蒙纳士大学/澳大利亚

授课型硕士 · 2026

健康数据分析硕士

Master of Health Data Analytics

硕士 · M6036Master

学制

—

授课

—

学费

—

英语

—

入学要求

—

学年课程

官网写明为示例课程,以当年公布为准

生物统计学Biostatistics Studies

  • Causal inference

    EPM5018

    6
  • 导论生物信息学Introduction to Bioinformatics

    BMS5021

    6
  • Regression modelling for biostatistics II

    EPM5028

    6
  • Health indicators and health surveys

    EPM5001

    6
  • 统计机器学习Statistical machine learning

    ETC5555

    6
  • Bayesian statistical methods

    EPM5013

    6
  • Diving deeply into data exploration

    ETC5521

    6
  • 数据勘探与可视化Data exploration and visualisation

    FIT5147

    6
  • Communicating with data

    ETC5523

    6
  • 设计随机对照试验Design of randomised controlled trials

    EPM5007

    6
  • Longitudinal and correlated data analysis

    EPM5008

    6

机器学习学Machine learning studies

  • Data analysis for semi-structured data

    FIT5212

    6
  • 机器学习Machine learning

    FIT5201

    6
  • 基础人工智能Fundamentals of artificial intelligence

    FIT5047

    6

选修Machine learning elective studies

  • Diving deeply into data exploration

    ETC5521

    6
  • Regression modelling for biostatistics II

    EPM5028

    6
  • 机器学习Machine learning

    FIT5201

    6
  • 基础人工智能Fundamentals of artificial intelligence

    FIT5047

    6
  • 导论数据库Introduction to databases

    FIT9132

    6
  • Bayesian statistical methods

    EPM5013

    6
  • Communicating with data

    ETC5523

    6
  • 数据勘探与可视化Data exploration and visualisation

    FIT5147

    6
  • Data analysis for semi-structured data

    FIT5212

    6
  • 数据处理大数据Data processing for big data

    FIT5202

    6
  • 深学习Deep learning

    FIT5215

    6
  • 统计机器学习Statistical machine learning

    ETC5555

    6
  • 自然语言处理Natural language processing

    FIT5217

    6

健康数据分析通识学Health data analytics general studies

  • Health indicators and health surveys

    EPM5001

    6
  • 自然语言处理Natural language processing

    FIT5217

    6
  • 基础人工智能Fundamentals of artificial intelligence

    FIT5047

    6
  • 高级R编程Advanced R programming

    ETC5450

    6
  • 数据勘探与可视化Data exploration and visualisation

    FIT5147

    6
  • Collaborative and reproducible practices

    ETC5513

    6
  • Communicating with data

    ETC5523

    6
  • Data analysis for semi-structured data

    FIT5212

    6
  • Regression modelling for biostatistics II

    EPM5028

    6
  • 数据处理大数据Data processing for big data

    FIT5202

    6
  • Causal inference

    EPM5018

    6
  • Bayesian statistical methods

    EPM5013

    6
  • 设计随机对照试验Design of randomised controlled trials

    EPM5007

    6
  • Longitudinal and correlated data analysis

    EPM5008

    6
  • 导论生物信息学Introduction to Bioinformatics

    BMS5021

    6
  • 导论数据库Introduction to databases

    FIT9132

    6
  • 统计机器学习Statistical machine learning

    ETC5555

    6
  • 深学习Deep learning

    FIT5215

    6
  • 机器学习Machine learning

    FIT5201

    6
  • Applied forecasting

    ETC5550

    6
  • Diving deeply into data exploration

    ETC5521

    6

部分基础学Part A. Foundation studies

  • Introduction to Python programming

    FIT9136

    6
  • 统计数据建模Statistical data modelling

    FIT5197

    6
  • 入门流行病学Introductory epidemiology

    MPH5040

    6
  • 数学基础生物统计Mathematical foundations for biostatistics

    EPM5026

    6
  • Programming principles for health data analytics using Python

    EPM5033

    6
  • 导论数据分析Introduction to data analysis

    ETC5510

    6
  • Regression modelling for biostatistics 1

    EPM5027

    6
  • Data wrangling

    FIT5196

    6
  • 导论健康数据分析Introduction to health data analytics

    EPM5029

    6
  • Principles of statistical inference

    EPM5003

    6

部分B.应用学Part B. Applied studies

  • 专业实践发展Professional practice development

    MPH5289

    6
  • 导论机器学习Introduction to machine learning

    ETC5250

    6
  • 健康数据分析项目Health data analytics project

    EPM5031

    6
  • 人类健康与疾病过程Human health and disease processes

    EPM5030

    6
  • Applied health data analytics group case study

    EPM5032

    6

申请节点

—

所载课名、学制、学费与入学要求摘自学校官网当年公布。中文名为惯例译名,英文以官网为准。缺字段写作「—」,不编数字。是否适合申请、如何排冲稳保,预约梳理后另议。

PRIVATE CONSULTATION

下一步:预约一次梳理

这一条只抄官网已经写明的标准。要不要申、怎么排,预约后单独谈。

预约梳理