Introduction to R Programming for Data Science Training
R gives analysts a free, reproducible way to clean, analyse and visualise data, replacing manual spreadsheet work that is hard to audit or repeat. The ten days start with the R environment, RStudio and writing your first scripts, then move to vectors, data frames, lists and importing data from CSV files, Excel workbooks and databases. Data wrangling with dplyr and tidyr follows, covering filtering, joining, reshaping and summarising datasets. You then build data quality, validation and compliance checks into your workflow so results can be trusted.
Exploratory data analysis and descriptive statistics teach you to summarise distributions, spot outliers and examine relationships between variables. Visualisation with ggplot2 shows how to produce clear, publication-ready charts, and later sessions introduce building and validating simple models. Exercises use realistic datasets drawn from business, health and development work. Analysts, researchers, M&E officers, statisticians and graduates moving into data roles with little or no coding experience will find the pace manageable. By the end you can take a raw dataset through cleaning, analysis and visualisation in a single reproducible R script.
About this course
This comprehensive 10-day programme equips participants with practical expertise in introduction to r programming for data science. Designed for working professionals across Africa, the course combines theoretical foundations with real-world case studies and hands-on exercises. Participants leave with immediately applicable tools and frameworks.
Whether you're advancing your current role or transitioning to a specialization, this course provides the skills and confidence needed to excel in today's data-driven environment.
Throughout the programme, participants work through realistic scenarios drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments, examining how introduction to r programming for data science is applied in practice and where it most often breaks down. Delegates will develop practical skill in designing appropriate frameworks, selecting and applying the right tools for data structures and data import, interpreting findings, and communicating conclusions clearly to colleagues, management and external stakeholders. Sessions move deliberately from principles to application, so that each technique introduced is practised against a realistic problem before the programme moves on — strengthening both individual effectiveness and the wider capability of the organizations delegates return to.
Training Objectives
By the end of this course, participants will be able to:
- Develop robust introduction to r programming for data science frameworks that support successful implementation and measurable outcomes.
- Analyze performance using recognized evaluation methods drawn from data structures and data import.
- Design practical systems that strengthen accountability, transparency and evidence generation.
- Evaluate outcomes and assess longer-term impact across a range of operating contexts.
- Apply relevant tools, frameworks and analytical methodologies confidently and appropriately.
- Integrate data visualisation with ggplot2 into existing organizational processes and reporting structures.
- Strengthen decision-making and operational effectiveness through evidence-based professional practice.
What you'll learn
Key Takeaways
By the end of the Introduction to R Programming for Data Science Training, participants will leave with:
- A comprehensive working understanding of introduction to r programming for data science principles and performance-measurement methodology.
- Enhanced ability to design practical systems and evaluate outcomes reliably.
- Improved competency in data structures and data import, reporting structures and analytical interpretation.
- Practical techniques for translating findings into strategic insight and clear recommendations.
- Greater confidence in applying introduction to r programming for data science and assessing measurable results.
- Stronger capability to support organizational learning and continuous performance improvement.
- Improved approaches to strengthening sustainability, accountability and operational effectiveness.
Training Methodology
This training course adopts a practical, participant-centered learning approach that builds introduction to r programming for data science expertise, strengthens analytical capability and develops confident professional judgement. The methodology combines specialist facilitation, applied hands-on experience and globally recognized professional practice, so that delegates return able to apply what they have learned immediately rather than in theory only.
Methodology includes:
- Expert-led sessions facilitated by experienced introduction to r programming for data science practitioners and subject-matter specialists.
- Practical application of the frameworks, tools and techniques used in data structures and data import.
- Real-world case studies drawn from technology organizations, digital transformation programmes, data-driven enterprises, and public sector IT environments.
- Tool-based demonstrations of the systems, templates and reporting applications used in current practice.
- Interactive discussion and scenario-based learning focused on realistic challenges and decision points.
- Structured group exercises with facilitator feedback, reinforcing learning through practice rather than lecture alone.
