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Learn to Program: R

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R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. … Continue reading

Data Capture and Surveys with REDCap

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This course introduces you to REDCap, a rapidly evolving web tool developed by researchers for researchers. About this event Would you like to enable secure and reliable data collection forms … Continue reading

Understanding and analysis of proteomic data

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This event is funded by Intellectual Climate Fund La Trobe University About this event In almost all living cells, proteins play a central role in majority of cellular functions and their alterations can lead to disease. Proteomics using mass-spectrometry is one of the –omics disciplines routinely utilized to identify, characterize and quantify proteins in complex … Continue reading

Longitudinal Trials with REDCap

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Learn how to run longitudinal data collection, such as surveys with post-intervention and follow-up questionnaires using REDCap About this event REDCap is a powerful and extensible application for managing and … Continue reading

An introduction to meta-analysis with applications in R

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This event is funded by Intellectual Climate Fund La Trobe University About this event Meta-analysis is the process of combining the estimates (effects) from several different studies to obtain a single estimated effect. By pooling these effects, we can increase power and explore new research questions. While conducting a meta-analysis in R is not a … Continue reading

Application of Machine Learning in Agriculture and Livestock Production

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This event is funded by Intellectual Climate Fund La Trobe University About this event Machine learning is the field of study that gives computers the ability to learn from data without being explicitly programmed (Arthur Samuel, AI pioneer, 1959). Today, applications of machine learning are everywhere and are increasingly growing. Machine learning approaches don’t require … Continue reading