As a data scientist (or an engineer or scientist bringing a data specialty to the team), you will bring data visualisation skills to any team. To get a job as a data scientist or data specialist, you will need to be able to show that you have those skills. The activities in this portfolio will give you hands-on practice at those skills, and serve as evidence of your expertise. You will produce visualisations, justify your choices, describe outcomes for your audience, and show that you can consider the context and goals of your data communication.
Through the portfolio you will explore a wide range of data types, visualisation types, and applied scientific and engineering contexts. This will help you to demonstrate and talk with authority about the breadth of your data science experience, and prevent you from becoming pidgeon-holed early in your career by the data you come across in your first job.
As well as contributing to a significant portion of your final grade for MXB262 (30%), the exercises in this portfolio will help you to develop and explore the skills needed to excel in your final project. In that project, all responsibility and most oversight will be handed over to you – you will be in charge of the data you explore, the questions you ask of it, and the visualisations you produce. Consider the tasks in this portfolio to be a more guided preparation for that project.
This porfolio is due as a single submission, but the questions will be released with the associated materials. The week these questions are released will be indicated through headings below. This is to indicate two things: first, this is the week you can look toward to find the relevant content in lectures and practical sheets. Second, this indicates how far along the portfolio you should be at all times through the semester. You will not be able to complete this portfolio in the week before it is due, the topics and methods you will need are far too broad.
Throughout these portfolio questions when you are being asked to produce a visualisation, you must comment on three things:
- appropriateness of plot type for the data being visualised, and variation in plot types throughout the questions (i.e. you should aim to show your ability to create a breadth of plot types covered in the unit up to that point)
- a full justification of your visalisation choices (plot types, variables, aesthetic choices, datasets where applicable)
- your answer to the question posed (where applicable), and your use of the visualisation to support that answer.
Note that you will be graded on the effective data visualisation choices you make in addition to your technical ability to produce the plots (i.e.: make it nice, and an effective communication tool).
Where you are being asked to justify your choices or answer questions, please limit your answers to two to three sentences where possible.
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