Programming for Data Analytics Term Two 2022-23
WARWICK BUSINESS SCHOOL
100% Individual Assignment
3500 words:
This is a strict limit not a guideline: any piece submitted with more words than the limit will result in the excess not being marked
Background
Board games have been enjoyed for centuries, and continue to be a popular form of entertainment for people of all ages. There are a large number of different types of board games, such as strategy games like Catan, party games like Pictionary, adventure games like Dungeons and Dragons. Some games can be played by single players while some may require as many as 10 players.
According to a recent market research, the global board game market size was valued at $13.4 billion in 2021, and expected to reach $26.88 by 20271. This is driven by an increase in the popularity of board games among all age groups, with millennials and Gen X leading the way. The COVID-19 pandemic has also played a role in the increase in sales, as people have been spending more time at home and looking for indoor activities.
1 https://www.businesswire.com/news/home/20221219005509/en/Global-Board-Games-Market-Report- 2022-to-2027-Industry-Trends-Share-Size-Growth-Opportunities-and-Forecasts---ResearchAndMarkets.com
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It is expected that the board game market will continue to grow in the coming years, driven by factors such as the growing popularity of tabletop gaming, the rise of online platforms and streaming, and the increased availability of niche and indie titles.
Tasks
1. Create a hypothetical business scenario, identify and propose a practical business objective(s) or problem(s). e.g. To help a game manufacturer create a new board game; To help a board game club decide on new games to offer, etc. At least one assumption needs to be made for your scenario, such as that the new game targets young kids or the game club charges guests by playing time (i.e., follows a subscription model).
2. Carry out a data science project based on your scenario. Your analysis should at least include machine learning based predictive modelling.
3. Write an executive report summarizing your scenario and work and explaining how your work would help to solve the proposed problem(s).
A dataset of 20343 board games is provided in the bgg.csv file. The data is collected from BoardGameGeek.com (BGG).
Requirements:
1. All codes must be implemented using Python.
2. You should use Jupyter Notebook to work on this project and submit the .ipynb file.
3. You are required to write an “executive summary” (word or pdf file) to present your
work. The summary should be no more than three pages (double-spaced, excluding any
figures, tables, and references)
4. Codes must be well documented with comments.
5. You should also include narratives along your codes using Markdown to explain and
justify your works and decisions, as well as describe any insights gained from each step.
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6. You may search online or discuss with other students, but each student must work independently2.
Notes:
1. Additional Python packages (not covered in class) are welcome to use. But they should be well documented through Markdown, including why they were needed.
2. Comments are different from explanations using Markdown.
3. Here is the relative importance and weighting of each component of your work. The
percentage is only indicative.
• Explanation/Description using Markdown (20% - 25%)
• Code, including comments (60% - 65%)
• Executive summary (15% - 20%)
4. Depending on your scenario and proposed objective(s) or problem(s), you may collect additional data from BGG or other sources to help your analysis. Please properly document the source of the additional data, including why the additional data was needed.
5. You might find similar datasets online but do not rely on the existing solutions as they may not work properly. The sample dataset has been adapted for this assessment.
6. The accuracy (or other metrics) of your final prediction model is less important than the process to achieve and improve that value.
7. You may try different algorithms (including those not covered in the lectures) and include them in your submission. However, a purposeful selection of a smaller number of algorithms with good justification is better than a random selection of a larger number of algorithms without good justification.
2 Please refer to WBS policy on collusion: https://my.wbs.ac.uk/-/academic/37360/item/id/381766/ Page 3 of 5
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SUBMISSION DEADLINE: 12:00 (noon, UK time) Thursday 27th April 2023
Word Count Policy and Formatting (found in your Masters Student Handbook Section 6.2c) Guidelines for Online Submission (found in your Masters Student Handbook Section 6.2e)
The submission deadline is precise and uploading of the document must be completed before 12.00 (UK time) on the submission date. Any document submitted even seconds later than 12.00 precisely will be penalised for late submission in line with WBS policy. Please consult your student handbook on my.wbs for more detailed information.
The online assignment submission system will only accept documents in portable documents format (PDF) files. Please note that we will not accept PDF files of scanned documents. You should create your assignment in your chosen package (for example, Word), then convert it straight to PDF before uploading. Please place your student ID number, NOT YOUR NAME, on the front of your submission as all submissions are marked anonymously.
All the scripts should also have the following paragraph included on the front page:
PLEASE ENSURE YOU KEEP A SECURITY COPY OF YOUR ASSESSMENT
Your Academic Writing and Avoiding Plagiarism Module on my.wbs has lots of useful information on structuring assignments, academic style and demonstrating critical engagement.
Please ensure that any work submitted by you for assessment has been correctly referenced as WBS expects all students to demonstrate the highest standards of academic integrity at all times and treats all cases of poor academic practice and suspected plagiarism very seriously. You can find information on these matters on my.wbs, in your student handbook and on the University's library web pages here.
The University's Regulation 11 clarifies that '...'cheating' means an attempt to benefit oneself or another by deceit or fraud. This includes reproducing one's own work...' It is important to note that it is not permissible to re-use work which has already been submitted by you for credit either at WBS or at another institution (unless you have been explicitly told that you can do so). This is considered self-plagiarism and could result in significant mark reductions.
Upon submission of assignments, students will be asked to agree to one of the following declarations:
Individual work submissions:
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By agreeing to these declarations (when the message pops up on submission) you are acknowledging that you have understood the rules about plagiarism and self-plagiarism and have taken all possible steps to ensure that your work complies with the requirements of WBS and the University.
You should only indicate your agreement with the relevant statement, once you have satisfied yourself that you have fully understood its implications. If you are in any doubt, you must consult with the Module Organiser or Named Internal Examiner of the relevant module, because, once you have indicated your agreement, it will not be possible to later claim that you were unaware of these requirements in the event that your work is subsequently found to be problematic in respect to suspected plagiarism or self- plagiarism.
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