This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2025-2026 (archived)
Module GEOL1151: Introductory Data Science for Geoscientists
Department: Earth Sciences
GEOL1151: Introductory Data Science for Geoscientists
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 1 | 20 | Available in 2025/2026 | None. | Durham |
Pre-requisites
- None.
Corequisites
- None.
Excluded Combinations
- None.
Aims
- To introduce fundamental concepts of data acquisition and analysis in a geoscientific context;
- To familiarise students with computational tools for manipulating and visualising a range of scientific and geospatial data;
- To introduce students to the core concepts of computer programming;
- To introduce the Python programming language;
- To encourage resilient and self-reliant problem-solving.
Content
- Introduction to the geospatial model, GIS software and remote sensing methods.
- Fundamental concepts of computer science: data types, algorithm design, functions;
- Control structures: conditional expressions, loops, iterators, exception handling;
- Modules/libraries including NumPy and Pandas;
- File input/output including reading/writing common file formats;
- Data visualisation and figure preparation using matplotlib & cartopy.
Learning Outcomes
Subject-specific Knowledge
- Explain the fundamentals of geospatial data, remote sensing, and data analysis methods;
- Explain how computational skills can be beneficial in an Earth Sciences context.
Subject-specific Skills
- Demonstrate a basic competence using geospatial software;
- Write, adapt and explain computer programs using the Python programming language;
- Plan, implement and execute computational data analysis tasks including:
- reading and writing data files in a variety of formats;
- organising and manipulating information;
- producing data visualisations including graphs and maps;
- implementing simple computational models for physical systems.
Key Skills
- Fundamental IT literacy;
- Analysis and presentation of diverse datasets;
- Enhanced practical numeracy skills.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- This module will consist of 5 weeks focussed on geospatial data and GIS skills and 15 weeks focussing on the Python programming language, computational data analysis, and data visualisation. There will be two 2-hour classes per week, providing a mix of instructor-led and self-paced teaching.
- Assessment comprises a take-home assignment covering GIS component (set at end of GIS section); and in-class test on Python fundamentals (set at ~mid-point of Python section); and a take-home assignment covering the full course content.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Practicals | 40 | 2 per week | 2 hours | 80 | Yes |
| Preparation and Reading | 120 | ||||
| Total | 200 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Continuous assessment | 100% | Assignment | 2 weeks allowed | 25% | |
| General Test | 25% | ||||
| Assignment | 50% |
Formative Assessment
Ongoing opportunities for feedback including an optional formative test in Term 1.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.