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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23

Department: Earth Sciences

GEOL50215: Data Science Applications in Earth Sciences

Type Tied Level 5 Credits 15 Availability Available in 2021/22
Tied to G5P123 Data Science (Earth and Environment)


  • None


  • None

Excluded Combination of Modules

  • None


  • To provide students with an understanding of the applications of data science in the Earth and Environmental Sciences
  • To provide students with experience of handling, amalgamating and analysing diverse Earth and Environmental datasets from a range of sources and across a range of spatial and temporal scales
  • To provide students with experience of using datasets to address problems at the forefront of Earth and Environmental Sciences, across a range of topics
  • To provide knowledge of, and the ability to apply, popular software packages currently used in industry settings.


  • The content will be based around topics including but not restricted to:
  • Geophysics and inverse theory application
  • Active remote sensing (LIDAR and radar)
  • Passive (multispectral) remote sensing
  • Environmental time series (e.g. river flows and water quality)
  • Data camp using field, drone and satellite observation

Learning Outcomes

Subject-specific Knowledge:
  • By the end of this module, students should:
  • Understand the systems for collecting, handling and plotting spatial data
  • Understand how to apply physical models to understand environmental systems.
  • Understand the spectrum of remote sensing techniques and Earth observation products
  • Understand the use of archived data
  • Appreciate the main software packages for collation and analysis of environmental data.
Subject-specific Skills:
  • By the end of this module, students should: • Be able to download and manipulate Earth Observation products • Be able to process data coming from a range of archived sources • Be able to collate and use data from a range of sources and across a range of spatial scales • Be able to use standard software packages to develop models and solve problems
Key Skills:
  • Effective written communication
  • Planning, organising and time-management
  • Problem solving and analysis

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Learning outputs are met through classroom-based workshops, supported by online resources. The workshops consist of a combination of taught input, case studies, discussion and computing labs. Online resources will typically consist of directed reading and a programming environment with example code.
  • The summative assessment will be based upon: an individual written report on the analysis of a given data set with options supplied from each of the topics covered. An individual report of data collected as part of a group exercise within the data camp

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures 8 2 times per week (Term 2, weeks 16-19) 1 hour 8
Workshops 8 1 times per week (Term 2, weeks 16 - 19) 2 hours 16
Surgery 4 1 times per week (Term 1, weeks 16-19) 1 hour 4
Data Camp 1 3 days of 7 hours per day 21 hours 21
Independent Learning 101
Total 150

Summative Assessment

Component: Assignment Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Individual written assignment based on data problem 2000 words maximum 100%

Formative Assessment:

The formative assessment consists of classroom-based exercises on specific data topics of relevant to the learning outcomes of the modules. Oral feedback will be given on a group and/or individual basis as appropriate.

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University