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Joanna Dipnall
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Dr Joanna Dipnall is an applied statistician with particular interests in the advanced statistical methods and machine and deep learning techniques. She completed her Honours in Econometrics with Monash University and PhD with IMPACT SRC, School of Medicine, Deakin University. Joanna works extensively with registry and linked medical data and collaborates extensively with the Faculty of IT at Monash to supervise Masters and PhD students to integrate AI within health research.
Courses taught by this instructor
Introduction to Python for Social Sciences: Online (2 days)
A step-by-step, interactive introduction to Python for participants with no experience with this software package.
Introduction to Mixed Effects Modelling: Online - (2 days)
This course is designed as an introduction to mixed effects modelling. These models involve data arising from longitudinal studies or studies where the data exhibits some form of hierarchy, and sometimes referred to as multilevel modelling.
Data Analysis Using Stata: Online
This course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool.
Introduction to Survival Analysis: Online - (2 days)
Survival analysis is used to find out the time it takes for an event of interest to occur (e.g., death, birth, recidivism). This course is designed as an introduction to survival analysis.
Introduction to R: Online - (3 days)
This masterclass offers a step-by-step, interactive introduction to R and RStudio for participants with no experience with these software packages.
Predictive Analytics for Data Science: Linear and Non-Linear Modelling: Online - (2 days)
This masterclass is an introduction to linear and non-linear predictive models. It will provide an interactive step-by-step guide to running these models and key diagnostics using the R software platform.
Multivariate Analysis of Variance (MANOVA)- (2 days)
Multivariate analysis of variance (MANOVA) is an extension of analysis of variance (ANOVA) to deal with two or more continuous outcomes or dependent variables. This technique is used to determine whether multiple levels of independent variables on their own or in combination with one another have an effect on the outcome variables.
Applied Multivariable Statistical Analysis: Online (3 Day)
This course aims to give you an understanding of different multivariable analysis techniques.
Machine Learning for Data Science: Supervised Learning Techniques: Online (3 days)
An introduction to supervised machine learning techniques for data science, providing an interactive step-by-step guide to running some of the standard statistical regression and classification machine learning models that every data scientist should know.
Data Analysis Using Stata
This course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool.
Introduction to Time-Series Analysis using Stata - (2 days)
Time-series analysis is gaining popularity across many disciplines for its ability to detect trends. This course takes a practical applied approach (rather than mathematical) and participants are provided with detailed, hands-on experience in running basic time-series analysis using Stata.
Introduction to Structural Equation Modelling using Stata - (2 days)
Designed as an applied introduction to SEM using Stata, this course aims to provide participants with a sound understanding of when to use SEM and how to assess and report their models
Structural Equation Modelling using Stata: Online - (2 days)
Designed as an applied introduction to SEM using Stata, this course aims to provide participants with a sound understanding of when to use SEM and how to assess and report their models.