Applied Data Skills for Processing & Presenting Data (UG)

I had not coded since my Undergraduate degree, so this course threw me in at the deep end, but it paid off. I can now competently use R Studio to analyse data which will definitely benefit my career development.

Lauren Roberts Applied Data Skills for Processing & Presenting Data

This course has made me enjoy my current role more as it has opened a new avenue for self-development and equipped me with unique skills. I have made new friends and started to grow a different professional network.

Emma Smillie Applied Data Skills for Processing & Presenting Data

Upskilling 

Applied Data Skills for Processing & Presenting Data Microcredential: Online distance learning

Duration: 10 weeks
Credits: 10 Academic Credits (UG)
Delivery: All course content is delivered on Moodle, our virtual learning platform. You will converse with the tutors and fellow students in online forums.
Timetable: Fully online and flexible, with no scheduled classes to attend.
Funding: You are eligible for a fully funded place on this course if you are Scottish-domiciled and/or work for an organisation based in Scotland.
College: College of Medical, Veterinary & Life Sciences
School: School of Psychology & Neuroscience

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Data science skills are becoming increasingly essential in all areas of work, from processing and analysing sales data to understanding and utilising social media analytics to grow a business and expand outreach. A report from the Department for Digital, Culture, Media and Sport (2021) found that 48% of companies were recruiting for roles requiring data skills, but 46% of them struggled to fill these positions. Additionally, manually processing data can be time-consuming and is subject to human error. Learning to transform, summarise, and visualise data using code is a vital skill. This course teaches learners how to turn various types of data into informative and visually appealing reports using the programming language R.

Why this course

This course provides learners with the fundamental skills to transform raw data into informative summaries and visualisations presented in professional, visually appealing reports, presentations, and dashboards that include descriptive statistics and automatically update when the underlying data changes. These techniques will not only save you time on repetitive tasks such as processing monthly data reports, but also reduce errors in reporting.

Upon completion of this course, you will be able to:

  • Use the Tidyverse to clean and process data;
  • Use R to quickly and accurately summarise data;
  • Use ggplot2 to informatively visualise data;
  • Use R Markdown to create reusable report templates.

Course structure

This course is designed for anyone looking to improve their data processing efficiency and ability to generate key insights using the programming language R. Whether you are a beginner or have some experience working with data, this course will help you develop the skills needed to take your data analysis to the next level, helping you to stand out in a highly competitive job market where data science is in high demand. It will cover the following topics: 

Week 1: Introduction to R & RStudio

Week 2: Reports with R Markdown

Week 3: Introduction to Data Visualisation

Week 4: Data Import

Week 5: Data Relations

Week 6: Practice Report

Week 7: Data Tidying

Week 8: Data Wrangling

Week 9: Data Summaries

Week 10: Customising Visualisations & Reports

 

Assessment

  • Final Project (100%) (optional) 

Learners will produce a report, presentation or dashboard of 1,000 words and associated code, that includes summary tables and plots. Due after Week 10.

Learners who choose to submit this assessment will be awarded 10 Academic Credits towards a relevant Master’s degree at the University of Glasgow.

 

Meet the Team

This course is designed and delivered by Dr Emily Nordmann and Professor Lisa DeBruine. 

Emily is a Senior Lecturer and Year 1 Lead for Psychology at the University of Glasgow and has taught programming to beginners since 2017. In 2020, she was awarded the College of Science and Engineering Award for Individual Excellence in Teaching and was named Higher Education Teacher of the Year by the British Psychological Society.

Lisa teaches coding to Postgraduate students and develops web apps and tools in R for reproducible research.

Lisa and Emily are both members of the psyTeachR team, which won the Society for the Improvement of Psychological Science's 2021 Mission Award for their contributions to teaching reproducible coding methods.

For all enquiries related to this course, contact the Upskilling team at upskillingproject@glasgow.ac.uk.

 

Course alteration or discontinuation
The University of Glasgow endeavours to run all courses as advertised. In exceptional circumstances, however, the University may withdraw or alter a course. For more information, please see: Student contract.

Career prospects

This course is designed to equip learners with skills to progress into the following roles and industries: 

  • Positions that require the creation of reports and data sources including customer satisfaction surveys, market data, employee databases, sales records or social media data
  • Positions that use R and RStudio
  • Data Science Professional 

Completion of this course grants potential for: 

  • Further academic study
  • Promotion
  • Increased earning potential
  • New career path

Fees & funding

Funding: If you are based in Scotland and/or work for an organisation based in Scotland, you are eligible for a fully funded place on this course, meaning you can upskill free of charge.

Fee: £799

Discounts: A 20% discount is available to NHS, Civil Service and Third Sector employees. A 10% discount is available to UofG alumni and for group bookings.

Find out more

Entry requirements

It is suggested that learners on this course are educated to at least SCQF Level in English and Mathematics and have an IELTS equivalent of 6.5. Learners will not be asked to prove their academic or professional history.

Learners must have access to a computer on which they can install R and RStudio or access RStudioCloud.