Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Data Visualisation
Code COMM740
Coordinator Dr MA Pogson
Communication and Media
Mark.Pogson@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 7 FHEQ Second Semester 15

Aims

At the end of the module, students will have a good understanding of different types of data and how to visualise them effectively. As part of this, they will build on their coding skills and knowledge of plotting libraries, which will help them in other areas of their studies. The module will also prepare them for future positions in research and industry, where data science and coding skills are in growing demand, especially data visualisation. The module will be taught and assessed to reflect an emphasis on gaining practical coding skills, underpinned by theoretical understanding.


Learning Outcomes

(LO1) Identify different types of data and relationships between data.

(LO2) Select methods to visualise data which reflect the characteristics of the data and the intended audience.

(LO3) Use plotting libraries to create informative visualisations of data.

(LO4) Use interactivity, animations and custom methods to create innovative and insightful visualisations.

(S1) Numeracy/computational skills - Problem solving

(S2) Numeracy/computational skills - Confidence/competence in measuring and using numbers

(S3) Critical thinking and problem solving - Creative thinking

(S4) Skills in using technology - Information accessing


Syllabus

 

The module will develop skills in data visualisation by building on the data science and coding skills acquired in Semester 1. The module content will be accessible through Canvas, and links will also be provided to other online resources. Students will be expected to read and work through set materials outside of contact hours. They will also be expected to search for supplementary resources, particularly as part of their coursework.

Coding will use a modern scripting language, e.g. Python. The module may include the following indicative content:

BLOCK ONE – INTRODUCTION TO DATA VISUALISATION
- Concepts behind data visualisation, critiques of different approaches, how to represent different types of data and relationships between data
- Static visualisation methods, e.g. using Matplotlib and Seaborn libraries in Python

BLOCK TWO – VISUALISATING SPATIAL DATA
- Static and interactive mapping of 2D spatial and geospatial data , e.g. using Plotly
- Immersive visualisation of spatial data, e.g. using Google Earth

BLOCK THREE – MULTIDIMENSIONAL PLOTS, ANIMATIONS, CUSTOM METHODS AND DASHBOARDS
- Using 3D axes and other approaches to visualise multiple dimensions, including animations
- Custom plots, interactivity and dashboards, e.g. using Bokeh


Teaching and Learning Strategies

Teaching method: Lecture/Workshop
Description: Teaching will be delivered through combined lectures and workshops, which will introduce students to key concepts and techniques, and give students chance to discuss and work on these in a computer lab.
This activity may be online or on campus and could be subject to changes.
Scheduled directed student hours: 22
Unscheduled directed student hours: 128
Attendance recorded: Yes
Notes: Description of how self-directed learning hours may be used:
Students should complete the assigned reading and exercises between taught sessions. They should start working on their assignments as soon as the assignments are introduced in class. It is also a good idea for students to book themselves onto any offline or online tutorials or book individual appointments with the aim of improving their academic writing and critical thinking skills.


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 22

          22
Timetable (if known)              
Private Study 128
TOTAL HOURS 150

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Portfolio of visualisations and descriptions. 1400 words (excluding code)  1200-1600 words    100       

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.