Module GEOL1151: Introductory Data Science for Geoscientists

Department: Earth Sciences

GEOL1151: Introductory Data Science for Geoscientists

Prerequisites

• None.

Corequisites

• None.

Excluded Combination 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

• Explain the fundamentals of geospatial data, remote sensing, and data analysis methods;
• Explain how computational skills can be beneficial in an Earth Sciences context.

• 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.

• 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

Summative Assessment

Formative Assessment:

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

■ 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