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A Data Programming Project ( Programming with data ) Now that you have had a chance to explore some techniques and tools in Python, it
A Data Programming Project Programming with data Now that you have had a chance to explore some techniques and tools in Python, it is time to start working on your own exploratory data analysis project. This is a chance for you to explore a research area of your choosing. You will identify a clear agenda for research and explore this topic at a high level. Expectations: Identify your own research area and questions, including importing knowledge from external sources. Acquiring a dataset that is fit for purpose. Exploring the dataset through different lenses, identifying key features and potential flaws in the data. Produce a systematic, rigorous and wellreasoned report on how you work through the dataset. Describe at both a technical and analytical level, how and why you are approaching the problem space in a particular way. Identify gaps in your approach, the dataset and any techniques, tools, libraries or data structures that you choose to utilise. Consider the ownership provenance of data through a data processing pipeline and how this might manifest. Consider how data can be prepared, refined and explored for further analysis eg for a final year project. Critically analyse, evaluate and summarise findings from a miniresearch project. Reflect on both processes and outcomes of your project, including any missing steps or stages. Give a valuable account as to how your analysis provides useful and interesting insights around some dataset. You should present your work in a single Jupyter Notebook ipynb file as part of a larger ZIP archive of files. Any data that you use should also be included and readily accessible for checking included in the ZIP archive. Your ZIP archive should not exceed MB in total, including your ipynb file and any data that you choose to utilise. The dataset should not be more than MB in total size. The marking rubric includes a description of expectations and deliverables, where sections aj are each worth a total of marks.
A Data Programming Project Programming with data
Now that you have had a chance to explore some techniques and tools in Python, it is time to start working on your own exploratory data analysis project.
This is a chance for you to explore a research area of your choosing. You will identify a clear agenda for research and explore this topic at a high level.
Expectations:
Identify your own research area and questions, including importing knowledge from external sources.
Acquiring a dataset that is fit for purpose.
Exploring the dataset through different lenses, identifying key features and potential flaws in the data.
Produce a systematic, rigorous and wellreasoned report on how you work through the dataset.
Describe at both a technical and analytical level, how and why you are approaching the problem space in a particular way.
Identify gaps in your approach, the dataset and any techniques, tools, libraries or data structures that you choose to utilise.
Consider the ownership provenance of data through a data processing pipeline and how this might manifest.
Consider how data can be prepared, refined and explored for further analysis eg for a final year project.
Critically analyse, evaluate and summarise findings from a miniresearch project.
Reflect on both processes and outcomes of your project, including any missing steps or stages.
Give a valuable account as to how your analysis provides useful and interesting insights around some dataset.
You should present your work in a single Jupyter Notebook ipynb file as part of a larger ZIP archive of files. Any data that you use should also
be included and readily accessible for checking included in the ZIP archive. Your ZIP archive should not exceed MB in total, including your
ipynb file and any data that you choose to utilise. The dataset should not be more than MB in total size.
The marking rubric includes a description of expectations and deliverables, where sections aj are each worth a total of marks.
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