Advance Course: Applied R in Data Analytics & Forecasting- Project Base

Categories: Advanced Courses
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About Course

This course focuses on applying conditional statements for data cleaning, healthcare automation (e.g., disease categorization, patient communication, critical case flagging), and 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 time series analysis (trend, seasonality, stationarity, ARIMA/SARIMA forecasting) and various statistical tests such as ANOVA, ANCOVA, 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 practical application of conditional statements, enabling participants to clean data, generate warning messages, categorize diseases, create personalized patient communications, assign medical priority, flag critical cases, and develop treatment plans within healthcare contexts.
  • Further extending these skills, learners will design remote patient monitoring systems, perform insurance risk classification, build AI chatbots, and create pharmacy drug recommendation systems using conditional logic; additionally, they will master 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.
  • Advanced topics will cover comprehensive time series analysis, focusing on identifying trends, seasonality, and stationarity, alongside practical forecasting using ARIMA and SARIMA models.
  • Finally, the training will equip participants with skills in one-way and two-way ANOVA, ANCOVA, 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

ORIENTATION VIDEO

  • Orientation
    13:32

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MODULE 2 OF 11: 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.

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MODULE 3 OF 11: DATA CLEANING & MANIPULATION (Click to see module content)

MODULE 4 OF 11: 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 11: 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 11: 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 11: 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.

MODULE 8 OF 11: QUALITATIVE DATA ANALYSIS- QDA (Click to see module content)
This module is designed to equip students with the skills to effectively interpret and analyze unstructured qualitative data. The primary objective is to move beyond mere observation, enabling students to uncover rich insights and meaningful patterns from text, interviews, and open-ended responses. Students will master techniques for identifying key themes and developing robust coding frameworks. Ultimately, this content aims to build proficiency in qualitative research methods, allowing students to support business decisions with evidence-based narratives. Upon completion, students will be able to synthesize complex information and present compelling stories hidden within the data. The final goal is to empower a new generation of data analysts who can provide a deeper, more human-centric understanding of an organization's challenges.

MODULE 9 OF 11 (ADD-ON): TIME SERIES ANALYSIS AND FORECASTING (Click to see module content)
Upon completing these modules, participants will be able to perform comprehensive time series analysis in R, including identifying and modeling trends, seasonality, and stationarity. They will also gain proficiency in forecasting future values using both ARIMA and SARIMA models.

MODULE 10 OF 11 (ADD-ON): DATA SCIENCE IN HEALTHCARE (Click to see module content)
Upon completing these modules, participants will be able to apply conditional statements in R to automate critical healthcare tasks, including disease categorization, patient message personalization, and medical priority assignment.

MODULE 1 OF 11: 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 11 OF 11: ETHICS OF DATA ANALYTICS
This course provides an extensive overview of the ethics of data analytics, covering foundational principles, practical challenges throughout the data life cycle, real-world case studies, and modern governance. It equips students to navigate complex ethical dilemmas by applying frameworks like deontology and utilitarianism. The content addresses key issues such as algorithmic bias, data privacy, and accountability. It also examines the ethical implications of data use in critical fields like healthcare and criminal justice. Finally, the course explores professional best practices and major regulations like GDPR, preparing students to develop and implement data solutions responsibly.

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