Training course

Overview

R Data Analysis is a comprehensive professional training course designed to develop practical and advanced capabilities in using R for data preparation, statistical analysis, visualization, modelling, and evidence-based decision-making. The course provides a structured pathway from the R environment and programming fundamentals through data wrangling, exploratory data analysis, statistical inference, regression modelling, predictive analytics, time-series analysis, and reproducible analytical reporting. Participants learn how to transform raw and complex datasets into reliable analytical outputs using professional R workflows.

R Data Analysis combines R programming with modern data analytics practices, enabling participants to work efficiently with structured and unstructured datasets across business, finance, economics, research, operations, marketing, public-sector, and management environments. The course introduces practical tools and packages including RStudio, tidyverse, dplyr, tidyr, ggplot2, readr, readxl, lubridate, stringr, purrr, and appropriate statistical modelling packages. Participants progressively develop skills in importing, cleaning, transforming, joining, visualizing, analysing, modelling, and reporting data.

The programme emphasizes professional analytical standards and best practices including reproducible research, data quality management, statistical validity, documentation, version control concepts, model diagnostics, responsible interpretation, and transparent reporting. Practical case studies and exercises simulate real-world analytical requirements such as customer analysis, financial performance assessment, operational monitoring, survey analysis, forecasting, risk assessment, programme evaluation, and organizational performance measurement. Participants learn not only how to execute R commands and functions, but also how to select appropriate analytical methods and communicate their findings clearly.

By the end of this 10-day R Data Analysis training course, participants will be able to establish professional R analytical projects, prepare and validate datasets, conduct exploratory and inferential analysis, build and evaluate statistical models, perform predictive and time-based analysis, automate analytical workflows, and communicate results through effective visualizations and reproducible reports. The course is suitable for professionals who want to develop practical R data analysis skills while building a strong foundation for advanced statistical analytics, data science, research, business intelligence, and evidence-based decision-making.

Course Duration

10 Days (80 Hours)

Target Participants

·         Data analysts and aspiring data analysts who need professional R data analysis skills

·         Statisticians, researchers, economists, and quantitative professionals

·         Business intelligence and reporting professionals

·         Financial, investment, and risk analysts

·         Marketing, customer, and operational analysts

·         Monitoring and evaluation professionals

·         Academic and applied researchers working with quantitative datasets

·         Managers and technical professionals responsible for data-driven decision-making

·         Professionals transitioning from Excel, SPSS, Stata, SAS, or other analytical environments to R

·         Professionals seeking a practical foundation for advanced R, statistical modelling, and data science

Course Objectives

By the end of the training, participants will be able to:

·         Navigate the R and RStudio environments and establish professional analytical projects

·         Understand R objects, vectors, data types, functions, operators, scripts, packages, and analytical workflows

·         Import and export data from Excel, CSV, text, and other common data sources

·         Clean, validate, transform, reshape, and integrate datasets using modern R tools

·         Identify and address missing values, duplicates, inconsistencies, outliers, and data-quality problems

·         Conduct descriptive and exploratory data analysis using appropriate statistical techniques

·         Create professional analytical visualizations using ggplot2 and related R tools

·         Apply statistical inference, hypothesis testing, correlation, and group-comparison techniques

·         Build, interpret, and diagnose regression and classification models

·         Apply predictive analytics, time-series methods, forecasting, and scenario analysis

·         Automate repetitive analytical tasks using functions, iteration, pipelines, and reusable scripts

·         Develop reproducible analytical reports and communicate findings to technical and non-technical audiences

·         Complete an end-to-end R data analysis project using real-world analytical practices

Course Content

Day 1: R Environment, Programming Foundations, and Data Analysis Workflow

Module 1: Foundations of R Data Analysis and Professional R Workflows

1.      Introduction to R for Professional Data Analysis — Understanding R's role in statistical analysis, business analytics, research, data science, forecasting, and evidence-based decision-making.

2.      R and RStudio Environment — Navigating the Console, Source Editor, Environment, History, Files, Plots, Packages, Help, and project interfaces.

3.      R Projects and Analytical Workspace Management — Creating structured projects, managing working directories, organizing scripts, data, outputs, documentation, and project resources.

4.      R Objects and Data Types — Understanding vectors, factors, matrices, lists, data frames, tibbles, dates, logical values, character values, and numeric structures.

5.      Variables, Operators, Functions, and Expressions — Working with assignment, arithmetic, logical, comparison, indexing, functions, arguments, and return values.

6.      R Scripts and Reproducible Analytical Workflows — Building organized scripts, documenting analytical steps, commenting code, and creating repeatable workflows.

