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 AND WAREHOUSING
Code CKIT528
Coordinator Professor FP Coenen
Computer Science
Coenen@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2022-23 Level 7 FHEQ Whole Session 15

Aims

To provide a comprehensive understanding of data warehousing concepts and techniques. To provide an opportunity for students to create a data warehouse using open source technologies and open source public data sets. To provide a comprehensive understanding of why data visualisation is important and how this communicates insights better than traditional reporting techniques. To provide students with an opportunity to create data visualisations and combine such visualisations into a single dashboard so as to tell a "data story".


Learning Outcomes

(LO1) An in-depth knowledge of data warehousing concepts and techniques.

(LO2) A comprehensive and critical understanding of the process of creating effective data warehousing solutions.

(LO3) A systematic understanding of data visualisation techniques and best practice.

(LO4) Hands-on experience of building a data visualisation dashboard using a state of the art visualisation system and how such visualisations may be used to support decision making.

(S1) Organisational skills

(S2) Communication skills

(S3) IT skills

(S4) Teamwork

(S5) Communication and collaboration online participating in digital networks for learning and research

(S6) Learning skills online studying and learning effectively in technology-rich environments, formal and informal

(S7) Team (group) working respecting others, co-operating, negotiating / persuading, awareness of interdependence with others


Syllabus

 

Week 1 Review of the rational for, and benefits of, data warehousing. Common challenges (integration, data cleansing) and different data warehousing architectures (transactional, dimensional). The business case for data warehousing.   Week 2 Design of relational databases (normalising vs. de-normalising, defining dimensions and facts) using public data sources and an open source database platform.   Week 3 Data accuracy and data cleansing; defining rules for data warehousing.   Week 4 Loading data into a data warehouse and ensuring that relevant business case objectives are met.   Week 5 Benefits of visualisation and data storytelling, best practices for visualisation and how to avoid common mistakes, exploration of a selected data visualisation system.   Week 6 The process of building visualisations to answer common business questions illustrated using the visualisation system introduced in week 5.   Week 7 Combining visualisations into a single dash board so as to "tell a story", and to provide insights concerning data stored in a data warehouse and appropriate conclusions yhat maybedrawn.     Week 8 Expanding and building on top of existing insights by adding trends and forecasts; comparison with other types of data analytics, such as predictive and prescriptive data analytics.


Teaching and Learning Strategies

Teaching Method 1 - Online Learning
Description: Weekly seminar supported by asynchronous discussion in a virtual classroom environment facilitated by an online instructor.
Attendance Recorded: Yes
Notes: Number of hours per week that students are expected to devote to reading, research and other individual work to support engagement in the classroom is 7.5.


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours           60

60
Timetable (if known)              
Private Study 90
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
Data warehousing group project Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 2,3,4    15       
Data visualisation group project Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 6,7,8    15       
Essay on data visualisation technology Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 5    10       
Ten discussion questions Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Whole session    50       
Essay on data warehousing technology Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Week 1    10       

Recommended Texts

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