Day 1: The R Environment and First Scripts
- Installing R and RStudio; panes, projects, and working directories
- R syntax essentials: assignment, operators, functions, and comments
- Atomic vectors, coercion rules, and vectorised arithmetic
- Installing and loading packages from CRAN; using the tidyverse metapackage
- Reading help files, vignettes, and reproducing examples
- Writing a first commented script and saving it inside a project
- Measuring performance and assuring quality in the r environment and first scripts
Day 2: Data Structures and Data Import
- Factors, matrices, lists, and data frames compared
- Subsetting with indices, names, and logical conditions
- Importing CSV, Excel, SPSS, and Stata files with readr, readxl, and haven
- Connecting to databases and pulling tables with DBI and dbplyr
- Inspecting structure, classes, and missing values on import
- Handling encodings, dates, and locale issues in African survey datasets
- Data, evidence and information requirements for data structures and data import
Day 3: Data Wrangling with dplyr and tidyr
- The five core verbs: filter, select, mutate, arrange, summarise
- Grouped operations and the pipe operator for readable pipelines
- Reshaping between wide and long form with pivot_longer and pivot_wider
- Joining datasets: inner, left, anti, and semi joins with key checks
- Recoding, binning, and deriving indicator variables
- String and date manipulation with stringr and lubridate
- Core principles and terminology underpinning data wrangling with dplyr and tidyr
Day 4: Data Quality, Validation, and Compliance Workflows
- Profiling datasets: duplicates, outliers, implausible values, and missingness patterns
- Writing assertion and validation rules that fail loudly
- Imputation options and documenting the treatment of missing data
- De-identification and handling personal data under data protection obligations
- Audit trails: logging cleaning decisions and versioning datasets
- Building a reusable cleaning function library for recurring submissions
- Roles, responsibilities and stakeholder coordination in data quality, validation, and compliance workflows
Day 5: Exploratory Data Analysis and Descriptive Statistics
- Measures of central tendency, dispersion, and distribution shape in R
- Frequency tables, cross-tabulations, and weighted summaries
- Correlation matrices and pairwise exploration
- Detecting skew, heteroscedasticity, and influential points visually
- Producing summary tables for reports with gtsummary or similar
- Framing exploratory findings as analytical questions for stakeholders
- Practical tools, templates and techniques for exploratory data analysis and descriptive statistics
Day 6: Data Visualisation with ggplot2
- Grammar of graphics: data, aesthetics, geoms, scales, and facets
- Selecting chart types for comparison, distribution, composition, and trend
- Themes, colour scales, annotation, and accessible palettes
- Faceted small multiples for regional and segment comparisons
- Exporting publication-quality figures at controlled size and resolution
- Designing a visualisation set that supports a specific stakeholder decision
- Documentation, record-keeping and reporting requirements
Day 7: Statistical Inference and Hypothesis Testing
- Sampling distributions, standard errors, and confidence intervals in R
- t-tests, ANOVA, and non-parametric alternatives
- Chi-square and proportion tests for categorical survey data
- Assumption checking and choosing an appropriate test
- Effect sizes, power considerations, and multiple comparison adjustment
- Reporting test results honestly, including limitations
- Regulatory, ethical and compliance dimensions of statistical inference and hypothesis testing
Day 8: Regression and Predictive Modelling
- Simple and multiple linear regression with lm; interpreting coefficients
- Diagnostics: residual plots, multicollinearity, and influence measures
- Logistic regression for binary outcomes and odds ratio interpretation
- Train/test splits, cross-validation, and guarding against overfitting
- Classification and regression trees plus random forests as comparators
- Validation metrics: RMSE, R-squared, accuracy, sensitivity, and AUC
- Practical exercise and facilitated review
Day 9: Automation, Functions, and Reproducible Reporting
- Writing user-defined functions with arguments and defaults
- Control flow and iteration with apply functions and purrr map
- R Markdown and Quarto documents producing Word, PDF, and HTML output
- Parameterised reports that regenerate for each region or period
- Introduction to interactive dashboards with Shiny components
- Version control habits and project structure for team collaboration
- Reviewing outcomes and capturing lessons learned
Day 10: Applied Analytics Project and Communicating Findings
- End-to-end project: import, clean, model, and visualise a supplied organisational dataset
- Translating model output into plain-language conclusions and recommendations
- Structuring an analytical brief for executives and non-technical audiences
- Handling questions on uncertainty, data limitations, and assumptions
- Coordinating analysts, subject experts, and IT in a data-driven initiative
- Presentation of project results with peer and facilitator review
- Standards, frameworks and good practice governing applied analytics project and communicating findings
Introduction to R Programming for Data Science is aimed at people who work with organisational data in spreadsheets or statistical packages and now need a reproducible, code-based workflow in R.
