161220

Data Analysis

Understanding, visualising and analysing data in a practical context using R/RStudio. Topics are selected from: data collection including experimental designs, observational studies, and surveys, data cleaning and preparation, exploratory analysis, visualisation of multivariate and time series data, regression, analysis of variance and covariance, autoregressive models and categorical data modelling.

Course code

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

161220

Level

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).

200-level

Credits

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

15

Course planning information

Course notes

Access to a computer and an approved statistics package are required for analysis of data.

Expected prior learning

Students with a high level of attainment in NCEA Level 3 Mathematics with Statistics may apply to enrol in 161.220 directly.

Prerequisite courses

Complete first

You need to complete the above course or courses before moving onto this one.

Restrictions

Choose just one

The courses listed above have similar content to this one meaning you can only enrol in this course or one of the listed courses. Only one of the courses can be credited towards your qualification.

General progression requirements

You must complete at least 45 credits from 100-level before enrolling in 200-level courses.

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 Pre-process, summarise and interpret different types of data and how they are gathered.
  • 2 Explore and discover patterns in both cross section and time series data using a range of exploratory data techniques.
  • 3 Apply standard inference techniques to analyse data, including transformations and explain the limitations and validate using resampling and nonparametric methods.
  • 4 Analyse data by fitting simple, multiple linear and autoregressive models, assessing and improving model fit.
  • 5 Analyse data from standard experimental designs using analysis of variance techniques.
  • 6 Use standard techniques for analysing categorical and count data.

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

Assessments

Assessment Learning outcomes assessed Weighting
Written Assignment 1 2 15%
Written Assignment 3 4 15%
Written Assignment 4 5 15%
Exam College/GRS-based (not centrally scheduled) 1 2 3 4 5 6 55%

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.
Participation
You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
Portfolio
Creative, learning, online, narrative, photographic, written, and other portfolios.
Practical or placement
Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
Simulation
Technology-based or experience-based simulations.
Test
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.

Course delivery details

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