7.      Packages and the R Ecosystem — Installing, loading, updating, and managing packages and understanding the roles of CRAN and widely used analytical libraries.

8.      Tidyverse Analytical Framework — Introducing dplyr, tidyr, ggplot2, readr, stringr, forcats, purrr, and other components of modern R data analysis.

9.      Analytical Standards and Best Practices — Applying principles of reproducibility, documentation, data integrity, analytical transparency, validation, and responsible statistical practice.

10.  Practical Exercise: Building an R Analytics Project — Participants create an RStudio project, organize analytical files, import a sample dataset, document the workflow, and produce an initial data inventory.

Day 2: Data Import, Cleaning, Validation, and Transformation

Module 2: Professional Data Preparation with R

1.      Importing CSV and Delimited Data — Using readr and base R tools to import structured data while controlling data types and parsing.

2.      Importing Excel and External Data — Working with Excel files and common structured data sources using appropriate R packages.

3.      Data Inspection and Profiling — Examining dimensions, variable names, structures, summaries, distributions, and metadata to assess analytical readiness.

4.      Data Cleaning with dplyr — Selecting, filtering, arranging, mutating, summarizing, and grouping data using efficient transformation pipelines.

5.      Missing Data Identification and Treatment — Detecting missing observations, understanding missingness patterns, and applying appropriate treatment strategies.

6.      Duplicate Records and Identifier Validation — Identifying duplicate observations, checking unique identifiers, and resolving record-level inconsistencies.

7.      Data Type Conversion and Standardization — Converting variables into appropriate numeric, character, factor, logical, and date formats.

8.      String and Date-Time Processing — Cleaning text fields, extracting patterns, standardizing labels, and manipulating dates with stringr and lubridate.

9.      Data Validation and Quality Control — Creating range checks, logical checks, cross-variable validations, and systematic data-quality rules.

10.  Case Study: Preparing a Real-World Analytical Dataset — Participants clean and validate a multi-source dataset, document identified problems, apply transformations, and create a reliable analysis-ready dataset.

Day 3: Data Integration, Reshaping, and Exploratory Data Analysis

Module 3: Advanced Data Wrangling and Exploratory Analytics

1.      Data Integration Principles — Understanding how multiple datasets can be combined to support comprehensive analytical questions.

2.      Joining Datasets with dplyr — Applying left, inner, right, full, semi, and anti joins while validating relationships between datasets.

3.      Append and Row-Binding Operations — Combining datasets vertically while maintaining consistent variable structures and data integrity.

4.      Reshaping Data with tidyr — Converting datasets between wide and long structures using pivot_longer and pivot_wider.

5.      Grouped Analysis and Aggregation — Producing counts, totals, means, medians, proportions, rankings, and other grouped summaries.

6.      Conditional Transformation and Feature Creation — Developing indicators, ratios, growth rates, categories, flags, and analytical features.

7.      Descriptive Statistics with R — Calculating central tendency, dispersion, quantiles, frequency distributions, and summary measures.

8.      Exploratory Data Analysis — Identifying distributions, relationships, trends, clusters, anomalies, and potential analytical issues.

9.      Outlier and Anomaly Investigation — Using statistical summaries and graphical methods to identify unusual observations and assess their significance.

10.  Practical Exercise: Integrated Exploratory Data Analysis — Participants combine multiple datasets, reshape information, create analytical variables, conduct descriptive analysis, and identify key patterns requiring further investigation.

Day 4: Data Visualization and Analytical Communication

Module 4: Professional Visualization with R and ggplot2

1.      Principles of Effective Data Visualization — Understanding visual perception, analytical purpose, audience requirements, accuracy, clarity, and appropriate chart selection.

2.      ggplot2 Grammar of Graphics — Understanding data, aesthetics, geometries, scales, coordinates, facets, themes, and layered visualization.

3.      Bar Charts and Categorical Comparisons — Visualizing counts, proportions, rankings, categories, and comparative performance.

4.      Histograms and Distribution Visualization — Examining continuous-variable distributions, skewness, concentration, and variability.

5.      Box Plots and Group Comparisons — Comparing distributions, detecting outliers, and assessing differences across categories.

6.      Scatterplots and Relationship Analysis — Visualizing relationships between continuous variables and identifying trends, clusters, nonlinear patterns, and influential observations.

7.      Line Charts and Time-Based Visualization — Communicating trends, seasonality, performance movements, and changes over time.

8.      Faceting and Multidimensional Visualization — Comparing multiple groups, regions, products, departments, or other analytical segments within coherent visual structures.

