R Data Analysis and Statistical Reporting Training Course

Course overview

Business teams increasingly expect analysts to turn operational, customer, financial and digital data into evidence they can act on. The difficulty is rarely producing a single chart or running a single test; it is building an analysis that is reproducible, statistically sound and clear enough for decision-makers to challenge and trust. This course equips professionals to use R as a disciplined analytics and reporting environment, replacing fragile spreadsheet workflows and one-off scripts with traceable data pipelines, validated models and publication-ready reports.

Participants work through the R workflow from importing and reshaping real-world data to exploratory analysis, statistical inference, regression modelling, visualisation and automated reporting. They use RStudio projects, tidyverse packages, ggplot2, inferential tests, linear and logistic regression, model diagnostics, resampling and Quarto documents. Emphasis is placed on selecting methods that fit the business question, checking assumptions, interpreting uncertainty, and communicating findings without overstating what the data supports.

Instructor-led demonstrations are followed by guided coding labs, peer review and a multi-day case study based on a realistic organisational dataset. Each participant develops a reproducible R analysis project containing documented scripts, a cleaned analysis dataset, visualisations, statistical outputs and a Quarto report suitable for sharing with a manager or stakeholder. By the end of the two weeks, participants can structure a repeatable analysis process and defend both the method and the recommendation behind their results.

The course is designed for analysts and technical professionals who already work with data and need stronger statistical reporting capability in R. It is equally valuable for teams standardising analytics practice, improving auditability, or moving recurring reporting from manual production to code-based workflows.

Course objectives

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

• Build reproducible RStudio projects using scripts, relative file paths and structured project folders
• Import, validate and reshape tabular data with readr, dplyr, tidyr and explicit data-quality checks
• Create exploratory visualisations with ggplot2 to identify distributions, relationships, outliers and missingness
• Select and perform hypothesis tests, confidence intervals and effect-size calculations for business questions
• Fit and interpret linear and logistic regression models with appropriate predictors and reference categories
• Diagnose model assumptions using residual plots, multicollinearity checks, influence measures and validation methods
• Produce parameterised Quarto reports that combine R code, tables, charts, statistical results and written conclusions
• Present a defensible analytical recommendation supported by reproducible evidence, limitations and next actions

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to replace manual analysis and reporting workflows with reproducible R projects
• Business Intelligence Analysts — who must add statistical evidence and explanatory modelling to dashboard insights
• Research Analysts — who need defensible inference, documented methods and publication-quality outputs
• Data Scientists — who require stronger reporting, model interpretation and stakeholder communication practices
• Financial Analysts — who analyse performance, forecast drivers and explain uncertainty in management reporting
• Digital and Marketing Analysts — who evaluate campaigns, customer behaviour and experiments using statistical methods

Requirements and prerequisites

Participants should be comfortable working with structured data in CSV or Excel form and understand basic concepts such as rows, columns, data types, summary statistics, percentages and charts. Prior exposure to an analytics language, SQL, Python, Excel formulas or basic R is helpful, because the course moves quickly into data wrangling and statistical modelling. Participants should also recognise the purpose of a mean, standard deviation and correlation. Advanced mathematics, prior regression modelling, prior Quarto experience and software engineering experience are not required. A laptop able to install R and RStudio is needed for practical work.

Training methodology

The programme alternates concise instructor-led explanations with live coding in RStudio and structured individual labs. Participants work with realistic operational and customer datasets, progressing from raw files to a finished statistical report. Exercises require learners to inspect code output, justify method selection and review another participant’s charts or conclusions. Short case discussions focus on misleading metrics, violated assumptions and reporting risk. During the final two days, each participant applies the workflow to an integrated case study and receives instructor feedback on their Quarto report, interpretation and recommended action.

Course outline

Day 1: R projects and analytical workflow

• R and RStudio interface navigation
• RStudio Projects and reproducible folder structures
• R objects, vectors, data frames and tibbles
• Script authoring, comments and code sections
• Package installation and library management
• Relative paths and portable file references
• Reading and inspecting CSV and Excel data

Workshop: Set up an RStudio Project and create a documented script that imports, profiles and saves an initial business dataset.

