Who are the most productive authors?

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Required submissions:
a. ONE written report (word or pdf format, through Canvas – Assignment 2-
Report Submission) of not more than 15 pages (excluding appendices) in normal 12 point fonts and single line – this is the full report including all graphs and any additional materials or outputs of your analysis etc. I would expect this is in a typical research paper format which includes sections of Introduction, Question Description, Analysis Methods/Process, Results Presentation/Analysis, Summary/Conclusion, and References, and plus Appendix if any.
b. A Full Set of Python “.py” or Jupyter Notebook “ipynb” files (through Canvas- Assignment 2 – Upload Your Program Code Files) and plus any datasets of your own. Important: If you made significant changes to the provided data files (e.g. merged several tsv files, mined additional data, etc.), you must also upload your datasets along with your program files so that we can check correctness of your calculations! This is to make sure your code can be verified by markers.
please copy and paste your codes into Appendix. Code should be formatted by equal width fonts such as Courier New or Consola.
If your programs are in py file, simply copy and paste into the report Appendix. If you are using Jupyter Notebook, please follow InstructionPY to convert it to “py” files first then copy the created py files into Appendix of the report.
Project Description and Requirement
This project is designed for you to practice your skills across the entire Exploratory Data Analysis/Visual Data Analytics (EDA/VDA) process including storytelling.
It is better to regard this as a new research project in which you have sufficient flexibility to conduct your research.
The goal is, but not limited to, to describe the current status of deep learning/machine learning/AI research, its past, its current patterns and its trend into future through the following steps:
1. Acquire data from ICLR2017 – 2021 conferences, hosted on OpenReview website server https://openreview.net. Note: Most data have been collected for you, please download the copy from Canvas. When this project starts, it is very likely that data of ICLR2022 will be available, you are encouraged to include 2022 data into your project, but this is NOT a must.
2. Explore the data (both the provided and that you may gather) to find a story and ask questions. For example, what are the major topics of each conference? How does it change in the recent years? Who are the most productive authors? Which university/organisation takes lead? Many questions you can ask.
3. Assess and explain the fitness of the data for answering your question. For example, if you want to know the research collaboration network among some researchers/authors, you may need acquire the relation/network information which OpenReview server can provide (you need find a way to get it).
4. Create necessary visualisation(s) that tell the story about the conference data. These visualisation should be used for the purpose of revealing patterns/your discovered insight and presenting the story you are telling underpinned by your solid data analysis.
5. Carefully explain your conclusions: what insight does your analysis bring?

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