? What is the research question(s) you are investigating? Is there any relevant background information/literature that can help give context to your data/questions? -- - **Methods** – Summarise what cleaning/pre-processing you have done to your data. Brief description of data science techniques you have used in your investigation. -- - **Results** – Tables, figures and summary values from your exploratory investigation. Summary of a fitted statistical model, such as estimates, plots and fit statistics. A brief explanation of what the results mean in the given context of your data. -- - **Discussion** – What are the advantages/limitations of the investigation you have performed? If applicable, are there any ethical considerations in relation to your investigation/findings? If you had more time, what would you have done? --- ## Write-up tips - Be clear. Minimise jargon, make sure explanations are clear. Get someone else in your team to read and edit your text. - Use a consistent font, size, and heading style. This enhances readability and gives your report a professional look. - Use a consistent citation style throughout your report. This adds credibility and allows readers to verify your sources. - Do a good job on the basics rather than trying to do everything. Remember you only have 1,500 words. - No penalty will be applied if the report has up to 10% extra words (no penalty if word count < 1650) --- ## Problems Hopefully you didn't have any, but... -- - Everyone should have contributed equally. When completing the peer evaluation, if you are suggesting that an individual had "No contribution" or "Very poor" contribution, then you must provide a justifiable explanation. -- - If this is not the case (or you have had any other difficulties), please say so in the peer evaluation form. We will also check GitHub commits if the peer evaluation forms suggest an issue. -- - Anyone who doesn't contribute a reasonable amount will have their marks reduced. -- - Everyone has different backgrounds, so you should try and work together and include everyone in the project. --- ## Delivarables - Presentation - on Friday, 29th November, during your regular workshop slot - slides submission - due on Monday, 2nd December, at 10:00 - Report - due on Monday, 2nd December, at 10:00 - Peer evaluation - due on Monday, 2nd December, at 10:00 --- ## About Submission - Submission done on Gradescope for report and slides: `final-project`. - Submit both the `.html` file for your report and the `.html` file for your presentation slides. - Only **one person per team** needs to complete this. - This person needs to add their group members to the submission using the drop down list. -- - Peer evaluation is via `WebPA` (in Learn > Assessment > WebPA). - **Everyone** needs to complete this. - If you do not complete this peer feedback for all members of your team, you will receive a penalty of 20% off your mark. --- ## Logistics - Make sure you include a link to your GitHub repo within the report, and add the course GitHub account to the repo. -- - In your report `.Rmd` file, change the `author:` field to include both: **your names** & **your group number** ``` md author: by TeamGoT (group number 8): Jon Snow, Arya Stark, Daenerys Targaryen, Tyrion Lannister ``` -- - In the `README.md` file in your project repository, please add a section with the name, student ID and Github username of each team member, such as ``` md ## Team members - Jon Snow (s1234567), johnsnow123 - Arya Stark (s1122334), thefaceless - Daenerys Targaryen (s4433221), dragon_queen - Tyrion Lannister (s1111111), t.lannister ``` --- class:middle #Questions?