9.      Professional Chart Design and Reporting — Applying meaningful labels, titles, annotations, scales, legends, themes, and presentation principles for professional reporting.

10.  Case Study: Building an Analytical Visualization Portfolio — Participants create a set of professional R visualizations explaining organizational performance, distributions, relationships, and trends for a management audience.

Day 5: Statistical Analysis, Inference, and Relationship Assessment

Module 5: Applied Statistical Analysis with R

1.      Foundations of Statistical Inference — Understanding populations, samples, estimators, sampling variability, uncertainty, and statistical evidence.

2.      Measures of Central Tendency and Dispersion — Applying means, medians, standard deviations, variance, ranges, and quantiles to analytical problems.

3.      Probability Concepts for Data Analysts — Reviewing probability principles, distributions, conditional probability, and their relevance to statistical analysis.

4.      Confidence Intervals — Calculating and interpreting confidence intervals for means, proportions, and other statistical quantities.

5.      Hypothesis Testing — Understanding null and alternative hypotheses, test statistics, p-values, significance levels, and interpretation.

6.      Comparing Groups with R — Applying t-tests, proportion tests, and appropriate comparison procedures to real-world datasets.

7.      Analysis of Variance — Using ANOVA to assess differences among multiple groups and interpreting group-level effects.

8.      Correlation and Association — Measuring relationships between variables and distinguishing statistical association from causal interpretation.

9.      Nonparametric Statistical Methods — Applying rank-based methods when distributional assumptions are inappropriate or measurement conditions require alternatives.

10.  Practical Exercise: Statistical Evidence Assessment — Participants formulate hypotheses, select appropriate tests, calculate confidence intervals, compare groups, evaluate statistical significance, and communicate practical implications.

Day 6: Regression Modelling and Statistical Diagnostics

Module 6: Regression Analysis and Model Interpretation

1.      Introduction to Regression Modelling — Understanding regression as a framework for analysing relationships between outcomes and explanatory variables.

2.      Simple Linear Regression — Building and interpreting models with one explanatory variable and evaluating estimated relationships.

3.      Multiple Linear Regression — Incorporating multiple predictors and assessing their independent relationships with the outcome.

4.      Categorical Predictors and Factors — Using factors and indicator variables to model categorical characteristics such as region, department, sector, or customer group.

5.      Interaction Effects — Modelling situations in which the relationship between predictors and outcomes varies across groups or conditions.

6.      Model Fit and Explanatory Power — Interpreting R-squared, adjusted R-squared, residual error, and related measures of model performance.

7.      Regression Assumptions and Diagnostics — Evaluating linearity, independence, normality of residuals, constant variance, and other model assumptions.

8.      Multicollinearity and Influential Observations — Identifying correlated predictors, leverage, influential cases, and their potential effects on model results.

9.      Predictions and Marginal Interpretation — Generating fitted values, prediction intervals, scenarios, and interpretable model-based estimates.

10.  Practical Exercise: Building and Diagnosing a Regression Model — Participants develop a multiple regression model, assess assumptions, investigate influential observations, interpret coefficients, and produce a professional analytical summary.

Day 7: Predictive Analytics, Classification, and Machine Learning Foundations

Module 7: Predictive Modelling with R

1.      Predictive Analytics Concepts — Distinguishing descriptive, inferential, explanatory, and predictive analysis and identifying appropriate use cases.

2.      Preparing Data for Predictive Modelling — Creating analytical features, handling missing values, encoding categorical variables, and preparing modelling datasets.

3.      Training and Testing Data — Understanding model development, validation datasets, generalization, and the importance of evaluating predictive performance.

4.      Logistic Regression — Modelling binary outcomes such as customer retention, default, compliance, failure, or programme completion.

5.      Classification and Predicted Probabilities — Generating predicted probabilities, classifications, thresholds, and interpretable risk measures.

6.      Classification Performance Assessment — Evaluating accuracy, sensitivity, specificity, precision, recall, confusion matrices, and related measures.

7.      Decision Trees and Model-Based Segmentation — Understanding tree-based approaches for classification, prediction, segmentation, and decision support.

8.      Introduction to Random Forest and Ensemble Methods — Understanding ensemble learning concepts and their applications to structured analytical problems.

9.      Model Comparison and Validation — Comparing predictive models using appropriate performance measures and avoiding overfitting.

10.  Case Study: Predicting Strategic Business Outcomes — Participants develop and evaluate predictive models for a realistic business or operational scenario and communicate model performance and practical implications.

