R Foundations for Data & Scientific Computing
Build a strong practical foundation in R for programming, data manipulation, statistical computing, and scientific visualization and prepare yourself for advanced work in Data Science, biostatistics, research, and analytics.
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January 18, 2027
What you will develop
You will develop the ability to write clean, efficient R programs, manipulate and transform real-world data with the tidyverse, perform statistical computing with base R, and create clear, publication-ready visualizations that support data-driven decisions.
The R skills you need before going further
Data Science, biostatistics, statistical computing, modern data applications, research analytics, and reproducible reporting all depend on strong R skills and familiarity with its ecosystem.
You will learn through structured instruction, guided demonstrations, and practical exercises designed to build confidence and turn R knowledge into practical skills. Strong emphasis is placed on writing clean, efficient, and maintainable R code; using the tidyverse for high-performance data manipulation; and using base R and modern packages to work with real-world datasets, including the messy and unstructured data commonly encountered in practice.
What you will learn
A comprehensive, structured curriculum covering the essential R skills, tools, and techniques needed for practical data and scientific computing.
Build a strong foundation in R programming, covering core syntax, data types, operators, control flow, functions, packages, and basic programming workflows for data and scientific computing.
Develop practical skills for working with R's core data structures, including vectors, matrices, arrays, lists, data frames, and factors, with emphasis on selecting, manipulating, and organizing data.
Develop proficiency in efficient data manipulation using dplyr to select, filter, transform, group, and combine data, applying tidy data principles to practical tasks. Participants will also use stringr and tidyr to reshape, clean, and transform data, applying functions across data structures.
Learn to create clear, analytical, and publication-ready visualizations with ggplot2, covering distributions, relationships, comparisons, grouped data, faceting, customization, statistical trends, and time-series graphics.
Built for your next step
R and its core data ecosystem are widely used across data science, biostatistics, research, analytics, and academic computing. Strong skills in R, tidyverse, dplyr, tidyr, and ggplot2 provide the practical foundation needed to work with data, automate tasks, build statistical reports, conduct research, and progress into more specialized technical roles.
Prepare data and build skills for advanced Data Science, statistics, and research.
The complete data workflow in R — clean, transform, analyze, and visualize clinical and public health data.
Acquire skills to conduct reproducible research, statistical analysis, and publication-ready reporting.
Automate repetitive data tasks and work efficiently with datasets of any size.
Analyze research data, uncover insights, and support evidence-based decisions.
Develop practical programming skills that prepare you for academic study, research work, and the careers employers look for.
What you will be able to do
Write clean, efficient, and reproducible R programs using functions, control flow, and the apply family.
Work fluently with R's core data structures including vectors, matrices, lists, data frames, and factors.
Prepare, clean, reshape, and join real-world data with dplyr, tidyr, and stringr following tidy data principles.
Create clear, professional, and publication-ready data visualizations with ggplot2, from basic charts through faceting, and customization.
Apply R to practical data analytics tasks, including grouped aggregation, multi-table joins, and reproducible reporting.
Build a strong foundation for advanced topics such as statistical modeling, machine learning, and data science.
Learn Skills That Move Your Career Forward
We don't just teach theory. We build job-ready professionals. Every lesson is hands-on, project-based, and designed around the real problems you face in your day-to-day work. Our instructors are industry experts who have developed real products at scale, people who've solved the exact problems you'll encounter.
Questions about this course?
Is this a Data Science or Machine Learning course?
No. This is a practical R foundation course. It develops the programming, statistical computing, data preparation, and visualization skills you need before progressing to more advanced Data Science, statistics, biostatistics, or research-focused training.
Who is this course designed for?
It is designed for aspiring Data Scientists and Statisticians, biostatisticians and health researchers, Data and Business Analysts, researchers, data professionals, and university or TVET students who want a strong practical foundation in R and its core data ecosystem.
Do I need prior R programming experience?
No. The course starts with R fundamentals and progressively introduces the tidyverse, dplyr, tidyr, and ggplot2. It is suitable for beginners as well as learners who've some R experience but want to strengthen their practical skills, particularly the emphasis on writing clean, efficient, and reproducible analysis-ready code.
How is the course delivered?
Classes are held in person at our training center in Upper Hill, Nairobi, led by experienced instructors. Each session combines live demonstrations, hands-on exercises, and structured learning. Sessions run Monday to Friday from 8:00 AM to 4:30 PM (EAT).
What do I need to participate in the course?
Just bring a laptop capable of running R and RStudio plus the required development tools, plus a willingness to participate in hands-on exercises. If you're using a work-issued laptop, make sure R and RStudio are already installed or that you have admin rights to install them yourself.
Can this course be offered online?
Yes — absolutely. Our standard format is in-person, but we also run the full course online through live, instructor-led sessions. You get the same curriculum, exercises, and mentorship, just delivered remotely.
Can the course be offered at our premises, at our own pace?
Yes. We offer fully customized on-site training at your premises, paced to suit your team. Content, schedule, and depth are all tailored to your organization.
Will I receive a certificate after completing the course?
Yes. Participants who successfully complete the course requirements receive a certificate of completion issued by STEM RESEARCH.
Learn from an experienced industry practitioner
John Indika
John is a Senior Software Engineer and Data Scientist with extensive experience in Python, R, scientific computing, data science, machine learning, and software engineering. He is the developer of the Stemfard API, a step-by-step mathematics computation platform designed to make mathematical and STEM computations programmable and accessible. He has also trained professionals, researchers, and students across universities, government institutions, NGOs, and other organizations both locally and internationally.
His approach combines software engineering, mathematical and scientific computing, and practical data skills, with an emphasis on clear explanations, hands-on learning, and solving real-world technical problems. He focuses on writing clean, efficient, maintainable, and high-performance code designed to scale and meet production requirements.
Build your R foundation.
Start with the essential skills you need to work confidently with R, data, and statistical computing.