This assignment has been designed to help students develop data analytics and visualisation skills and to allow students to practice state of the art approaches that can be used in storytelling based on Visual Data Analytics (VDA) on real world datasets.
Key Admin Information
- Required submissions:
- 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.
- 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.
- The late penalty for the assignment is 5% of the assigned mark per day, starting after 4pm on the due date.
- Numbers with decimals should be reported to the Fourth–decimal point in the report.
- If you wish to include additional material, you can do so by creating an appendix.
There is no page limit for the appendix. Keep in mind that making good use of your audience’s time is an essential business skill. Every sentence, table and figure have to count. Extraneous and/or wrong material will potentially affect your mark.
- Anonymous marking: As the anonymous marking policy of the University, please only include student ID in the submitted report, and do NOT include your name.
The file name of your report should follow the following format. Replace “XXXX” with your SID. Example: QBUS6860_2022S1_SIDXXXX.
- Presentation of the assignment is part of the assessment. Markers will deduct up to 10 marks for poor clarity of writing and presentation.
- For Turnitin to check your code, 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.
- Carefully read the requirements for the assignment.
- Please follow any further instructions announced on Canvas and ED.
- You May Do your data manipulation outside Python (e.g. using excel) although we believe using python is more convenient. However you MUST use Python to produce any visualisations that you have. You must submit your Python code with your processed data for verification.
- Reproducibility is fundamental in data analysis, so that you make sure you suggest the right Python py file or Jupyter Notebook ipynb files that generate the results in your report. Markers will run your program for checking.
- The University of Sydney takes plagiarism very seriously. Please be warned that plagiarism between individuals/groups is always obvious to the markers and can be easily detected by Turnitin.
- Not submitting your code will lead to a loss of 50% of the assignment marks.
- Failure to read information and follow instructions may lead to a loss of marks.
Furthermore, note that it is your responsibility to be informed of the University of Sydney and Business School rules and guidelines, and follow them.
- Referencing: Business School recomm ends APA Referencing System. (You mayfind the details at:https://libguides.library.usyd.edu.au/citation/apa7)
- Feedback will be provided on the marked submission.
The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding,gaming, and robotics.
Each of recent years, the conference attracts more than 2000 paper submissions, touching a wide range of modern machine learning research.
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.
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