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Module Code - Title:


Year Last Offered:


Hours Per Week:













Grading Type:


Prerequisite Modules:

Rationale and Purpose of the Module:

This module will cover an introduction to programming in R and RStudio for data analytics assuming no prior experience. The module will cover data wrangling, data visualisation, summarising data, and simple statistical modelling in R. Students will learn about reproducible research, coding and analytics practices, and be given an introductory overview of RShiny dashboards.


1. Language essentials: objects; functions; vectors; missing values; matrices and arrays; factors; lists; data frames; indexing, sorting, loops; logical operators; packages and libraries. 2. Data wrangling: subsetting; filtering; merging; grouping. 3. Flow control: for, while, if/else, repeat, break. 4. Data visualisation: generating graphics and interactive graphics using ggplot. 5. Probability distributions: built-in distributions in R; densities, cumulatives, quantiles, random numbers. 6. Statistical inference: one- and two-sample inference. 7. Predictive analytics: correlation and regression; tabular data. 8. Reproducible coding practices using RMarkdown. 9. Introduction to RShiny dashboards.

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

On successful completion of this module, students will be able to: 1. Demonstrate proficiency in standard programming constructs in R and RStudio. 2. Examine and evaluate a dataset through visualisation methods. 3. Summarise data using appropriate summary statistics. 4. Develop a pipeline to visualise, summarise, and model data. 5. Apply reproducible coding practices to real-world datasets. 6. Develop interactive visualisation tools for dissemination.

Affective (Attitudes and Values)

On successful completion of this module, students will be able to: 1. Synthesise information across the analytics pipeline for decision-making. 2. Formulate a well-constructed rationale to defend and justify analytics approaches adopted. 3. Display a professional commitment to reproducible data analytics practices.

Psychomotor (Physical Skills)


How the Module will be Taught and what will be the Learning Experiences of the Students:

This module will be block taught online as part of the UL@Work Human Capital Initiative. This module will contribute towards graduates who are KNOWLEDGEABLE (being able to bring their analytics knowledge to bear on real-world problems), RESPONSIBLE (via reproducible analytics practices), ARTICULATE (being able to present their findings to a variety of professional stakeholders), CREATIVE (developing dashboards to disseminate their findings), COLLABORATIVE (sharing best practice in an analytics pipeline).

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Irizarry, R.A. (2020) Introduction to Data Science Data Analysis and Prediction Algorithms with R , Routledge

Other Relevant Texts:

Programme(s) in which this Module is Offered:

Semester - Year to be First Offered:

Module Leader: