Data Science Foundations with R
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Next Start
October 12, 2026
(Offered monthly)Duration
5 Days
ClassroomProgram Fee
$1,250
Exclusive 16% VATOther Dates
November 9, 2026
December 14, 2026
December 14, 2026
Course Description
This comprehensive professional course provides a complete foundation in data science using R, starting from the very basics. Participants will learn R programming from scratch, then progress through data manipulation with dplyr and tidyr, data visualization with ggplot2, statistical analysis, and machine learning. The curriculum emphasizes practical, hands-on skills using R's powerful scientific ecosystem.
The program begins with R fundamentals and progressively builds through data wrangling, exploratory data analysis, statistical modeling, and predictive analytics. Participants will work with real-world datasets, build visualizations, and develop the practical skills needed for data science roles - all while learning R from the ground up.
Prerequisite note: This course does not assume any prior R knowledge. The curriculum starts with R fundamentals and builds progressively to advanced data science applications. Basic computer literacy is recommended.
Course Outline
This course is structured into 5 core modules, each carefully designed to build progressively on the previous one.
Learning Outcomes
Technical Skills
- Write R code from scratch for data science applications.
- Work with R data structures including vectors, matrices, lists, and data frames.
- Write functions, use control flow, and handle errors in R.
- Perform data manipulation with dplyr (filter, select, mutate, summarise, group_by).
- Reshape and transform data with tidyr (pivot_longer, pivot_wider, separate, unite).
- Import, clean, and preprocess data from multiple sources using readr and readxl.
- Create professional data visualizations with ggplot2.
- Conduct exploratory data analysis to uncover patterns and insights.
- Apply statistical methods and hypothesis testing using base R and stats.
- Build and evaluate predictive models using caret and tidymodels.
- Implement machine learning algorithms including regression, classification, and clustering.
- Evaluate model performance using appropriate metrics and validation techniques.
Career Benefits
- Build a strong, practical foundation in R programming for data science.
- Learn to solve real-world data problems using R's scientific ecosystem.
- Master the complete data science workflow from R basics to model deployment.
- Gain hands-on experience with R, dplyr, tidyr, ggplot2, and caret.
- Develop proficiency in data manipulation, visualization, and statistical analysis.
- Build a portfolio of data science projects demonstrating practical skills.
- Prepare for advanced roles in data science, analytics, and machine learning.
- Establish a solid foundation for specialized study in statistical modeling and R development.
Who Should Attend
- Beginners — Individuals with no prior R experience wanting to enter data science.
- Aspiring Data Scientists — People looking to build R skills for data science roles.
- Career Changers — Professionals transitioning into data science and analytics.
- Data Analysts — Analysts wanting to transition from Excel/SQL to R for data analysis.
- Researchers & Academics — Individuals applying R to research data analysis.
- Students — Learners building essential data science skills for their careers.
- Business Professionals — Anyone who wants to use R for data-driven decision making.
- Data Enthusiasts — Individuals curious about data science and R programming.
Prerequisites & Requirements
Prerequisites
- Basic computer literacy and familiarity with file management.
- Ability to install software (R and RStudio).
- Basic high school mathematics (algebra, functions) is recommended.
- No prior R experience is required.
- A willingness to learn programming and work with data.
- Curiosity and enthusiasm for data-driven problem solving.
Requirements
- Laptop with at least 2GB RAM (GB recommended for smoother performance).
- All learning facilities are provided on-site, including high-speed internet, projectors, writing materials, and refreshments.
- Administrative rights on your laptop to install R, RStudio, and necessary packages.
- Sufficient free disk space (at least 2GB) for software installation and project files.
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