Training Course on Statistical Data Management and Analysis using R (DMC011)
Training Course on Statistical Data Management and Analysis using R
The Statistical Data Management and Analysis using R training provides a comprehensive introduction to leveraging the R programming language for effective data manipulation, exploration, and statistical modeling. Through a series of hands-on exercises and real-world examples, participants will learn essential techniques such as data importation, cleaning, visualization, and modeling. By the end of the course, attendees will have the skills and confidence to tackle diverse analytical challenges, create reproducible reports using R Markdown, and apply statistical methods to extract meaningful insights from complex datasets.
Course Duration
Online Training: 7 days (4hrs per day)
Classroom Training: 5 days (7hrs per day)
Course Objectives
By the end of this course, participants will be able to:
- Understand core concepts of statistical analysis and research design
- Install, navigate, and effectively use R and RStudio for statistical computing
- Import, manage, clean, and transform datasets using R
- Apply data wrangling techniques to prepare data for analysis
- Conduct exploratory data analysis using descriptive statistics and tables
- Create and customize data visualizations using base R graphics
- Perform mean comparison tests and tests of association
- Build and interpret predictive regression models using R
- Apply appropriate statistical methods to answer research questions
- Produce reproducible and well-documented analytical outputs
Organisational Impact
Upon completion of this course, organizations will benefit from:
- Enhanced in-house capacity for statistical data analysis and interpretation
- Improved data quality through effective data cleaning and management practices
- Increased use of evidence-based decision-making supported by robust analysis
- Reduced reliance on external consultants for routine statistical analysis
- Improved efficiency in handling large and complex datasets
- Standardized and reproducible analytical workflows using R
- Stronger monitoring, evaluation, research, and reporting outputs
Personal Impact
By completing this course, participants will:
- Gain practical, hands-on skills in statistical analysis using R
- Build confidence in handling, analyzing, and visualizing data
- Strengthen analytical and problem-solving skills
- Improve understanding of statistical concepts and their real-world application
- Increase professional competitiveness and employability in data-driven roles
- Enhance ability to independently conduct and interpret statistical analyses
- Develop skills to produce clear, accurate, and reproducible analytical results
Course Outline
Module 1: Introduction to Statistical Analysis
- Basic steps of the research process
- Difference between populations and samples
- Difference between experimental and non-experimental research designs
- Difference between independent and dependent variables
Module 2: Introduction to R software for statistical computing
- Overview of the R Studio IDE
- Installing, loading and updating R packages
- Creating objects in R
- Data types
- Data structures
- Sorting vectors and data frames
- Directory management commands
- Direct data entry in R (for small data sets)
- Importing data from other software
- Decision structures (if, if-else, if-else if-else)
- Repetitive structures (for and while loops)
- Other important programming functions (break, next, warn, stop)
Module 3: Data Wrangling and Cleaning in R
- Working with variables
- Transform continuous variables to 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 4: Explanatory Data Analysis (EDA) in R
- Creating tables of frequencies and proportions
- Cross tabulations of categorical variables
- Descriptive statistics for continuous variables
Module 5: Data Visualization using R base package
- 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 relationship between variables
Module 6: Mean Comparison Tests in R
- One Sample T Test
- Independent Samples T Test
- Paired Samples T Test
- One-way analysis of variance (ANOVA)
Module 7: Tests of Associations in R
- Chi-Square test of independence
- Pearsons Correlation
- Spearmans Rank-Order Correlation
Module 8: Predictive Regression Models Using R
- Linear Regression
- Multiple Linear Regression
- Binary Logistic Regression
- Ordinal Logistic Regression
Note: The specific content, activities, and duration of each session may be adjusted based on the target audience, learning objectives, and available time.
Course Language
English
Training Methodology
The course will be delivered using a practical, learner-centered approach that balances theory with hands-on application. The methodology includes:
- Interactive lectures to introduce statistical concepts and methodologies
- Live demonstrations of R and RStudio functionalities
- Guided hands-on coding sessions using real-world datasets
- Step-by-step practical exercises aligned with each module
- Individual and group-based assignments to reinforce learning
- Case studies and applied data analysis scenarios
- Question-and-answer sessions and peer learning
- Continuous trainer feedback and technical support throughout the training
Certification
Upon completion of training, the participant will be issued with a certificate of Completion.
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