R is an open-source programming language that provides various statistical and graphical techniques.  This Training on Statistical Data Analysis using R will give you a solid foundation in creating statistical analysis solutions using the R language, and how to carry out a range of commonly used analytical processes. Participants will learn to manipulate data, conduct exploratory data analysis, perform statistical tests, and create visualizations.

Course Objectives

Statistical data analysis using R aims to help participants;

1. Understand the fundamentals of R programming language.
2. Learn data manipulation and cleaning techniques using R.
3. Perform exploratory data analysis (EDA) to understand the structure and relationships within datasets.
4. Apply statistical tests and methods to analyze data.
5. Create insightful visualizations to communicate data analysis results effectively.

Who Should Attend

1. Researchers
2. Data Analysts
3. Statisticians
4. Business Intelligence Professionals 
5. Policy Analysts
6. Financial Analysts
7. Marketing and Customer Insights Analysts

Course Duration

Classroom- Based    5 Days

Online                        7 Days

Course Outline

Module 1: Introduction to Statistical Analysis

• Overview of R and its applications in data analysis
• Installing, loading, and updating R packages
• Basics of R programming: data types, vectors, matrices, data frames, and lists
• Introduction to R packages and libraries
• Importing data from other software
• Directory management commands

​Module 2: Data Wrangling and Cleaning in R

• Importing data into R from various sources (CSV, Excel, databases)
• Working with variables
• Transform continuous variables into categorical variables
• Add new variables to data frames
• Handling missing values
• Sub-setting data frames
• Appending and merging data frames
• Spit data frames
• Stack and unstack data frames

Module 3: Exploratory Data Analysis (EDA)

• Understanding data distributions and relationships
• Creating tables of frequencies and proportions
• Cross tabulations of categorical variables
• Descriptive statistics for continuous variables

Module 4: Data Visualization

• Introduction to graphs and charts in R
• Customizing graph attributes (titles, axes, text, legends)
• Graphs for categorical variables
• Graphs for continuous variables
• Graphs to investigate the relationship between variables

Module 5: Statistical Inference

• Hypothesis testing: t-tests, chi-square tests, ANOVA
• Confidence intervals and p-values interpretation
• Understanding statistical significance

Module 6: Regression Analysis

• Linear Regression
• Multiple Linear Regression
• Binary Logistic Regression
• Ordinal Logistic Regression

Module 7: Data Visualization and Reporting

• Customizing visualizations
• Creating interactive visualizations
• Exporting plots for reports and presentations

Key Notes

• This training will be presented by competent professionals with hands-on experience in statistical data analysis using the R programming language.
• Participants will learn by active participation during the workshop through presentations, exercises, questionnaires, and group discussions.
• Participants will also have access to course materials, datasets, and R code examples.
• Upon completion of the course, participants will be issued with a certificate.
• This course can be tailor-made to meet organization-wide needs.