Deliverables

  1. Project proposal
  2. Final presentations
  3. Final paper
  4. [[Project ideas]]

General remarks on the project reports

Project reports are not solely focused on the final results, but also on the process and decisions made along the way. We expect to hear the reasons for your final decisions, for instance the reason why you choose X, over alternative options like Y.

  1. Clarify the objectives and goal of your project. What do you want to do it, and why are your questions important to us?
  2. Provide a detailed description about the data you will use. Where the data are collected from, how they are compiled and preprocessed for your analysis. What are the data type of your focal features, and what features do you think are relevant for your analysis?
  3. Determine the appropriate methods. Additionally, consider discussing the methods used in previous studies. Considering the data types and the information you aim to present, what methods could potentially be suitable? It would also be beneficial to explore what approaches others have taken when working with similar datasets.
  4. Clarify the limitation and advantage of your approach. The limitation and advantage stems from data and methodologies, and must be discussed in light of existing works. For instance, you want to develop a link prediction algorithm for a social network based on the common neighbor approach. What are the fundamental assumption underlying the link prediction algorithms? When does the algorithm fail? Can you think of the advantage of your algorithm over other alternatives such as graph neural networks?
  5. Embrace failures. As Thomas Edison famously said, "I have not failed. I’ve just found 10,000 ways that won’t work." In many cases, works and analyses may appear to follow a single pathway, but it is important to recognize that this is just one of many paths that people have taken, many of which have turned out to be unsuccessful. It is crucial to try out multiple candidates, and more importantly, to document your failures and understand why they did not work. Consider using fake data, small subsets, mock-ups, and sketches. These methods can help you iterate and refine your approach, ultimately leading to more successful outcomes.

Proposal

A document should include the following sections:

  1. Project Title
  2. Team Members (1-4 people; keep in mind that a larger team is expected to accomplish more than a smaller one)
  3. Abstract: A concise summary of your project.
  4. Introduction: Provide motivation, background, and objectives for your project. Explain why it is important or interesting and why others should care. Review and discuss relevant existing works, particularly those that have inspired your project. Critique these works substantively. Remember, there is always a wealth of relevant work available.
  5. Questions or Objectives: Specify the methods you plan to create and what you hope to discover from the data.
  6. Datasets and Methods: Identify the dataset you will be using. If you haven't done so already, I strongly encourage you to reconsider your project. Obtaining and cleaning datasets can be time-consuming. Describe the dataset, including its structure and data types if it is tabular. Explain the methods you plan to apply and why you have chosen them. Finally, provide detailed information about the dataset to convincingly argue that it is suitable for your project and proposed methods.