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Data Analysis Course Using R

About the Course

R is a programming language and free software environment for statistical computing and beautiful data visualisation. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques. Being a full programming language, R is highly extensible.

R is now widely regarded as the best software for statistical analysis and data science. It is a fast, powerful statistical package designed by statisticians for data analysts of all disciplines. With Base R and a library of packages, the analyst has everything for data management, analysis and data visualisation. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. 

In this course, participants will experience the desired qualities and functionalities that make R widely preferred. R is absolutely free. It compiles and runs on a wide variety of UNIX platforms, Windows and macOS. Our facilitators are experienced, intentional, interactive and friendly. We invite you to join this course and take your data analysis and visualisation skills to the next level.

Course Objectives

Whether you are new to R or have dabbled without structure, this course takes you from the fundamentals of R programming through to confident, independent data analysis:

  • R programming foundations: Participants will learn to navigate RStudio, understand R's data structures, write and apply functions, access packages and organise clean, well-commented R scripts that support quality control and reproducibility.
  • Data management in R: Participants will be able to import and export data between R and common formats, including Excel, CSV and Stata, and apply systematic data cleaning and management procedures in preparation for analysis.
  • Descriptive statistics and subgroup analysis: Participants will understand the theory and application of descriptive statistics, conduct appropriate descriptive and stratified analyses in R, and produce publication-ready graphics using Base R and GGPLOT functions.
  • Inferential statistics: Participants will be introduced to confidence intervals, p-values and hypothesis testing, and will be able to select and apply appropriate parametric and non-parametric tests, and they will understand the rationale for multiple regression models.

Course Outcomes

R is only as powerful as the analyst using it. After completing this course, participants will be able to take raw data from their own research settings, clean and structure it properly, and produce neat, reproducible analyses and publication-quality graphics entirely in R. They will be proficient in both descriptive and inferential statistics, confident in interpreting and communicating statistical outputs, and better equipped to critically evaluate the analyses they encounter in research papers and reports.

Course Content

Day 1: Introduction to Data and R
  • Understand how R works and why it has become the tool of choice for researchers and data scientists
  • Introduction to R programming. 
  • Begin working with R’s core data structures, functions and packages
  • Write clean, organised R scripts that make your work reproducible and easy to revisit
  • Import, export and manage data across formats including Excel, CSV and Stata
  • Develop a solid conceptual understanding of descriptive statistics and apply it confidently in R
  • Produce subgroup analyses and publication-ready graphics
  • Calculate and interpret confidence intervals and understand what they tell you about your data
  • Select and apply the right statistical test for categorical data comparisons
  • Choose between parametric and non-parametric approaches based on the nature of your data
  • Interpret measures of effect, take your first steps into multiple regression, and consolidate learning through a final project
Week 1: Introduction to Data and R
  • Understand how R works and why it has become the tool of choice for researchers and data scientists
  • Introduction to R programming
  • Set up your R environment and begin working with R’s core data structures, functions and packages
  • Write clean, organised R scripts that make your work reproducible and easy to revisit
  • Import, export and manage data across formats including Excel, CSV and Stata
  • Develop a solid conceptual understanding of descriptive statistics and apply it confidently in R
  • Produce subgroup analyses and publication-ready graphics from your own data
  • Calculate and interpret confidence intervals, and select and apply the right statistical test for your data
  • Choose between parametric and non-parametric approaches, interpret measures of effect, and take your first steps into multiple regression

Who Should Attend

  • Researchers
  • Biostatisticians
  • Data analysts
  • Economists
  • HODs
  • Epidemiologist
  • Programme managers
  • Postgraduate students
  • Market researchers
  • Medical researchers
  • Scientists
  • Government officials

Price Includes

In-person:

  • Course attendance 
  • Full refreshments: 
    • Lunch 
    • Welcome tea (tea and pastries) 
    • Two tea breaks (tea and pastries) 
  • Course lecture notes and training manual 
  • Complimentary parking 
  • Certificate of attendance

Virtual:

  • Access to interactive webinars 
  • Videos for every interactive webinar session 
  • All pre-reading materials 
  • E-Certificate of attendance

Course Details

In-Person
  • 5 days
  • R13,500
Online
  • 4 weeks
  • R11,000
Enrol Now

For more details about our services contact:

Dimakatso Mofokeng
info@cesar-africa.com
+27 11 403 1411