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DATA1001 is a Level I Mathematics course.

Units of Credit: 6

Cycle of offering: Term 2

More information:

°Õ³ó±ðÌýCourse Outline (pdf) contains information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

ÁñÁ«¹ÙÍø Plagiarism Policy

The University requires all students to be aware of its .

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

°Õ³ó±ðÌý, contains up-to-date timetabling information.

If you are currently enrolled in DATA1001, you can log into  for this course.

This course will be taught as a number of distinct, but related, topics covering the fundamentals of data science as it is applied in mathematics and statistics,computer science, and economics. The course will be pitched at a level accessible for students as a general education elective and it forms a platform for students wishing to undertake further studies in data science. The course will provide an introduction to topics such as data analytics, data mining, Bayesian statistics, statistical software, econometrics, machine learning, business forecasting.

The course is jointly taught by three schools: Mathematics and Statistics, Business, and Computer Science and Engineering, each covering the basics of Data Science.

Business: Descriptive statistics and basic econometric modelling, taught in several modern contexts.

Computer Science and Engineering: Relational database model, the MapReduce parallelizable processing algorithm, spatial data and graph data

Mathematics and Statistics: Introduction to Bayesian probability, topic modelling and variable selection for high-dimensional structured data.