Day 2: Data preparation and quality control

• Data types, parsing problems and type conversion
• Missing-value profiling and treatment decisions
• Filtering, selecting and sorting with dplyr
• Creating derived variables with mutate and case_when
• Grouping and aggregation with summarise
• Joining lookup tables and transaction data
• Duplicate detection and data-validation assertions

Workshop: Clean and join customer, transaction and product files, producing a documented analysis-ready dataset and quality-check summary.

Day 3: Exploratory analysis and visual communication

• Distribution analysis with histograms and density plots
• Categorical summaries and proportion tables
• Scatterplots, boxplots and relationship exploration
• Faceting and grouping in ggplot2
• Scale, label and theme design for business charts
• Outlier identification and investigation
• Exploratory findings logs and analytical questions

Workshop: Create a chart pack that identifies three material patterns, anomalies or segments in the prepared dataset.

Day 4: Statistical inference for decisions

• Population, sample, parameter and statistic concepts
• Sampling variability and confidence intervals
• Null and alternative hypothesis formulation
• One-sample and two-sample t-tests
• Paired tests and before-and-after comparisons
• Chi-square tests for categorical association
• P-values, effect sizes and practical significance

Workshop: Test whether a service intervention changed customer outcomes and write a decision-focused interpretation of the result.

Day 5: Non-parametric methods and experimental analysis

• Normality assessment with plots and diagnostic tests
• Mann-Whitney and Wilcoxon signed-rank tests
• Kruskal-Wallis testing for multiple groups
• One-way ANOVA and post-hoc comparisons
• Contingency tables and proportion tests
• A/B test design, power and sample-size considerations
• Multiple-testing risk and interpretation controls

Workshop: Analyse a multi-variant campaign test, select an appropriate method and produce a concise recommendation with caveats.

Day 6: Linear regression and explanatory modelling

• Linear regression purpose and model structure
• Continuous and categorical predictor encoding
• Coefficient interpretation and reference levels
• Interaction terms and conditional effects
• Adjusted R-squared and model comparison
• Prediction intervals and confidence intervals
• Regression result tables and coefficient plots

Workshop: Build a linear regression model for revenue drivers and prepare a stakeholder explanation of the strongest controllable factors.

Day 7: Logistic regression and model diagnostics

• Binary outcome modelling with logistic regression
• Odds, log-odds and odds-ratio interpretation
• Probability prediction and classification thresholds
• Residual diagnostics and influential observations
• Multicollinearity assessment with variance inflation factors
• Confusion matrices, sensitivity and specificity
• Cross-validation and held-out performance checks

Workshop: Develop and evaluate a customer-churn classification model, then recommend a threshold aligned to business risk.

Day 8: Reproducible statistical reporting with Quarto

• Quarto document structure and YAML metadata
• Code chunks, execution options and inline R values
• Narrative interpretation of statistical output
• Formatted tables with gt
• Figure sizing, captions and cross-references
• Parameterised reports for recurring analysis
• Rendering HTML, PDF and Word outputs

Workshop: Convert a completed analysis into a parameterised Quarto management report containing methods, findings, charts and recommendations.

Day 9: Reporting governance and stakeholder challenge

• Analytical question framing and decision criteria
• Traceability from source data to reported conclusion
• Assumption registers and limitation statements
• Reproducibility checks and peer code review
• Misleading visualisations and selective reporting risks
• Communicating uncertainty to non-technical audiences
• Git version control for analysis projects

Workshop: Peer-review a partner’s R analysis project using a reporting assurance checklist and provide documented improvement actions.

Day 10: Integrated analytics capstone

• Capstone case briefing and analytical scope
• Data preparation and exploratory evidence review
• Method selection and statistical test justification
• Model development and diagnostic review
• Quarto report assembly and quality assurance
• Executive briefing and recommendation framing
• Workplace application roadmap and reusable templates

Workshop: Complete and present an end-to-end R analysis project with a Quarto report, executive findings summary and 90-day application plan.

Tools & standards covered

R, RStudio, Quarto, Git

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions