Quantitative Data Analysis in R Programming Language

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About Course

This course focuses on mastering advanced data structures like datatables and tibbles, alongside list, date, and time manipulation. It covers comprehensive regression analysis, including linear, logistic, Poisson, and negative binomial models, emphasizing assumption validation. Additionally, the curriculum delves into various statistical tests such as ANOVA and non-parametric methods, all reinforced through practical application-based assessments.

What Will You Learn?

  • A significant portion of the course will focus on the higher-level data structures such as datatables, tibbles, and advanced list manipulation.
  • The curriculum also includes mastering date and time manipulation with lubridate and implementing advanced piping techniques for streamlined data workflows. Participants will delve into various regression analyses, including linear regression with its critical assumptions (linearity, independence of errors, normality of residuals, homoscedasticity, no multicollinearity), binary and multinomial logistic regression, Poisson regression, and negative binomial regression for count data.
  • Finally, the training will equip participants with skills in one-way and two-way ANOVA and non-parametric tests like Wilcoxon, Wilcoxon Signed-Rank, and Kruskal-Wallis, culminating in extensive application-based assessments to solidify their understanding of advanced data analysis.

Course Content

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MODULE 1 OF 7: FOUNDATIONAL COURSE REVISION (Click to see module content)
A foundational course in R programming for data analytics typically covers the essential skills needed to manipulate, analyze, and visualize data using R.

MODULE 2 OF 7: R MARKDOWN- ADDING BEAUTY TO YOUR CODE PRESENTATION (Click to see module content)
This series aims to equip users with the skills to leverage R Markdown for creating professional and visually appealing code presentations. Objectives include mastering file management and knitting processes to generate HTML, Word, and PDF documents. Furthermore, the series will cover techniques for enhancing the aesthetic appeal of R Markdown outputs, including image insertion, clickable links, and basic website development principles, alongside efficient handling of code execution, tables, and graphs.

MODULE 3 OF 7: DATA CLEANING & MANIPULATION (Click to see module content)

MODULE 4 OF 7: QUANTITATIVE DATA ANALYSIS 1 (Click to see module content)
Upon completing this module, participants will be able to effectively analyze and present relationships between variables by calculating and interpreting correlation coefficients in both tabular and visual formats, utilizing ggplot2 for compelling data visualizations. Also, participants will be able to build, interpret, and validate various regression models in R, including linear, binary logistic, multinomial, Poisson, and negative binomial regressions for count data. They will also gain proficiency in assessing the critical assumptions of linear regression, such as linearity, independence of errors, normality of residuals, homoscedasticity, and the absence of multicollinearity, to ensure robust model performance.

MODULE 5 OF 7: QUANTITATIVE DATA ANALYSIS 2 (Click to see module content)
Upon completing this module, participants will be able to effectively analyze and present relationships between variables by calculating and interpreting correlation coefficients in both tabular and visual formats, utilizing ggplot2 for compelling data visualizations. Also, participants will be able to build, interpret, and validate various regression models in R, including linear, binary logistic, multinomial, Poisson, and negative binomial regressions for count data. They will also gain proficiency in assessing the critical assumptions of linear regression, such as linearity, independence of errors, normality of residuals, homoscedasticity, and the absence of multicollinearity, to ensure robust model performance.

MODULE 6 OF 7: PARAMETRIC TEST (Click to see module content)
Upon completing these modules, participants will be able to apply various statistical tests in R to compare group means and distributions. They will gain proficiency in performing one-way ANOVA and two-way ANOVA for parametric data, as well as non-parametric tests such as Wilcoxon, Wilcoxon Signed-Rank, and Kruskal-Wallis for non-normally distributed data.

MODULE 7 OF 7: NON-PARAMETRIC TEST (Click to see module content)
Upon completing these modules, participants will be able to apply various statistical tests in R to compare group means and distributions. They will gain proficiency in performing one-way ANOVA, two-way ANOVA, and ANCOVA for parametric data, as well as non-parametric tests such as Wilcoxon, Wilcoxon Signed-Rank, and Kruskal-Wallis for non-normally distributed data.

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