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GEOL1151: Introductory Data Science for Geoscientists

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Type	Open
Level	1
Credits	20
Availability	Available in 2024/2025
Module Cap	85
Location	Durham
Department	Earth Sciences

Prerequisites

None.

Corequisites

None.

Excluded Combinations of Modules

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.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total	Monitored
Practicals	40	2 per week	2 hours	80	Yes
Preparation, online learning activities, reading				120
Total				200

Summative Assessment

Component: Continuous assessment		Component Weighting: 100%
Element	Length / Duration	Element Weighting	Resit Opportunity
Take-home assignment covering GIS component (set at end of GIS section)	2 weeks allowed	25
In-class test on Python fundamentals (set at ~mid-point of Python section)	2 hours	25
Take-home assignment covering full course		50

Formative Assessment

Ongoing opportunities for feedback including end-of-exercise skills tests.

More information

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