- Monitoring and evaluation officers building indicator dashboards and survey analyses
- Research assistants and survey statisticians moving from SPSS, Stata, or Excel to R
- Data analysts and business intelligence analysts producing recurring analytical reports
- Epidemiologists, health information officers, and public health data staff
- Economists, actuarial and risk analysts fitting descriptive and predictive models
- Data quality and compliance officers responsible for validating datasets before release
- Programme and portfolio managers leading data-driven transformation initiatives who must interpret analytical output
Participants who complete all ten days receive a Certificate of Completion from Vision Reach Global Consultancy, confirming their ability to write R scripts that import, clean, analyse, model, and visualise organisational data reproducibly. The certificate records competence in R-based statistical analysis and reporting rather than any external accreditation.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Choose Where You Want to Learn
Introduction to R Programming for Data Science Training Venues
KenyaMombasaNext Nov 2· 13 dates$3,200- KenyaNaivashaNext Nov 9· 13 dates$3,000
- KenyaNairobiNext Nov 16· 13 dates$3,000
- KenyaKisumuNext Nov 2· 13 dates$3,000
- KenyaMachakosNext Nov 9· 13 dates$3,200
UgandaKampalaNext Nov 9· 13 dates$3,800
RwandaKigaliNext Nov 2· 13 dates$3,800
South AfricaCape TownNext Nov 2· 13 dates$7,600
TanzaniaZanzibarNext Nov 23· 13 dates$5,400
UAEDubaiNext Nov 2· 13 dates$7,600- South AfricaPretoriaNext Nov 9· 13 dates$6,400
- South AfricaJohannesburgNext Nov 9· 13 dates$6,800
- TanzaniaDodomaNext Nov 2· 13 dates$3,600
- TanzaniaDar es SalaamNext Nov 2· 13 dates$4,000
- TanzaniaArushaNext Nov 16· 13 dates$3,800
NigeriaAbujaNext Nov 16· 13 dates$5,600- NigeriaLagosNext Nov 23· 13 dates$5,600
GhanaAccraNext Nov 23· 13 dates$9,000
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Frequently Asked Questions About Introduction to R Programming for Data Science Training
Everything you need to know about this course before you register.
By the end of the Introduction to R Programming for Data Science Training programme, you'll be able to apply statistical techniques and data analysis methodologies to real organizational challenges, design and implement data visualization strategies that drive stakeholder decision-making, build and validate predictive and descriptive models using industry-standard tools, and integrate quality assurance and compliance frameworks into data management workflows. The full breakdown of topics is covered session by session in the Course Outline tab above.
Introduction to R Programming for Data Science is aimed at people who work with organisational data in spreadsheets or statistical packages and now need a reproducible, code-based workflow in R. Typical delegates include Monitoring and evaluation officers building indicator dashboards and survey analyses, Research assistants and survey statisticians moving from SPSS, Stata, or Excel to R, Data analysts and business intelligence analysts producing recurring analytical reports, Epidemiologists, health information officers, and public health data staff, Economists, actuarial and risk analysts fitting descriptive and predictive models, Data quality and compliance officers responsible for validating datasets before release, and Programme and portfolio managers leading data-driven transformation initiatives who must interpret analytical output.
Introduction to R Programming for Data Science Training typically runs as 10 Days. It's available as in-person classroom training — every course can also be delivered on-site for your team on dates that suit you.
Introduction to R Programming for Data Science Training is scheduled in-classroom in Mombasa, Kenya, Naivasha, Kenya, Nairobi, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Introduction to R Programming for Data Science Training starts November 16, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Introduction to R Programming for Data Science Training from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Introduction to R Programming for Data Science Training is pitched at foundation to intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Introduction to R Programming for Data Science Training vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Introduction to R Programming for Data Science Training can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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Training by Country
Introduction to R Programming for Data Science Training by Country
- Introduction to R Programming for Data Science Training in GhanaGHClassroom in Accra
- Introduction to R Programming for Data Science Training in KenyaKEClassroom in Mombasa, Naivasha, Nairobi, Kisumu, Machakos
- Introduction to R Programming for Data Science Training in NigeriaNGClassroom in Abuja, Lagos
- Introduction to R Programming for Data Science Training in RwandaRWClassroom in Kigali
- Introduction to R Programming for Data Science Training in South AfricaZAClassroom in Cape Town, Pretoria, Johannesburg
- Introduction to R Programming for Data Science Training in TanzaniaTZClassroom in Zanzibar, Dodoma, Dar es Salaam, Arusha
- Introduction to R Programming for Data Science Training in UgandaUGClassroom in Kampala
- Introduction to R Programming for Data Science Training in the UAEAEClassroom in Dubai
- Live Online Introduction to R Programming for Data Science TrainingInstructor-led, joined from anywhere
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