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Showing 25 course outlines from 4473 matches
3401
STATS 707
: Computational Introduction to Statistics2025 Semester One (1253)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: ENGSCI 314, STATS 201, 207, 208, 210, 225
Restriction: ENGSCI 314, STATS 201, 207, 208, 210, 225
3402
STATS 707
: Computational Introduction to Statistics2024 Semester One (1243)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: ENGSCI 314, STATS 201, 207, 208, 210, 225
Restriction: ENGSCI 314, STATS 201, 207, 208, 210, 225
3403
STATS 707
: Computational Introduction to Statistics2023 Semester One (1233)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
3404
STATS 707
: Computational Introduction to Statistics2022 Semester One (1223)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
3405
STATS 707
: Computational Introduction to Statistics2021 Semester One (1213)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
3406
STATS 707
: Computational Introduction to Statistics2020 Semester One (1203)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: STATS 201, 208, 210, 225
Restriction: STATS 201, 208, 210, 225
3407
STATS 708
: Topics in Statistical Education2025 Semester One (1253)
Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas.
No pre-requisites or restrictions
3408
STATS 708
: Topics in Statistical Education2023 Semester One (1233)
Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas.
No pre-requisites or restrictions
3409
STATS 708
: Topics in Statistical Education2021 Semester One (1213)
Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas.
No pre-requisites or restrictions
3410
STATS 709
: Predictive Modelling2025 Semester Two (1255)
Predictive modelling forecasts likely future outcomes based on historical and current data. Following an advanced introduction to statistics and data analysis, the course will discuss concepts for modern predictive modelling and machine learning.
Prerequisite: COMPSCI 130, MATHS 108, and 15 points from STATS 101, 108, or equivalent
Restriction: STATS 201, 207, 208, 210, 225, 707, 765
Restriction: STATS 201, 207, 208, 210, 225, 707, 765
3411
STATS 709
: Predictive Modelling2024 Semester Two (1245)
Predictive modelling forecasts likely future outcomes based on historical and current data. Following an advanced introduction to statistics and data analysis, the course will discuss concepts for modern predictive modelling and machine learning.
Prerequisite: COMPSCI 130, MATHS 108, and 15 points from STATS 101, 108, or equivalent
Restriction: STATS 201, 207, 208, 210, 225, 707, 765
Restriction: STATS 201, 207, 208, 210, 225, 707, 765
3412
STATS 710
: Probability Theory2025 Semester Two (1255)
Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem, modes of convergence. Advanced topics may include Poisson random measures, random trees, Lévy processes, random spatial models. Students will undertake assigned individual research projects based on a journal article or advanced textbook, including a detailed explanation of the techniques of probability theory exemplified therein.
Prerequisite: B+ or higher in STATS 225 or 15 points from STATS 310, 320, 325
3413
STATS 710
: Probability Theory2024 Semester Two (1245)
Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem.
Prerequisite: B+ or higher in STATS 225 or 15 points from STATS 310, 320, 325
3414
STATS 710
: Probability Theory2022 Semester Two (1225)
Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem.
Prerequisite: B+ or higher in STATS 225 or 15 points from STATS 310, 320, 325
3415
STATS 710
: Probability Theory2021 Semester Two (1215)
Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem.
Prerequisite: B+ or higher in STATS 225 or 15 points from STATS 310, 320, 325
3416
STATS 710
: Probability Theory2020 Semester Two (1205)
Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem.
Prerequisite: STATS 310, 320 or 325
3417
STATS 720
: Stochastic Processes2024 Semester One (1243)
Stochastic models and their applications. Discrete and continuous-time jump Markov processes. A selection of topics from point processes, renewal theory, Markov decision processes, stochastic networks, inference for stochastic processes, simulation of stochastic processes, and computational methods using R.
Prerequisite: STATS 320 or 325
3418
STATS 720
: Stochastic Processes2023 Semester One (1233)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Prerequisite: STATS 320 or 325
3419
STATS 720
: Stochastic Processes2022 Semester One (1223)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Prerequisite: STATS 320 or 325
3420
STATS 720
: Stochastic Processes2021 Semester One (1213)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Prerequisite: STATS 320 or 325
3421
STATS 720
: Stochastic Processes2020 Semester One (1203)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Prerequisite: STATS 320 or 325
3422
STATS 721
: Foundations of Stochastic Processes2025 Semester Two (1255)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Prerequisite: 15 points from STATS 125, 210, 225, 320 with at least a B+ and 15 points from MATHS 208, 250, 253
Restriction: STATS 325
Restriction: STATS 325
3423
STATS 721
: Foundations of Stochastic Processes2024 Semester Two (1245)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Prerequisite: 15 points from STATS 125, 210, 225, 320 with at least a B+ and 15 points from MATHS 208, 250, 253
Restriction: STATS 325
Restriction: STATS 325
3424
STATS 721
: Foundations of Stochastic Processes2023 Semester Two (1235)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Restriction: STATS 325
3425
STATS 721
: Foundations of Stochastic Processes2022 Semester Two (1225)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Restriction: STATS 325
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