Day 8: Time-Series Analysis, Forecasting, and Advanced Statistical Modelling

Module 8: Time-Based Analytics and Strategic Forecasting

1.      Time-Series Data Concepts — Understanding trends, seasonality, cycles, shocks, autocorrelation, and other characteristics of time-based datasets.

2.      Preparing Time-Series Data in R — Creating appropriate date or time indexes and organizing observations for time-series analysis.

3.      Time-Based Transformations — Applying lags, leads, differences, growth rates, rolling calculations, and other dynamic transformations.

4.      Trend and Seasonality Analysis — Identifying long-term movements, recurring patterns, structural changes, and seasonal effects.

5.      Time-Series Visualization — Creating effective time-based charts to communicate historical performance and emerging trends.

6.      Autocorrelation and Time-Series Diagnostics — Assessing serial dependence and identifying analytical implications.

7.      Forecasting Fundamentals — Understanding forecasting objectives, baseline models, forecasting horizons, and evaluation principles.

8.      Forecasting with R — Applying appropriate forecasting techniques and evaluating forecasts against historical outcomes.

9.      Scenario, Sensitivity, and Stress Analysis — Developing alternative assumptions and assessing how changes affect expected outcomes.

10.  Case Study: Strategic Forecasting in R — Participants analyse historical operational or financial data, identify trends and seasonality, develop forecasts, assess forecast performance, and create alternative scenarios.

Day 9: Advanced R Programming, Automation, and Reproducible Analytics

Module 9: Advanced R Analytical Workflows and Automation

1.      Writing Reusable R Functions — Creating custom functions to standardize analytical procedures and reduce repetitive coding.

2.      Functional Programming with purrr — Applying map-based workflows and iteration to repetitive data-processing and analytical tasks.

3.      Conditional Programming and Control Structures — Using if statements, loops, vectorized operations, and logical structures to develop flexible workflows.

4.      Advanced Data Pipelines — Designing efficient pipelines with the pipe operator and modern dplyr workflows.

5.      Automated Data Quality Checks — Creating reusable procedures for missing values, duplicates, ranges, consistency, and structural validation.

6.      Batch Analysis and Automated Reporting — Running repeated analyses across departments, regions, products, customers, or reporting periods.

7.      Reproducible Research with R Markdown or Quarto — Combining analytical code, narrative, tables, visualizations, and results into reproducible reports.

8.      Version Control and Analytical Collaboration — Understanding Git-based workflows, change tracking, collaboration, documentation, and reproducibility principles.

9.      Analytical Workflow Optimization — Improving code organization, efficiency, readability, maintainability, and scalability.

10.  Practical Exercise: Developing an Automated R Workflow — Participants build a reusable analytical pipeline that imports data, validates quality, performs analysis, generates visualizations, and produces a reproducible report.

Day 10: Advanced R Data Analysis, Strategic Decision Support, and Capstone

Module 10: Integrated R Analytics Excellence and Professional Capstone

1.      Integrated Data Analysis Framework — Connecting business questions, data preparation, exploratory analysis, statistical modelling, validation, visualization, and decision-making into a complete analytical workflow.

2.      Advanced Model Evaluation and Robustness — Applying alternative specifications, sensitivity analysis, validation techniques, diagnostic checks, and comparative modelling.

3.      Combining Statistical and Predictive Evidence — Integrating descriptive statistics, inferential methods, regression, classification, and forecasting into coherent analytical assessments.

4.      Advanced Feature Engineering — Developing meaningful analytical variables, transformations, interaction features, time-based features, and domain-specific indicators.

5.      Strategic Data Storytelling — Translating analytical results into clear narratives that explain what happened, why it matters, what is uncertain, and what decisions may be informed by the evidence.

6.      Executive Reporting with R — Preparing concise tables, charts, summaries, analytical reports, and presentation-ready outputs for non-technical stakeholders.

7.      Analytical Quality Assurance — Reviewing data integrity, code quality, model assumptions, statistical validity, reproducibility, documentation, and reporting accuracy.

8.      Responsible Data Analysis and Limitations — Recognizing bias, data limitations, uncertainty, model constraints, privacy considerations, and risks of overinterpreting analytical results.

9.      Integrated Capstone: End-to-End R Data Analysis Project — Participants define a real-world analytical problem, prepare and validate data, conduct exploratory analysis, develop appropriate statistical or predictive models, evaluate results, create visualizations, and produce a reproducible analytical report.

10.  Capstone Presentation and Professional R Analytics Action Plan — Participants present their findings, defend methodological choices, communicate limitations and insights, and develop a practical 90-day plan for applying R data analysis within their professional environment.