Year
2021
Units
4.5
Contact
2 x 50-minute lectures weekly
1 x 50-minute workshop weekly
1 x 90-minute computer lab weekly
Enrolment not permitted
1 of BUSN1009, BUSN1209, STAT1122, STAT1201, STAT1202, STAT1412, STAT8721 has been successfully completed
Topic description
  1. Design of experiments: experiments, observational studies, sample surveys; measurements & variables; replication & pseudo-replication
  2. Descriptive statistics: graphical & numerical summaries; the shape of a distribution; data screening & outliers
  3. Exploring relationships: predictor-response data; the least-squares line; residuals & transformations; prediction; the sample correlation coefficient; time series
  4. Probability: basic concepts; conditional probability & independence; random variables, the binomial distribution, the normal distribution
  5. Statistical inference: samples & populations; estimation, confidence intervals, hypothesis testing; inference for normal samples (one-sample); inference for proportion
  6. Data management including selection, sampling and cleaning; big data storage and management issues; tools and techniques for exploratory data analysis and prediction; limitations and interpretations of inductive inference and case studies
Educational aims

This topic is directed towards students with little quantitative experience.

It serves as an introduction to the interdisciplinary and emerging field of data science. Students will learn how to combine tools and techniques from statistics, computer science and data visualization. It aims to impart an understanding of the key issues in the analysis of statistical data together with practical experience in using a modern statistical package to perform elementary statistical analysis in a wide range of applications

Expected learning outcomes
On completion of this topic you will be expected to be able to:

  1. Gain an understanding of the statistical concepts and techniques presented
  2. Acquire quantitative confidence
  3. Gain an understanding of the data management, visualization and preservation of large collections of data

Key dates and timetable

(1), (2)

Each class is numbered in brackets.
Where more than one class is offered, students normally attend only one.

Classes are held weekly unless otherwise indicated.

FULL

If you are enrolled for this topic, but all classes for one of the activities (eg tutorials) are full,
contact your College Office for assistance. Full classes frequently occur near the start of semester.

Students may still enrol in topics with full classes as more places will be made available as needed.

If this padlock appears next to an activity name (eg Lecture), then class registration is closed for this activity.

Class registration normally closes at the end of week 2 of each semester.

Classes in a stream are grouped so that the same students attend all classes in that stream.
Registration in the stream will result in registration in all classes.
  Unless otherwise advised, classes are not held during semester breaks or on public holidays.