Statistical Data Science

An introduction to computer programming and statistics for transforming, visualising and modelling data to discover information and support decision making. A practical approach to analysing New Zealand data includes data cleaning, statistical summaries, data wrangling, visualisation and predictive modelling. Includes an exploration of the statistical ideas of sampling, probability and inference as well as modern programming tools emphasising reproducibility.

Course code

Qualifications are made up of courses. Some universities call these papers. Each course is numbered using six digits.



The fourth number of the course code shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).



Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.



Course planning information

Expected prior learning

At least 16 credits in NCEA Level 3 Mathematics from these standards: 91573, 91574, 91575, 91576, 91577, 91578, 91579, 91587; or passed any 100-level mathematics course (prefix 160) except 160.104. If you do not have this prior learning or equivalent you should enrol in this Massey University course instead: 160.105 Methods of Mathematics If it is some time since you studied Mathematics at school, you can find out if you have the expected background by taking this maths quiz .


Similar content

You cannot enrol in this course if you have passed (or are enrolled in) any of the course(s) above as these courses have similar content or content at a higher level.

Learning outcomes

What you will learn. Knowledge, skills and attitudes you’ll be able to show as a result of successfully finishing this course.

  • 1 Write computer programs to summarise and visualise complex data sets in a reproducible manner and communicate results in context.
  • 2 Recognise tidy data and write code for data wrangling.
  • 3 Analyse and interpret relationships between variables.
  • 4 Use statistical models for inference of populations parameters, interpreting these in context.
  • 5 Critique a data collection process and its influence on analysis and inference.

Learning outcomes can change before the start of the semester you are studying the course in.


Assessment Learning outcomes assessed Weighting
Written Assignment 1 2 3 20%
Written Assignment 1 2 3 5 20%
Written Assignment 1 2 3 4 5 20%
Portfolio 1 2 3 4 5 40%

Assessment weightings can change up to the start of the semester the course is delivered in.

You may need to take more assessments depending on where, how, and when you choose to take this course.

Explanation of assessment types

Computer programmes
Computer animation and screening, design, programming, models and other computer work.
Creative compositions
Animations, films, models, textiles, websites, and other compositions.
Exam College or GRS-based (not centrally scheduled)
An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format.
Exam (centrally scheduled)
An exam scheduled by Assessment Services (centrally) – you’ll usually be told when and where the exam is through the student portal.
Oral or performance or presentation
Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations.
You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
Creative, learning, online, narrative, photographic, written, and other portfolios.
Practical or placement
Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
Technology-based or experience-based simulations.
Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school.
Written assignment
Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments.

Textbooks needed

There are no set texts for this course.