STATS 383 : The Science and Craft of Data Management


2023 Semester Two (1235) (15 POINTS)

Course Prescription

A structured introduction to the science and craft of data management, including: data representations and their advantages and disadvantages; workflow and data governance; combining and splitting data sets; data cleaning; the creation of non-trivial summary variables; and the handling of missing data. These will be illustrated by data sets of varying size and complexity, and students will implement data processing steps in at least two software systems.

Course Overview

Applied statisticians and data scientists spend large amounts of time engaged in activities which make a raw dataset fit for downstream high-level analyses. This is a critical component of the statistician’s and data scientist’s toolkit but is also a set of valuable skills for those who collect data. Data management requires a combination of statistical and programming knowledge, but the principles are independent of the specific software tools; we use both R and SAS in this course to illustrate the differences in the approach they represent.

Course Requirements

Prerequisite: ENGSCI 314 or STATS 201 or 208, and COMPSCI 101 or ENGSCI 233 or STATS 220

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate an understanding of and appraise the different data management models used to standardise the way datasets are organised (Capability 1, 2, 3 and 4)
  2. Plan, prepare and implement a workflow for the reproducible curation and summarisation of data. (Capability 1, 2, 3, 4 and 5)
  3. Explain and evaluate the ethical and regulatory considerations integral to the development of a data management workflow. (Capability 1, 2, 4, 5 and 6)
  4. Develop and demonstrate a good understanding of the methods and best practices for bulk data transformations. (Capability 1, 2 and 3)
  5. Identify, describe and demonstrate an understanding of different data cleaning processes. (Capability 1, 2 and 3)
  6. Develop an understanding of how to construct new variables from those in a cleaned data so that they are suitable for analysis. (Capability 1, 2 and 3)
  7. Demonstrate simple transformations of text data and explain how these become more complicated in a multilingual and multicultural context. (Capability 1, 3 and 6)


Assessment Type Percentage Classification
Laboratories 40% Individual Coursework
Online test 15% Individual Coursework
Final Exam 45% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Online test
Final Exam

Labs are marked and count towards your final grade but attendance is not mandatory.


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Special Requirements

Test may be conducted outside of standard hours. 

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:

  • 3 hours of lectures
  • A 2-hour lab
  • 2 hours of reviewing the course content
  • 5.5 hours of work on lab exercises, online quiz and/or test preparation

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including labs to receive credit for this component of the course.
Lectures will be available as recordings. Other learning activities including labs will not be available as recordings.
The course will not include live online events.
The activities for the course are scheduled as a standard weekly timetable.
This course is available for remote students.

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.

Course Materials:

  • Lecture notes, lab exercises, and quizzes will be available on Canvas.

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 workload has been modified so that all internal assessments, other than the test, is through lab exercises and quizzes. I.e. no assignments in 2023.

Other Information

In 2022, internal assessments took the form of 4 assignments (25%), 10 quizzes (10%) and 1 test (15%). In 2023, assignments will be dropped from the internal assessments. Instead, there will be 10 quizzes (40%) and 1 test (15%). Each lab's 4% of marks will be awarded as follows: 1% for in-person lab attendance + 3% for quiz. Only under exceptional circumstances will students be excused from attending labs without forfeiting the 1% for in-person attendance. Lab exercises will be handed out at the start of each of weeks 1-5 and weeks 7-11. The questions in the weekly quizzes will be an interrogation of the current week's lab exercises. Quiz questions will become visible to students at the start of their 2-hour lab and will close at the end their lab session, i.e. quiz questions must be attempted during lab hours.

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 against online source material 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 02/11/2022 08:45 p.m.