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CAPSTONE PROJECT – FOUNDATIONS OF DATA SCIENCE OVERVIEW This assessment involves writing a report that summarises a data science related investigation that you have conducted on data that you have collected yourself. The investigation must involve the main topics covered in the subject, most noticeably data pre-processing (representation, wrangling, tidying) and exploratory data visualisation using R/RStudio. It some ways this is a merger of Assessments on Exploratory Visualisation and and Data Pre-Processing, however neither the dataset nor the pre-processing/exploratory steps to be carried out will be provided, you have to make independent choices and decisions. You will need to find your own data using good practices. Your dataset cannot be smaller than 1000 observations of 5 variables, except if the targeted data science problem to be addressed relates to spatial-temporal data, case in which less than 5 dimensions could be allowed. Preferably, you should use a dataset relevant to your place of work. Do not use data from textbooks or from R packages. Do not use data from the same public sources that have been used in the subject (e.g. UCI repository). Do not use data from on-line prediction competitions such as kaggle. You can use public data, but the data should be appropriate for addressing a relevant data science problem. You don’t need to solve this entire data science problem in your investigation, but you need to clearly indicate what the targeted problem would be about and how your project can contribute towards addressing it. You have to write a report with details about the problem in question, the data, the methods, results, analyses and findings. You might like to look online for research papers for examples of how to shape your report. Obviously many of these papers will have undergone extensive work to collect their data, we don’t expect that for you. We also don’t expect you to win a Nobel prize with this assessment. Ideally, you will be able to demonstrate that: (a) you have grasped important concepts associated with this subject, most noticeably data pre-processing and exploratory visualization; and (b) you can communicate your investigation in a formal written manner.Regarding (a), we expect that your investigation will include at least six (60% or more) of the following topics:

    1. Data representation
    2. Unstructured to Structured data
    3. Data cleaning
    4. Type conversion
    5. Missing value imputation
    6. Gathering/Spreading
    7. Data subset selection and/or subsampling
    8. Group-based data summarisation
    9. Variable selection and/or transformation
    10. Exploratory visualisation using ggplot2

 

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  • Posted on : June 14th, 2019
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