ENVSCI 705 : Handling Environmental Data


2024 Semester One (1243) (15 POINTS)

Course Prescription

Contemporary approaches to understanding and analysing environmental data with an emphasis on developing skills to support the ‘transformation, visualisation, modelling’ cycle. The importance of adopting reproducible research practices (eg, data and code archiving) will be emphasised. The course focuses on an applied laboratory component and will be taught in open-source software. Assessment will be via projects analysing environmental data. No formal prerequisites but an understanding of basic statistical methods equivalent to STATS 101 will be presumed.

Course Overview

The sciences are awash in data, and environmental science is no different. Whereas in the past individual scientists might have collected their own data over their careers, working either independently or in small collaborations, this is no longer always the case. Instead, large databases allow synthesis-driven science, and computational advances open up new avenues for the analysis of such data. However, these new approaches to science also mean that scientists need to develop new skills in manipulating and analyzing large data, visualizing it effectively, and being aware of the uncertainties it carries. This course is designed to equip you with some of these skills. The course is organized as a series of studio-taught workshops combining lectures and interactive code development.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

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: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Manipulate and visualise environmental data and associated uncertainties using coding skills developed in studio workshop exercises and assignments (Capability 3, 4, 5, 6 and 8)
  2. Communicate the outcomes of the analysis of environmental data (Capability 4, 5, 6 and 8)
  3. Produce a reproducible data workflow that demonstrates understanding of the principles of reproducible research practice (Capability 3, 4, 5, 6, 7 and 8)
  4. Develop, design and justify a data analysis pipeline using the technical skills developed during the course. (Capability 3, 4, 5 and 8)


Assessment Type Percentage Classification
Assignments 50% Individual Coursework
Coding exercises 50% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Coding exercises


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

As part of the University-wide Tuākana community, The School of Environment Tuākana Programme aims to provide a welcoming learning environment for and enhance the success of, all of our Māori and Pacific students. We are led by the principles of tautoko (support) and whanaungatanga (connection) and hope you find a home here at the School. Students who have identified as Māori and/or Pacific will receive an invitation to our online portal introducing the Programme, the resources we have available, and how you can get involved. This course is supported by our Programme Coordinator, Kaiāwhina/Māori student adviser, and Pacific student adviser. They are able to organize group study and facilitate direct assistance regarding material taught in this course. 

Key Topics

Note that ENVSCI 705 does not aim to teach statistical methods, although we will touch on this – rather, it is concerned with how to manage and wrangle environmental data, with statistical analysis as one of many possible outcomes.

Special Requirements

We will use the free and open-source R IDE, RStudio, which runs on Linux, Windows, and Mac OS. You will need to bring a laptop to each teaching session. Before the first session, please ensure you have the latest version of R and RStudio installed on your machine. This is important, as some packages used in the workshop may not install correctly (or at all) if R and RStudio are not current.

Workload Expectations

This course is a standard 15-point course and students are expected to spend 10 hours per week involved in each 15-point course that they are enrolled in.

For this course, you can expect 30 hours of workshops, 10 hours of reading and thinking about the content and 80 hours of work on assignments, the report, and associated skills development.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including studios to complete components of the course. Lectures will be available as recordings. Other learning activities including tutorials will be available as recordings. The course will not include live online events. 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.

We will draw on the living e-books (available online for free):

  • Irizzary, R.A. (2023). Data Wrangling and Visualization with R. Routledge. [URL: http://rafalab.dfci.harvard.edu/dsbook-part-1/]
  • Wickham, H. Cęntinkaya-Rundel, M., & Grolemund, G. (2023) R for Data Science: Import, Tidy, Transform,  Visualize, and Model Data, First edition. O’Reilly. [URL: https://r4ds.hadley.nz/]

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.

 The content of this course is updated each year according to student feedback.

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 http://disability.auckland.ac.nz

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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.


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 10:22 a.m.