MyGlasgow Students

Applied Data Skills.

What will I learn from this course?

This course provides an overview of the basic skills needed to turn raw data into informative summaries and visualisations presented in professional reports, presentations, and dashboards. The course will introduce learners to R, a programming language that can help automate working with data. The course will cover importing and processing data from spreadsheets, producing data summaries of descriptive statistics in tables, creating beautiful and informative visualisations, and constructing reports that automatically update when the underlying data changes. You will also learn to use Generative AI to ethically and responsibly assist with coding.  

While the technical focus is on data processing in R, the course is designed for students from all disciplines, including those with no prior programming experience. Examples and applications will highlight how data skills are used across sectors—such as public policy, health, education, creative industries, and social sciences. 

When will this course be available?

Semester 2, 2025

How many credits is this course?

20 credits

What is the course code?

The course code is PSYCH1012. More information on how to enrol can be found on the registration webpages.

How will I learn on this course?

This course will be taught over 10 weeks. There will be one two-hour workshop and one one-hour workshop each week for a total of three hours per week of contact time. These sessions will run in weeks 1-5 and 7-10 of semester 2, with a break for reading week and an optional drop-in GTA session in week 11.  

Attendance at 75% of sessions will be required to be awarded credit. In each two-hour session, there will be a short (~15 minute) lecture to begin, and then the session will focus on working through a series of coding exercises with each week building on skills and introducing new functions. In the one-hour session, you will work through coding exercises that reinforce the new concepts learned that week that build to support the summative assessment. Each session will have a lecturer and GTA present to support and we will also implement pair programming where you work with another student to complete the exercises.  

All workshops are hands-on and highly practical, designed to build skills week by week. Students will work collaboratively through exercises during each session, with direct support from staff and GTAs. The emphasis is on ‘learning by doing’ rather than lecture-based delivery. 

There will also be a component of self-directed work each week in the form of a small amount of reading (in addition to the workbook), weekly quizzes to test your knowledge on the concepts covered, and you will be expected to complete any of the exercises you did not complete in class. 

How will I be supported academically on this course?

In addition to the practical workshops that form the contact hours, you will have access to staff office hours. The main additional form of support will be conducted via Microsoft Teams where a dedicated Graduate Teaching Assistant will answer questions and help troubleshoot code or provide further explanation. You will also learn how to use Generative AI as an effective personal tutor.

Recognising that troubleshooting is an essential part of learning to code, time will be set aside during workshops for addressing common issues, and students will be supported by both staff and GTAs. Additional help is available through Microsoft Teams and drop-in sessions. 

What unique learning experiences will I have on this course?

You will collaborate to solve real-world data challenges, culminating in an individual project that integrates technical and communication skills. 

 What skills will I learn on this course? 

You will develop a broad and transferable skillset aligned with the University’s Future Skills Taxonomy. These include technical, analytical, and interpersonal competencies essential for thriving in a data-rich and interdisciplinary world. In addition to technical data and coding skills, you will develop transferable skills in digital communication, critical thinking, and collaborative problem solving—skills that are valuable across all four Colleges and a wide range of careers. The course also introduces students to ethical and effective use of generative AI for coding support, including how to construct prompts that enhance learning and reflect responsible digital practice. 

Students will develop: 

• Data literacy and digital confidence: Import, clean, summarise, and visualise data using R and RMarkdown. 

• Computational thinking: Break down problems and build reproducible, logical workflows. 

• Problem-solving and decision-making: Design solutions using real-world datasets and apply ethical considerations when using AI for coding support. 

• Communication and content creation: Translate technical analyses into clear and compelling outputs (e.g., dashboards and reports) for diverse audiences. 

• Collaboration and teamwork: Engage in peer programming and collaborative troubleshooting. 

• Self-regulation and reflection: Identify personal learning progress and troubleshoot independently and with others. 

These map to the following key Future Skills areas: 

• Solve Problems and Make Decisions 

• Analyse Context, Information, Data and Evidence 

• Communicate Effectively to Others 

• Work With Others 

• Through Content: Create and Present Information 

• Use Digital Tools with Integrity 

Skills will be surfaced through: 

• Weekly practical workshops using real-world datasets and peer programming techniques. 

• Formative activities such as weekly coding quizzes and guided exercises. 

• Authentic assessments, including the ‘backwards engineering’ task and a fully reproducible technical report. 

• Reflection opportunities, including peer review and prompt-based journaling about AI use and skills development. 

• Collaborative troubleshooting during class and via Microsoft Teams, where learners articulate problems and support peers. 

Students will know they have learned these skills through: 

• Monitoring their progress through structured feedback from quizzes, peer reviews, and summative assessments. 

• Reflecting on their skill development through guided prompts embedded in the course materials and assessment briefs. 

• Receiving targeted feedback using assessment rubrics that explicitly highlight skill-related criteria, such as clarity of communication, problem-solving strategy, and ethical use of digital tools. 

• Identifying their growing independence and confidence in applying coding and analytical techniques to novel problems. 

How will I be assessed on this course?

Full details of each assessment will be provided in dedicated assessment briefs. The descriptions below are indicative summaries and are designed to support student understanding of overall course structure and expectations.

Sequence	Assessment type (drop down menu)	Group or Individual Assessment	Weighting    (indicate % or Pass/Fail
Summative	Weekly MCQs: There will be a weekly multiple-choice quiz administered on Moodle that tests and consolidates the functions and concepts learned that week. You will be given two attempts on each quiz.	Individual	10%
Summative	Backwards engineer: You will be given a dataset and a finished report on that data. Your task is to write the code that produces the report. You will be asked to peer review other submissions and will receive a mark for participation (7.5% for submitting, 7.5% for peer-review).  A solution file and walkthrough video will be released as instructor feedback.    Coding is not a skill that can be learned by cramming. Additionally, we place a large emphasis on peer coding and therefore you must attend 75% of all classes to receive a grade for this assessment.	Individual	15%
Summative	Technical brief: You will be given a choice of two datasets and a technical brief for the content of the report. Your task is to write a fully reproducible report using R and RMarkdown that clearly and effectively presents and summarises the data to provides key insights. Individual feedback will be provided via a rubric on  clearly defined marking criteria in addition to an overall written comment.	Individual	75%

Who is the course leader?

Professor Emily Nordmann & Professor Lisa DeBruine are leading this course.