Data Management and Statistical Data Analysis using R Course

Introduction

This course is designed to introduce learners to the practical and theoretical aspects of working with data using the R programming language one of the most widely used tools in statistics and data science.

The course begins with the fundamentals of R, focusing on data structures, basic programming concepts, and the use of essential libraries for data manipulation and visualization. Participants will learn how to clean, transform, and prepare datasets for analysis using powerful packages such as tidyverse, dplyr, and ggplot2. Emphasis is placed on reproducible workflows, efficient coding practices, and real-world data applications.

Building on this foundation, participants will explore core statistical techniques including descriptive statistics, hypothesis testing, regression analysis, and sampling methods. The course encourages analytical thinking and statistical reasoning by applying these methods to real-life datasets, helping participants understand both the "how" and the "why" of data analysis.

By the end of the course, participants will be equipped to conduct their own data analysis projects using R, from data import to final reporting. Whether you're a student, researcher, or professional looking to develop in-demand analytical skills, this course provides a strong foundation in data management and statistical analysis within the R environment.

Related course – click the course title to view the course outline

·       Research Design, Mobile Data Collection and Data Analysis using R and Nvivo Course

Duration

5 days

Who Should Attend?

This course is designed for individuals who want to gain practical skills in data management and statistical analysis using the R programming language. It is ideal for:

• Researchers in fields such as statistics, social sciences, public health, economics, and environmental studies.
• Data analysts and professionals seeking to enhance their ability to work with data in a reproducible and statistically rigorous way.
• Academics and educators who want to integrate R-based data analysis into their teaching or research workflows.
• Public sector and NGO staff involved in survey design, monitoring and evaluation, or evidence-based policy development.
• Beginners in data science who have little or no prior experience with R but want to build a strong foundation in statistical programming.

Course Objective

By the end of this course, participants will be able to:

·       Introduce the fundamentals of R programming for data handling and analysis.

·       Develop the ability to clean, organize, and transform datasets using key R packages such as tidyverse, dplyr, and tidyr.

·       Enable learners to perform descriptive and inferential statistical analyses, including hypothesis testing, regression, and correlation.

·       Teach effective data visualization techniques using tools like ggplot2 to uncover patterns and communicate insights.

·       Guide students in designing and analyzing surveys, including sampling strategies and interpretation of results.

·       Promote the use of reproducible and efficient coding practices for transparent data analysis workflows.

·       Encourage critical thinking and ethical decision-making in the use and communication of statistical data.

·       Prepare participants to conduct end-to-end data analysis projects using real-world datasets and reporting best practices.

Course content

Module 1: Introduction to Statistical Analysis

• Overview of the research process and its basic steps
• Populations vs. samples: key distinctions
• Experimental vs. non-experimental research designs
• Understanding variables: independent vs. dependent

Module 2: Getting Started with R for Statistical Computing

• Introduction to R and the RStudio Integrated Development Environment (IDE)
• Installing, loading, and updating R packages
• Creating and manipulating R objects
• Understanding data types and data structures in R
• Sorting vectors and data frames
• Managing working directories and file paths
• Direct data entry for small datasets
• Importing datasets from external sources (CSV, Excel, SPSS, etc.)
• Control flow structures: if, if-else, if-else if-else
• Looping constructs: for and while
• Programming tools: break, next, warning(), and stop() functions

Module 3: Data Wrangling and Cleaning in R

• Creating and modifying variables
• Recoding continuous variables into categories
• Adding new variables to data frames
• Identifying and handling missing values
• Subsetting, appending, and merging data frames
• Splitting data frames using logical conditions
• Stacking and unstacking data for reshaping

Module 4: Exploratory Data Analysis (EDA)

• Creating frequency and proportion tables
• Generating cross-tabulations for categorical variables
• Calculating descriptive statistics for continuous variables
• Interpreting distributions and identifying outliers

Module 5: Data Visualization Using Base R

• Introduction to data visualization in R
• Customizing graph attributes: titles, axes, labels, and legends
• Visualizing categorical variables: bar charts and pie charts
• Visualizing continuous variables: histograms, boxplots, density plots
• Exploring relationships between variables: scatter plots and line graphs

Module 6: Statistical Tests for Mean Comparison

• One-sample t-test
• Independent samples t-test
• Paired samples t-test
• One-way ANOVA for comparing means across groups

Module 7: Tests of Association and Correlation

• Chi-square test of independence for categorical variables
• Pearson’s correlation for continuous variables
• Spearman’s rank-order correlation for ordinal and non-parametric data

Module 8: Predictive Modeling with Regression Techniques

• Introduction to regression analysis
• Simple linear regression and interpretation of results
• Multiple linear regression for multivariate analysis
• Binary logistic regression for classification
• Ordinal logistic regression for ordered categorical outcomes

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue

The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment

Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better

Online/ On-site (Nairobi, Kenya) Training Schedule