DATA422-22S2 (D) Semester Two 2022 (Distance)

Data Wrangling for Data Science

15 points

Start Date: Monday, 18 July 2022
End Date: Sunday, 13 November 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 31 July 2022
  • Without academic penalty (including no fee refund): Sunday, 2 October 2022


This course develop students skills in data cleaning and processing, data integration techniques and implementing data wrangling workflows for a real world datasets.

This occurrence of the course is for approved online students only. On-campus students should enrol in the (C) occurrence of this course.

Data wrangling is the iterative process of transforming data from a source format into a format suitable for storage, analysis, visualization, communication. The process is constrained by the requirement of preserving as much of the relevant information contained in the dataset, as well as ensuring an ethical treatment of the data subject, e.g., protecting their security and privacy. The course aims to provide the students the tools to handle different source formats (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 (Python, Julia) will be provided. Peer, group, and class interaction will be explicitly required during the course.

Learning Outcomes

  • 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 dataset (e.g., JSON);
  • 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.


Subject to approval of the Head of Department of Mathematics and Statistics.

Course Coordinator / Lecturer

Giulio Dalla Riva


Heyang (Thomas) Li

Textbooks / Resources

Recommended Reading

Locke, Stephanie; Data manipulation in R ; [2 edition] ; Colour version; Locke Data, 2017.

Indicative Fees

Domestic fee $1,051.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

All DATA422 Occurrences

  • DATA422-22S2 (C) Semester Two 2022
  • DATA422-22S2 (D) Semester Two 2022 (Distance)