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Organiser: Rebecca Barter, Division of... 

  • Python for R Users with Rebecca Barter from the Division of Epidemiology, University of Utah School of Medicine

    14 November 2024

    Python for R Users with Rebecca Barter from the Division of Epidemiology, University of Utah School of Medicine https://dresa.org.au/events/python-for-r-users-with-rebecca-barter-from-the-division-of-epidemiology-university-of-utah-school-of-medicine This workshop is designed to help researchers fluent in R, particularly with the tidyverse, transition easily to Python, enhancing their data analysis skills across a range of fields. Participants will gain a robust understanding of Python's libraries and data structures, enabling them to integrate Python into their research workflows efficiently and expand their analytical capabilities, including with Python's substantial toolkits for machine learning and deep learning such as Scikit-learn and Keras. 2024-11-14 04:00:00 UTC 2024-11-14 11:00:00 UTC Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine Instats info@instats.org [] [] [] open_to_all []
  • Introduction to R for Data Analysis with Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine

    16 - 17 January 2025

    Introduction to R for Data Analysis with Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine https://dresa.org.au/events/introduction-to-r-for-data-analysis-with-rebecca-barter-division-of-epidemiology-university-of-utah-school-of-medicine This seminar provides a comprehensive introduction to R programming for data analysis, tailored to social scientists, epidemiologists, and researchers across various fields. Participants will learn essential skills in data manipulation, visualization, and statistical analysis, enabling them to improve their research methodologies by effectively using R. 2025-01-16 04:00:00 UTC 2025-01-17 10:00:00 UTC Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine Instats info@instats.org [] [] [] open_to_all []
  • Python for R Users with Rebecca Barter from the Division of Epidemiology, University of Utah School of Medicine

    14 November 2024

    Python for R Users with Rebecca Barter from the Division of Epidemiology, University of Utah School of Medicine https://dresa.org.au/events/python-for-r-users-with-rebecca-barter-from-the-division-of-epidemiology-university-of-utah-school-of-medicine This workshop is designed to help researchers fluent in R, particularly with the tidyverse, transition easily to Python, enhancing their data analysis skills across a range of fields. Participants will gain a robust understanding of Python's libraries and data structures, enabling them to integrate Python into their research workflows efficiently and expand their analytical capabilities, including with Python's substantial toolkits for machine learning and deep learning such as Scikit-learn and Keras. 2024-11-14 04:00:00 UTC 2024-11-14 11:00:00 UTC Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine Instats info@instats.org [] [] [] open_to_all []
  • Introduction to R for Data Analysis with Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine

    16 - 17 January 2025

    Introduction to R for Data Analysis with Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine https://dresa.org.au/events/introduction-to-r-for-data-analysis-with-rebecca-barter-division-of-epidemiology-university-of-utah-school-of-medicine This seminar provides a comprehensive introduction to R programming for data analysis, tailored to social scientists, epidemiologists, and researchers across various fields. Participants will learn essential skills in data manipulation, visualization, and statistical analysis, enabling them to improve their research methodologies by effectively using R. 2025-01-16 04:00:00 UTC 2025-01-17 10:00:00 UTC Rebecca Barter, Division of Epidemiology, University of Utah School of Medicine Instats info@instats.org [] [] [] open_to_all []

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