- Learners can apply the skills obtained during the course to write a short data analysis project report.
Capstone Project
The Capstone Project report is the final assignment for this course and a completion is required to receive a course certificate about successful participation.
Learning Objectives
GitHub repository
Establishing the GitHub repository with self-identified data was part of the homework assignment of module 5, 6, and 7.
- Module 5 - Assignment 1: Identify data for your capstone project
- Module 7 - Assignment 2: Get going with your capstone project
It is important that these two assignments are completed before continuing with the write up of the report as outlined on this page.
GitHub issue tracker
The GitHub issue tracker of each student’s capstone project repository is used to communicate and ask questions about the Capstone Project report. Each course participant is assigned to one of the course instructors.
Submission due date
The due date for submission of the report is Tuesday, 13th February 2024.
Required items
Table 1 is a detailed list of items that need to be included for a complete submission of the capstone project report. Items are categorized into technical, data, and intellectual tasks. If any item is unclear, please reach out to the course instructors.
no | category | items |
---|---|---|
1 | technical | The report renders without errors to HTML format and contains at least five chapters of heading level 1 that are named: Introduction, Methods, Results, Conclusions, References. |
2 | technical | YAML header of report has title, author, date, and table of contents that are correctly displayed in the compiled HTML output. |
3 | technical | Warnings are hidden from the compiled output, but code is shown in the compiled output. |
4 | technical | The report has at least two data visualisations. |
5 | technical | Each data visualisation has edited human-readable labels (e.g. axis labels, legend title). |
6 | technical | Each data visualisation applies at least of one scaling function (e.g. color/fill, axes). |
7 | technical | Each data visualisation has a label defined in the code-chunk options. |
8 | technical | Each data visualisation has a caption defined in the code-chunk options. |
9 | technical | Each data visualisation is cross-referenced in the narrative using the defined label from the code-chunk options. |
10 | technical | The report has at least one table with summary statistics (e.g. count, mean, median, standard deviation, etc.). |
11 | technical | Each table is formatted in the rendered output using a function taught during the course (e.g. kable() function or gt() function). |
12 | technical | Each table has a label defined in the code-chunk options. |
13 | technical | Each table has a caption defined in the code-chunk options. |
14 | technical | Each table is cross-referenced in the narrative using the defined label from the code-chunk options. |
15 | technical | The report includes at least 3 citations using a bibliography.bib file created via RStudio Visual Editor. |
16 | technical | References are automatically listed in References section from YAML entry to bibliography.bib file. |
17 | data | Data from data/raw folder was imported, cleaned, and stored as analysis-ready processed data in data/processed folder. |
18 | data | The data/processed folder contains a data dictionary.csv file with two columns (variable_name, description) which document each variable of the data in the same folder. |
19 | data | The data/processed folder contains a README.md file from a provided template and documentation is completed for the data in the same folder. |
20 | intellectual | Introduction section with 3 to 5 sentences introduces the context within which the data was created. |
21 | intellectual | Methods section describes in 3 to 5 sentences how the data was obtained. |
22 | intellectual | Figures and tables in Results section are interpreted with 2 to 3 sentences each. |
23 | intellectual | Conclusions concisely summarize findings in a bullet point format. |