2024/25 Undergraduate Module Catalogue

MATH1603 Data Science & Communication

20 creditsClass Size: 60

Module manager: Francesco Cosentino; Pierre-Philippe Dechant
Email: F.Cosentino@leeds.ac.uk; P.P.Dechant@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2024/25

Pre-requisite qualifications

Acceptance onto the BSc Data Science programme

This module is not approved as a discovery module

Module summary

This module examines the nature of data science, the data science lifecycle and the strategic organisational role of data scientists. It introduces students to a high-level approach to analysis of data in different formats and lays the foundation to working at the level of raw data, experimentation, and reproducible programming pipelines. A key aspect is the interdisciplinary and integrated nature of the subject, in particular the social science dimension around communication, collaboration, design, and ethics.

Objectives

This module aims are to introduce:
- learners to the hands-on ways of working of a data scientist.
- different types of data such as textual, geospatial and image data, along with numerical vector data, which motivates study of algebra in other mathematics modules.
- students to learning via a series of collaborative and interactive workshops
- an approach to the subject from high-level data science questions before delving into more technical aspects in later modules.
- the iterative, integrated and interdisciplinary nature of the subject, combining design cycles of continuous improvement, collaborative team working and hands-on active learning in digital environments.
- the cognitive basis behind study skills, communication, and collaboration, and to embed these into their practice as reflective data science practitioners.
- the creative problem-solving process, the scientific method and data science design cycles
- working effectively in teams
- communicating their work via data storytelling and visualisation
- experimenting with and monitoring their own learning
- the open-ended and interdisciplinary nature of data science, appreciating the crucial importance of domain and social science knowledge that complements the mathematical and computer science technical knowledge,
- assessing their data science work in a wider organisational or societal context and evaluating the ethical dimension thereof
- evidence-based research behind these and reinforced by active practice.
- a framework for a data science mindset, to which to add later specialist knowledge in different disciplines.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Explain the nature of data science, the importance of domain knowledge and the design approach, and apply basic principles to creative problem solving.
2. Work with different types of non-vector data such as textual, geospatial, and image data.
3. Investigate basic data science questions both in a high-level software environment and with basic programming pipelines for raw data.
4. Describe key aspects of the data science life cycle and related iterative optimisation cycles such as the scientific method and the design approach.
5. Articulate the role of a data scientist within an organisation as a key communication interface, in the context of organisational strategic goals, and with a view to achieving impact & effecting change.
6. State basic facts about the evidence-based research behind effective teams, and apply these principles to collaborative work.
7. Understand and apply basic cognitive principles of communication, e.g. to logical technical arguments, data visualisation, narrative formats and data storytelling for specialist and non-specialist audiences.
8. Discuss fundamental ethical issues in data science around narrative persuasion, , reproducibility, consent, and use of personal data with commercial or political interests.
9. Search for, evaluate, and select information from different sources, using digital and non-digital tools, and attributing results with academic integrity.
10. Explain and apply basic natural language analysis techniques e.g. to textual data curation, word clouds, and sentiment analysis.
11. State basic facts about the cognitive basis of learning, apply these to their own learning practice, and reflect on their learning in the strategic context of professional skills benchmarks.
12. Apply basic techniques of geospatial data analysis, data curation, visual design and data visualisation, and work with images and videos.
13. Recognise differences in organisations’ strategic contexts, purpose and commercial interests, and articulate societal, economical and political aspects of data science issues.

Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SLO1. Articulate ethical questions surrounding different organisations’ operations.
SLO2. Communicate and visualise content for diverse audiences.
SLO3. Follow basic principles for effective teamwork and articulate the value of diversity.
SLO4. Develop resilience strategies and pragmatism to cope with uncertainty, ambiguity and open-endedness of real-world issues.
SLO5. Negotiate interdisciplinary perspectives and tensions at the intersection of behavioural sciences, business, and technical subjects.
SLO6. Think strategically about own development as a life-long learner.
SLO7. Perform critical analyses and solve problems creatively, applying basic principles of design thinking.
SLO8. Practise an active and reflective approach to learning, with a growth mindset and deliberate practice.

Syllabus

1. The nature of data science, the data science lifecycle and the organisational role of a data scientist; the open-ended nature of data science projects and design challenges; creative problem solving and critical analysis
2. Asking basic data science questions from a high-level perspective and in appropriate software environments such as business analytics tools PowerBI, Tableau; QGIS, Gale Digital Lab etc; working with different non-vector data formats such as textual, geospatial and image data
3. Fundamentals of social science aspects for
- communication: narrative versus paradigmatic pathways, good writing practice, data storytelling, effecting change, narrative persuasion, ethics, marketing etc; information searching, referencing; data visualisation, Schneiderman’s mantra, visual design; popular science and communication to non-experts
4. collaboration: collective intelligence and effective teamwork, importance of domain knowledge, interdisciplinary working, integration of professional competencies (knowledge, skills and behaviours)
5. cognition: memory, study skills, experimentation, active learning, deliberate practice, reflective learning
6. Virtuous cycles and optimisation: the scientific method, design thinking, group intelligence, evolution, reflective practice; role of optimisation algorithms in data science
7. Beginnings of working with raw data and reproducible data pipelines in python: Jupyter notebooks, mark-up/LaTeX, exploratory data analysis (EDA), data visualisation
8. Textual data: basics of natural language processing, word clouds, bigrams, sentiment analysis; data curation, web scraping e.g. Google Trends/Analytics, social media data or Wikipedia; complexity case study; visualisations such as bar chart race
9. Geographical information systems (GIS), sustainability case study
10. Image data manipulation: binary symmetric channel, communication and basic ideas of information theory, noisy channels and error-correction
11. Organisational and societal context: organisational strategic goals, communicating recommendations, business needs, marketing, automation and customer segmentation; competition in the communication marketplace; political, societal and economical aspects, ethics; Schelling model, polarisation, echo chambers, role of recommender systems

Methods of Assessment

We are currently refreshing our modules to make sure students have the best possible experience. Full assessment details for this module are not available before the start of the academic year, at which time details of the assessment(s) will be provided.

Assessment for this module will consist of;

2 x Coursework

Teaching methods

Delivery type	Number	Length hours	Student hours
Practical	10	2.00	20.00
Practical	10	3.00	30.00
Independent online learning hours			60.00
Private study hours			90.00
Total Contact hours			50.00
Total hours (100hr per 10 credits)			200.00

Opportunities for Formative Feedback

Learners will regularly produce work in the hands-on teaching sessions, and get peer and formative feedback, including for preparation for the group project and portfolio. Some information searching, written work and presentation tasks will be set with feedback opportunities. The students can then act on this feedback for their project or reflective portfolio.

Reading list

The reading list is available from the Library website

Last updated: 22/10/2024

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