Statistical Analysis in R
Enhance your data analysis skills with our Applied Statistical Data Analysis Using R course. Improve your career prospects in various fields.
What you will learn
- Basics of statistical inference, confidence intervals and hypothesis testing. Commonly used tests. Pvalues, statistical and practical significance.
- Analysis of Variance (ANOVA) and post-hoc tests. Diagnostics, implementation and interpretation using R.
- Numerical Methods: The use of simulations, nonparametric bootstrap and permutation tests using R.
- Linear Regression, Analysis of Variance with Covariates (ANCOVA), Generalised Linear Models (GLMs) and Mixed Effects Linear models using R.
- Basics of power analysis (sample size evaluation) and some thoughts on experimental design.
- The data analysis skills provided by this course will improve your employment and career prospects, whatever your area or interest.
- Data analytic skills are widely employed in epidemiology, ecology, forestry, agriculture, meteorology, marketing, (and pretty much everywhere).
- Data analysts work in governmental organisations, the private sector and various branches of education.
- Being data and numerically literate will also improve your understanding of other people’s results and make you a better communicator and decision maker.
Program Overview
Applied Statistical Data Analysis Using R professional certificate is directed at people with limited statistical background and no practical experience who have to do data analysis, as well as those who are “out of practice”. The course is practice orientated, aiming to give learners an understanding of why the method works, how to implement it using R, when to apply it and where to look if the particular method is not applicable in the specific situation.
Courses in this program
Basics of Statistical Inference and Modelling Using R
Learn why a statistical method works, how to implement it using R and when to apply it and where to look if the particular statistical method is not applicable in the specific situation.
Advanced Statistical Inference and Modelling Using R
Extend your knowledge of linear regression to the situations where the response variable is binary, a count, or categorical as well as to hierarchical experimental set-up.
Job Outlook
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