Use the Tab and Up, Down arrow keys to select menu items.
This course introduces students to data cleaning, standardisation, and the integration of disparate data sources and structures. Students will learn how to convert data from many different sources into a consistent format ready for analysis, and will learn about data quality, ethics, management, storage, and persistency.
Data comes in a variety of shapes and formats: text documents, images, tables, social network graphs, databases, webpages. Data is used for a variety of uses: archiving, analysis, visualization, communication, and even art. Data wrangling is the process of reshaping data so that it can be more efficiently used. The process can be difficult because it is important to preserve, as much as possible, the relevant information contained in the dataset, while at the same time ensuring an ethical treatment of the data subjects, e.g., protecting people’s security and privacy. Data scientist, thus, need to take careful decisions, and it is estimated that up to 80% of the worktime of a data scientist is spent in cleaning and wrangling data. Learning to do this efficiently, thus, proves to be essential across many discipline and industries.The course aims to provide the students with the tools to handle different sources of data (csvs, spreadsheets, web pages, apis, …), some target formats (long / wide data frames, packages, …) and a variety of data kinds (dates, numeric, strings, text, …). Wherever possible, the students will work on real-world datasets and ethical facets of data wrangling will be explicitly discussed in class. During the course, R will be the default programming language, and the use of JupyterLab and Rstudio strongly encouraged. Reference to other programming languages, e.g. Julia, will be provided. Peer, group, and class interaction will be explicitly required during the course.
Having engaged in learning during the course, students will be able to:Access (read in) different data formats;Interact (manipulate) relational dataset (e.g., data frames) and hierarchical datasets;Output (write to) different data formats;Analyse a dataset in order to identify its format and possible errors;Analyse a data wrangling problem: identify the available source format(s); define the suitable target format(s) and the relevant ethical / technical constraints; develop a flow to transform data from source to target formats.
15 Points of 100-level COSC, MATH orSTAT orINFO125
Students must attend one activity from each section.
Giulio Dalla Riva
Suggested Textbook:Stephanie Locke, Data Manipulation in R
Domestic fee $777.00
International fee $3,375.00
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
For further information see
Mathematics and Statistics.