# Search Course Outline

### Showing 25 course outlines from 3694 matches

2701

#### STATS 210

: Statistical Theory2022 Summer School (1220)

Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.

Prerequisite: 15 points from ENGSCI 111, ENGGEN 150, STATS 125
Corequisite: 15 points from MATHS 208, 250, ENGSCI 211 or equivalent

2702

#### STATS 210

: Statistical Theory2021 Semester Two (1215)

Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.

Prerequisite: 15 points from ENGSCI 111, ENGGEN 150, STATS 125
Corequisite: 15 points from MATHS 208, 250, ENGSCI 211 or equivalent

2703

#### STATS 210

: Statistical Theory2021 Semester One (1213)

Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.

Prerequisite: 15 points from ENGSCI 111, ENGGEN 150, STATS 125
Corequisite: 15 points from MATHS 208, 250, ENGSCI 211 or equivalent

2704

#### STATS 210

: Statistical Theory2021 Summer School (1210)

Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.

2705

#### STATS 210

: Statistical Theory2020 Semester Two (1205)

Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and masters degree in statistics.

2706

#### STATS 210

: Statistical Theory2020 Semester One (1203)

Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and masters degree in statistics.

2707

#### STATS 220

: Data Technologies2024 Semester One (1243)

Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.

Prerequisite: 15 points at Stage I in Computer Science or Statistics

2708

#### STATS 220

: Data Technologies2023 Semester One (1233)

Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.

Prerequisite: 15 points at Stage I in Computer Science or Statistics

2709

#### STATS 220

: Data Technologies2022 Semester One (1223)

Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.

Prerequisite: 15 points at Stage I in Computer Science or Statistics

2710

#### STATS 220

: Data Technologies2021 Semester One (1213)

Prerequisite: 15 points at Stage I in Computer Science or Statistics

2711

#### STATS 220

: Data Technologies2020 Semester One (1203)

Prerequisite: 15 points at Stage I in Computer Science or Statistics

2712

#### STATS 225

: Probability: Theory and Applications2024 Semester One (1243)

Covers the fundamentals of probability through theory, methods, and applications. Topics should include the classical limit theorems of probability and statistics known as the laws of large numbers and central limit theorem, conditional expectation as a random variable, the use of generating function techniques, and key properties of some fundamental stochastic models such as random walks, branching processes and Poisson point processes.

Prerequisite: B+ or higher in ENGGEN 150 or ENGSCI 111 or STATS 125, or a B+ or higher in MATHS 120 and 130
Corequisite: 15 points from ENGSCI 211, MATHS 208, 250

2713

#### STATS 225

: Probability: Theory and Applications2023 Semester One (1233)

Covers the fundamentals of probability through theory, methods, and applications. Topics should include the classical limit theorems of probability and statistics known as the laws of large numbers and central limit theorem, conditional expectation as a random variable, the use of generating function techniques, and key properties of some fundamental stochastic models such as random walks, branching processes and Poisson point processes.

Prerequisite: B+ or higher in ENGGEN 150 or ENGSCI 111 or STATS 125, or a B+ or higher in MATHS 120 and 130
Corequisite: 15 points from ENGSCI 211, MATHS 208, 250

2714

#### STATS 225

: Probability: Theory and Applications2022 Semester One (1223)

Covers the fundamentals of probability through theory, methods, and applications. Topics should include the classical limit theorems of probability and statistics known as the laws of large numbers and central limit theorem, conditional expectation as a random variable, the use of generating function techniques, and key properties of some fundamental stochastic models such as random walks, branching processes and Poisson point processes.

Prerequisite: B+ or higher in ENGGEN 150 or ENGSCI 111 or STATS 125, or a B+ or higher in MATHS 120 and 130
Corequisite: 15 points from ENGSCI 211, MATHS 208, 250

2715

#### STATS 225

: Probability: Theory and Applications2021 Semester One (1213)

2716

#### STATS 225

: Probability: Theory and Applications2020 Semester One (1203)

Prerequisite: 15 points from ENGGEN 150, ENGSCI 111, STATS 125 with a B+ or higher, or MATHS 120 and 130 with a B+ or higher
Corequisite: 15 points from MATHS 250, ENGSCI 211 or equivalent

2717

#### STATS 240

: Design and Structured Data2024 Semester Two (1245)

An introduction to research study design and the analysis of structured data. Blocking, randomisation, and replication in designed experiments. Clusters, stratification, and weighting in samples. Other examples of structured data.

Prerequisite: STATS 101 or 108

Restriction: STATS 340

Restriction: STATS 340

2718

#### STATS 240

: Design and Structured Data2023 Semester Two (1235)

An introduction to research study design and the analysis of structured data. Blocking, randomisation, and replication in designed experiments. Clusters, stratification, and weighting in samples. Other examples of structured data.

Prerequisite: STATS 101 or 108

Restriction: STATS 340

Restriction: STATS 340

2719

#### STATS 240

: Design and Structured Data2022 Semester Two (1225)

An introduction to research study design and the analysis of structured data. Blocking, randomisation, and replication in designed experiments. Clusters, stratification, and weighting in samples. Other examples of structured data.

Prerequisite: STATS 101 or 108

Restriction: STATS 340

Restriction: STATS 340

2720

#### STATS 240

: Design and Structured Data2021 Semester Two (1215)

Prerequisite: STATS 101 or 108

Restriction: STATS 340

Restriction: STATS 340

2721

#### STATS 240

: Design and Structured Data2020 Semester Two (1205)

Prerequisite: STATS 101 or 108

Restriction: STATS 340

Restriction: STATS 340

2722

#### STATS 255

: Optimisation and Data-driven Decision Making2024 Semester One (1243)

Explores methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualisation will be considered.

Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208

Restriction: ENGSCI 255

Restriction: ENGSCI 255

2723

#### STATS 255

: Optimisation and Data-driven Decision Making2023 Semester One (1233)

Explores methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualisation will be considered.

Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208

Restriction: ENGSCI 255

Restriction: ENGSCI 255

2724

#### STATS 255

: Optimisation and Data-driven Decision Making2022 Semester One (1223)

Explores methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualisation will be considered.

Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 150 or 153 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208

Restriction: ENGSCI 255

Restriction: ENGSCI 255

2725

#### STATS 255

: Optimisation and Data-driven Decision Making2021 Semester One (1213)

Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 120 or 130 or 150 or 153 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208

Restriction: ENGSCI 255

Restriction: ENGSCI 255

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