The topic of the project is analysing datasets related to the cryptocurrency market. The topic can be on anything you wish in the space of big data related to the cryptocurrency market. Anything reasonably related to topics that are covered in the course is within scope. For reference, there are two types of projects you might consider:
- Learn additional capabilities (e.g., visualization) of Python and Jupyter, and use them to build an interactive notebook for visualizing or exploring the cryptocurrency market related dataset of your choice. Your interactive notebook should interact with Spark, so that it will be capable of supporting exploration of data sets that are too large to fit in the memory of a single machine.
- Perform some interesting data science. Is there a particular cryptocurrency market dataset you’d like to explore or analyze? Your project could involve performing interesting analytics on a dataset—here, the focus would be the analytical product and the insights gleaned, as opposed to the raw algorithms themselves.
The use of Apache Spark should be justified in your project. For example, if you analyze only 1 MB of data, isn’t it better to use Python? Remember that it is okay to analyze a smaller dataset if (1) the dataset can potentially be considered big data. For example, using 20 MB of Twitter data makes sense because it can be potentially much bigger, (2) your Spark solution is scalable. Even if you are testing it on smaller datasets, it can potentially handle much bigger datasets. If you do not follow this rule, you cannot get more than 50% of the project mark.
Your project will be evaluated according to the following criteria, with roughly equal weight placed on each one.
- Scope/Relevance: Is the objective clear? Is the project related, course-related, and substantial enough?
- Methodology: Is the methodology appropriate and clearly described?
- Evaluation: Did you evaluate your work? Did you achieve your objective? If not, did you explain why not?
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