90 DAYS COURSE: Advanced Course in Data Analytics with R Programming Language and Forecasting

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; 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.