STATS 220 : Data Technologies


2024 Semester One (1243) (15 POINTS)

Course Prescription

Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.

Course Overview

This course introduces R programming and the tidyverse eco system of R packages to handle a wide variety of data science tasks, from generating, importing, wrangling, and visualising data, to reproducible reporting. Students will learn how to use computers to store data and process different file formats, access modern adaptable data sources, and apply related data technologies, for the purpose of effective data-driven decision-making and solving data-analytical problems in both business and research environments. Students will also learn how to think creatively with data technologies, combine data technologies to solve problems, and communicate about data science products.

Course Requirements

Prerequisite: 15 points at Stage I in Computer Science or Statistics

Capabilities Developed in this Course

Capability 1: People and Place
Capability 2: Sustainability
Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Capability 7: Collaboration
Capability 8: Ethics and Professionalism
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Undertake a broad variety of data science tasks (Capability 3, 4, 5 and 6)
  2. Demonstrate understanding of programming concepts and skills associated with importing, wrangling and visualising data for decision making, using R (Capability 3, 4 and 5)
  3. Describe tidy data principles, grammar of data manipulation and grammar of graphics (Capability 3)
  4. Develop communication skills, including using reproducible reporting with R Markdown (Capability 6, 7 and 8)
  5. Select and combine a range of data technologies, including HTML and CSS for reporting and web scraping (Capability 3, 4, 5, 6 and 8)
  6. Apply good practice of project-oriented workflow and data-related responsibilities including ethical considerations (Capability 1, 2, 3, 4 and 8)


Assessment Type Percentage Classification
Labs 10% Individual Coursework
Projects 30% Individual Coursework
Test 20% Individual Coursework
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Final Exam


Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at

Statistics has a Tuākana Programme where there is a workspace and a social space shared with Science Tuakana students. Tutorials and one-to-one assistance are available. Tuākana tutors/mentors work alongside the lecturer to support students with assignments and revision for the quizzes and exams. For more information and to find contact details for the Statistics Tuākana coordinator, please see
Contacts are Susan Wingfield ( and Heti Afimeimounga (

Key Topics

  • Module 1: Creating HTML by combining modern technologies
  • Module 2: Creating web-based dynamic reporting systems
  • Module 3: Creating new variables and data structures
  • Module 4: Creating visual-based communications
  • Module 5: Creating data from digital sources
  • Module 6: Creating automated code-driven processes

Special Requirements

  • Lab sessions have been timetabled to provide assistance with weekly lab tasks and quizzes, and the projects, but attendance is not mandatory.
  • The test will be computer-based and may be held at a time other than the standard lecture time, including in the evening.
  • The final exam will be computer-based.

Workload Expectations

This course is a standard 15-point course and students are expected to spend 150 hours per semester involved in each 15-point course that they are enrolled in.

For this course, a typical weekly workload includes:

  • 2 hours of lectures
  • A 1-hour lab session
  • 4-5 hours of reading and thinking about the content
  • 4-5 hours of work on projects and/or test/exam preparation

Delivery Mode

Campus Experience

  • Lectures will be available as recordings. 
  • Lab sessions will not be available as recordings.
  • The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

  • Latest R (freely available from and contributed packages as needed
  • RStudio IDE (freely available from

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

For example, the due dates for projects will be changed to better align with the timing of the lab sessions and student office hours. Additionally, some additional material may be made available to complement the content provided in the lecture slides and online lab tasks.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 01/11/2023 10:23 a.m.