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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

GEOL50315: Data Science Tools in Earth Sciences

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

Prerequisites

  • None

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To provide students with an understanding of data methods and tools used in the Earth and Environmental Sciences, with a particular focus on those used for analysing spatial and temporal datasets
  • To provide experience of physical modelling of complex real-world systems
  • To provide knowledge of, and the ability to apply, popular software packages currently used in industry settings.

Content

  • Spatial information systems
  • Geostatistics
  • Geographical Information Systems software
  • Numerical analysis
  • Inverse theory
  • Time series analysis
  • General and generalised linear models

Learning Outcomes

Subject-specific Knowledge:
  • By the end of this module, students should:
  • Understand the systems for recording spatial data
  • Understand how to solve forward and inverse physical models
  • Develop statistical models of environmental data
  • Appreciate the main Python and R packages for analysis of Earth and Environmental data and understand how to use them.
Subject-specific Skills:
  • By the end of this module, students should:
  • Be able to convert data between coordinate systems
  • Be able to analyse time series data in both the time and frequency domains
  • Be able to construct predictive time series models
  • Be able to solve or invert physical models
  • Be able to develop general and generalised linear models of continuous and discrete data
  • 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 a series of data modelling exercises to demonstrate knowledge of techniques taught.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures 8 2 times per week (Term 1, weeks 6-9) 1 hour 8
Workshops 8 2 times per week (Term 1, weeks 6-9) 2 hours 16
Surgery 12 3 times per week (Term 1, weeks 6-9) 1 hour 12
Preparation and reading 114
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 relevance 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