2018/19 Taught Postgraduate Module Catalogue

GEOG5991M Programming for Geographical Information Analysis (WUN): Core Skills

15 creditsClass Size: 30

Module manager: Dr Andrew Evans
Email: A.J.Evans@leeds.ac.uk

Taught: 1 Apr to 31 Jul, 1 Apr to 31 Jul (Adv pre 2223), 1 Jan to 30 Apr, 1 Jul to 31 Oct, 1 Oct to 31 Jan (Adv pre 2223) View Timetable

Year running 2018/19

This module is mutually exclusive with

GEOG5990M	Programming for Geographical Information Analysis: Core Skil
GEOG5995M	Programming for Social Science: Core Skills

This module is not approved as an Elective

Module summary

This module gives a complete basic training in computer programming using the Python language. Python is a key programming language for data analysis. On completion of this module, you should have the following skills and knowledge: -Foundation level computer programming for Geographical Information analysts. -Know how to build computer analysis and modelling tools for geographical data. -An understanding of how important elements of computers, such as the Web and file storage work. -Insight into industrial programming practice and frameworks. The vast majority of Geographical Information specialists are good at using `out of the box' functionality of software, but may be less good at enhancing this functionality or developing software to do specific tasks that cannot easily be done with existing software. This module will take you to the next level - the level at which you are no longer just a software user, but a software developer capable of developing bespoke tools for geographical analyses Python is often the language of choice for both environmental and business consultancies and it is widely used in research. The module is suitable for those with no experience of programming, or those with experience of other programming languages. The module is an excellent foundation for dissertation projects or work placement modules, and provides training suitable for the module GEOG5871M: Web-Based GIS (WUN).

Objectives

On completion of this module, students should:
Be able to develop Python programs for geographical data processing.
Be aware of the history of computer programming and the importance of software licensing and source code documentation.
Have developed an understanding of industry standards in software engineering.

Learning outcomes
Foundation level computer programming for geographical data processing.
Awareness of the breadth and depth and development of the Python language.
An understanding of how important elements of computers, such as the Web and file storage work.
Insight into industrial programming practice and frameworks.

Skills outcomes
Abstraction and synthesis of information from a variety of sources.
Solving problems and making reasoned decisions.
Plan, design, execute and report research.
Undertake effective analysis work.
Employ a variety of technical methods for the analysis and presentation of spatial and environmental information.
Apply numerical and computational skills to data.
Use information technology effectively.
Industry knowledge.

Syllabus

-Python architecture / history
-The core language: variables, arrays, flow controls
-Functions
-Classes/Objects
-Documentation
-Input/output
-Useful libraries

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	8	2.00	16.00
Practical	8	2.00	16.00
Tutorial	1	2.00	2.00
Private study hours			116.00
Total Contact hours			34.00
Total hours (100hr per 10 credits)			150.00

Private study

Students will be given structured practical work and will be expected to work on these projects independently, with academic support
delivered by online forums, email, and, if necessary, over the phone. The practical projects, which comprise the minor assessment in the form of a portfolio, will build the foundational knowledge for the major assessment: a final stand-alone computer model/analysis tool of a geographical system. The students will be supported through this work with VLE materials, including online learning materials, FAQs, and materials on ancillary and supporting topics.

Opportunities for Formative Feedback

Student progress will be monitored through practical tasks. The major assessment will be individual to each student; students will have the option of designing their own project (within some key limitations - for example, that the software must load and write data, and involve data analysis). Students will have access to assessment-orientated materials, including walk-through discussions of structuring solutions and the coding that might be involved.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	2,500 word equivalent	70.00
Portfolio	1,500 word equivalentPortfolio of practical work	30.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 02/07/2018

Disclaimer

Browse Other Catalogues

• Undergraduate module catalogue
• Taught Postgraduate module catalogue
• Undergraduate programme catalogue
• Taught Postgraduate programme catalogue

Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD