Training Course

Overview

Practical R Data Analysis is a comprehensive hands-on professional training course designed to develop the practical skills required to use R and RStudio for real-world data preparation, analysis, visualization, statistical interpretation, and reporting. The course focuses on applied analytical workflows rather than purely theoretical programming, enabling participants to work confidently with real operational, financial, commercial, workforce, project, and performance datasets. Participants progressively develop the ability to transform raw data into reliable analytical information and actionable insights.

This practical R data analysis training provides extensive experience with R, RStudio, tidyverse, dplyr, ggplot2, data frames, tibbles, functions, scripts, and reproducible analytical workflows. Participants learn how to import data from common business sources, inspect and clean datasets, identify data-quality problems, transform variables, combine datasets, calculate performance measures, conduct exploratory analysis, and create professional visualizations. Practical exercises and realistic datasets are used throughout the course to reinforce each analytical technique.

The course progresses from foundational R skills to intermediate and advanced applied analytics, covering descriptive statistics, exploratory data analysis, statistical inference, regression, predictive modelling, time-based analysis, forecasting, automation, and analytical reporting. Participants work through case studies involving operational performance, quality management, customer analysis, workforce monitoring, financial information, project performance, and business planning. Best practices for data validation, documentation, reproducibility, analytical quality assurance, and responsible interpretation are integrated throughout the program.

By completing this practical R data analysis course, participants will be able to independently manage an end-to-end analytical workflow, from receiving raw datasets through cleaning, analysis, visualization, modelling, interpretation, and professional reporting. The course emphasizes learning by doing, with practical exercises, guided demonstrations, case studies, simulations, and an integrated capstone project. Participants will leave with reusable analytical techniques and professional R workflows that can be adapted to a wide range of organizational and operational data analysis requirements.

Course Duration

10 Days (80 Hours)

Target Participants

·         Data analysts and business analysts seeking practical R data analysis skills

·         Professionals responsible for preparing, cleaning, and analysing organizational datasets

·         Operations, finance, marketing, human resources, quality, and project professionals

·         Researchers and monitoring and evaluation professionals working with structured data

·         Business intelligence and reporting professionals seeking hands-on R capabilities

·         Professionals transitioning from spreadsheets or other analytical tools to R

·         Supervisors and managers who regularly perform practical data analysis

·         Professionals responsible for recurring analytical reports and performance monitoring

·         Technical and administrative professionals seeking reproducible data analysis skills

·         Anyone requiring practical, workplace-oriented R data analysis capabilities

Course Objectives

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

·         Navigate R and RStudio confidently for practical data analysis

·         Create organized R projects and reproducible analytical workflows

·         Import data from Excel, CSV, TXT, and other common sources

·         Inspect datasets and understand their structure, variables, and identifiers

·         Clean, validate, transform, and document analytical datasets

·         Identify and address missing values, duplicates, inconsistencies, and outliers

·         Combine and reshape datasets using practical data-wrangling techniques

·         Use dplyr and tidyverse tools to manipulate and summarize data efficiently

·         Conduct descriptive and exploratory data analysis using R

·         Develop professional visualizations using ggplot2

·         Apply statistical inference and interpret confidence intervals and hypothesis tests

·         Perform correlation and regression analysis for practical business questions

·         Develop basic predictive models and evaluate their performance

·         Analyse time-based data and produce practical forecasts

·         Automate recurring analytical processes using R scripts and functions

·         Generate professional analytical reports using R Markdown or Quarto

·         Apply reproducibility, documentation, data-quality, and analytical governance practices

·         Interpret analytical results accurately and communicate practical findings

·         Translate data analysis into actionable recommendations

·         Complete an end-to-end practical R data analysis project

Course Content

Day 1: R Foundations and Practical Analytical Workflows

Module 1: R Environment, Data Structures, and Applied Analysis

1.      Introduction to Practical R Data Analysis

2.      Installing and Configuring R and RStudio

3.      Understanding the RStudio Interface and Analytical Workspace

4.      Creating R Projects and Organizing Analytical Files

5.      R Syntax, Commands, Operators, Expressions, and Functions

6.      Objects, Variables, Vectors, Factors, Lists, and Data Frames

7.      Tibbles and Modern R Data Structures

8.      Installing, Loading, and Managing R Packages

9.      Writing Scripts, Comments, and Reproducible Analytical Code

10.  Practical Exercise: Creating a Complete R Analysis Project from Scratch

Day 2: Data Import, Inspection, and Cleaning

Module 2: Practical Data Preparation and Data Quality Management

1.      Importing Excel and CSV Data into R

2.      Reading Delimited and Text-Based Data Sources

3.      Inspecting Data with glimpse, summary, str, and Related Tools

4.      Understanding Variable Types, Formats, and Identifiers

5.      Detecting Missing Values and Incomplete Observations

6.      Identifying and Removing Duplicate Records

7.      Detecting Invalid Values and Data Inconsistencies

8.      Applying Range, Logical, and Cross-Variable Validation Rules

9.      Documenting Data Quality Issues and Cleaning Decisions

10.  Case Study: Cleaning and Validating a Real-World Operational Dataset

Day 3: Data Wrangling and Dataset Transformation

Module 3: Practical Data Manipulation, Transformation, and Integration

1.      Selecting Variables and Filtering Observations with dplyr

2.      Sorting and Arranging Data for Analytical Review

3.      Creating Variables with mutate

4.      Recoding Categories and Standardizing Data

5.      Creating Flags, Ratios, Rates, and Derived Measures

6.      Grouping Data with group_by

7.      Summarizing Data with summarise

8.      Joining Multiple Datasets with Practical Relational Techniques

9.      Reshaping Data Between Wide and Long Formats

10.  Practical Exercise: Building an Integrated Analytical Dataset from Multiple Data Sources

Day 4: Exploratory Data Analysis and Descriptive Statistics

Module 4: Practical Exploratory Analytics and Data Interpretation

1.      Principles of Exploratory Data Analysis

2.      Frequency Tables and Categorical Variable Analysis

3.      Mean, Median, Mode, Range, and Standard Deviation

4.      Quantiles, Percentiles, and Distribution Analysis

5.      Grouped Descriptive Statistics with dplyr

6.      Identifying Outliers and Unusual Observations

7.      Cross-Tabulation and Comparative Analysis

8.      Correlation Analysis and Relationship Exploration

9.      Identifying Patterns, Trends, and Performance Exceptions

10.  Case Study: Analysing Operational Performance Across Departments or Teams

Day 5: Practical Data Visualization with R

Module 5: ggplot2, Visual Analytics, and Professional Data Communication

1.      Principles of Effective Data Visualization

2.      Introduction to ggplot2 and the Grammar of Graphics

3.      Creating Bar and Column Charts

4.      Creating Histograms and Distribution Charts

5.      Creating Box Plots for Comparative Analysis

6.      Developing Scatterplots and Relationship Visualizations

7.      Creating Line Charts and Trend Visualizations

8.      Customizing Titles, Labels, Scales, Themes, and Annotations

9.      Designing Professional Analytical Charts and Visual Reports

10.  Practical Exercise: Creating a Complete Performance Visualization Report

Day 6: Statistical Analysis and Regression

Module 6: Practical Statistical Inference and Regression Modelling

1.      Introduction to Statistical Analysis with R

2.      Understanding Samples, Populations, and Statistical Uncertainty

3.      Confidence Intervals and Practical Interpretation

4.      Hypothesis Testing and P-Values

5.      Comparing Groups and Assessing Differences

6.      Correlation and Association Analysis

7.      Introduction to Simple Linear Regression

8.      Multiple Linear Regression and Predictor Variables

9.      Regression Diagnostics, Model Fit, and Interpretation

10.  Case Study: Modelling the Factors Associated with a Business or Operational Outcome

Day 7: Predictive Analytics and Classification

Module 7: Practical Predictive Modelling and Decision Analytics

1.      Foundations of Predictive Data Analysis

2.      Preparing Data for Predictive Modelling

3.      Selecting Predictors and Defining Target Variables

4.      Linear Prediction and Practical Scenario Estimation

5.      Logistic Regression and Binary Outcomes

6.      Interpreting Probabilities, Odds, and Predictive Effects

7.      Classification and Prediction Accuracy

8.      Model Validation and Overfitting

9.      Translating Predictive Results into Practical Decisions

10.  Practical Exercise: Building and Evaluating a Predictive Model for a Real-World Scenario

Day 8: Time-Based Analysis and Forecasting

Module 8: Practical Time Series Analysis and Forecasting

1.      Introduction to Time-Based Data Analysis

2.      Working with Dates and Time Variables in R

3.      Preparing and Structuring Time-Series Data

4.      Identifying Trends, Seasonality, and Cycles

5.      Calculating Growth Rates, Differences, Lags, and Leads

6.      Visualizing Time-Based Performance and Demand

7.      Introduction to Forecasting Methods

8.      Evaluating Forecast Accuracy and Uncertainty

9.      Scenario Analysis and Practical Forecast Interpretation

10.  Case Study: Forecasting Demand, Revenue, Workload, or Operational Performance

Day 9: Automation, Reproducibility, and Reporting

Module 9: Practical R Automation, Reporting, and Analytical Workflow Management

1.      Designing Reproducible R Analysis Workflows

2.      Creating Reusable R Scripts for Recurring Analysis

3.      Functions, Conditional Logic, and Practical Automation

4.      Using Loops and Functional Programming Techniques

5.      Automating Data Cleaning and Validation Tasks

6.      Automating Tables, Statistics, and Visualizations

7.      Introduction to R Markdown and Quarto

8.      Creating Reproducible Analytical Reports

9.      Documentation, Version Control Principles, and Analytical Auditability

10.  Practical Exercise: Automating a Recurring Data Analysis and Management Report

Day 10: Integrated Practical R Analytics Capstone

Module 10: Advanced Practical R Data Analysis and End-to-End Capstone

1.      Integrating the Complete R Data Analysis Workflow

2.      Advanced Data Quality Assurance and Validation

3.      Combining Exploratory, Statistical, Predictive, and Time-Based Analysis

4.      Advanced Data Transformation and Analytical Feature Development

5.      Advanced Visualization and Analytical Storytelling

6.      Model Evaluation, Sensitivity Analysis, and Robustness Checks

7.      Reproducible Analytical Reporting and Professional Presentation

8.      Translating Analytical Results into Actionable Recommendations

9.      Integrated Capstone: End-to-End Analysis of a Real-World Dataset

10.  Capstone Presentation, Evaluation, Findings Review, and 90-Day Practical Analytics Action Plan