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ORBS7120 - Big Data Analytics and Visualisation
Individual project instruction
1. Assessment structure
The individual project accounts for 80% of the module grade. Please choose one data set in the list below for your project. The length of the report should be of 3000 words, excluding references. All the relevant literature and resources for your project should be properly cited in the Harvard referencing style (you will find this website helpful). For your convenience,a template is provided here. Previous exemplars are available here.
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Marks allocated to criteria: |
Criteria |
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20% |
1. Introduction to data and research question (~1000 words) Please introduce the data set used and its background. The relevant literature (e.g., academic journal articles and textbooks) should be surveyed and properly cited with Harvard referencing style. More importantly, please identify a problem to be addressed with this data set (i.e., the research question). Please note that the problem should be specific (i.e., relevant in the application domain and linked to the variables available from the data set). |
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15% |
2. Data processing and exploration (~500 words) Please explain: Which variables are available from the data set? Which variables have been selected for the analysis and why? Any data transformations have been done and why? |
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25% |
3. Data visualisation and interpretation (~800 words) Please provide at least three data visualisations as descriptive analytical results (e.g., properties of the variables selected) and advanced analytical results (e.g., relationships between the variables selected, machine learning results). Please follow best practices taught in the module regarding data visualization. Importantly, please interpret the results and findings with details. Note that the data visualisations should be nontrivial representations of information, yet easy to interpret. |
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20% |
4. Data insights and conclusions (~700 words) Please provide the insights drawn from the analytics and summarise the findings. In particular, is the problem (i.e., research question) identified at the beginning addressed by the analytics? How? |
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20% |
5. Writing, styling and references The clarity, logic and presentation of the report, including spelling, grammar and punctuation. The general styling and references should be clear and consistent. |
2. Recommended datasets
NOTICE: Before starting the individual project, you will need to confirm your choice of data set withthis link on Moodle . The link will be available from Monday, April 29th 2024, 12:00 pm (UK time). Any submissions without data choice confirmation will have the marks reduced accordingly.
While the use of generative AI technologies such as ChatGPT could be helpful for learning Python programming, it is important to note that employing it to produce any portion of a written report is strictly prohibited.
Please find a list of recommended datasets below. All of them are from https://nijianmo.github.io/amazon/index.htmland have significant textual content (i.e., Amazon reviews). Therefore, text analytics tools should be employed. Please note that each dataset includes reviews (ratings, text, helpfulness votes) as well as product metadata (descriptions, category information, price, brand, and image features), which are linked by product ASIN number. Depending on your device’s capacity, you might use the origina dataset or the alternative 5 core version (which is smaller and easier to be processed) . You may also use the latest version of the dataset fromhttps://amazon-reviews- 2023.github.io/main.html, which covers a longer period until 2023.
• Amazon review Amazon Fashion
• Amazon review All Beauty
• Amazon review Appliances
• Amazon review Arts Crafts and Sewing
• Amazon review Automotive
• Amazon review Books
• Amazon review CDs and Vinyl
• Amazon review Cell Phones and Accessories
• Amazon review Clothing Shoes and Jewelry
• Amazon review Digital Music
• Amazon review Electronics
• Amazon review Grocery and Gourmet Food
• Amazon review Home and Kitchen
• Amazon review Industrial and Scientific
• Amazon review Kindle Store
• Amazon review Luxury Beauty
• Amazon review Movies and TV
• Amazon review Musical Instruments
• Amazon review Office Products
• Amazon review Patio Lawn and Garden
• Amazon review Pet Supplies
• Amazon review Software
• Amazon review Sports and Outdoors
• Amazon review Tools and Home Improvement
• Amazon review Toys and Games
• Amazon review Video Games
• Others (please email to confirm)