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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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DRPS : Course Catalogue : School of Geosciences : Postgrad Research Courses (School of GeoSciences)

Postgraduate Course: Numeracy Modelling and Data Management (PRGE11017)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThe course provides and overview of numerical methods, data management and computations useful in the GeoSciences. Topics include version control, programming using Python, visualisation with Matplotlib, spatial data handling using QGIS and statistics using R.
Course description The course takes 1st year PhD students through a number of skills that will be useful as they proceed through their PhD. They will learn to manage their data through version control, which allows user to track any changes to their data and easily discover to previous versions of data, programs and text. In addition, students will learn the basic elements of the programming language Python, which is popular in many scientific communities. Pythons visualisation library, MatPlotlib, will also be introduced. Students will learn about data assimilation through both python and the statistical package R, and will refresh their knowledge of basic statistics using this software package. Students will also learn how to visualise data with R. Students will be introduced to the open source GIS QGIS and will learn the basics of handling spatial data. Finally, students will learn the basics of constructing a numerical model. All tools used in the course are non-commercial and each course session will involve a brief overview lecture followed by group and individual work while staff members are present to answer questions.

Session 1: Basics of the Shell
Session 2: Repositories and Git
Session 3: Basic python
Session 4: Python and matplotlib (data visualisation)
Session 5: Statistics in python and plotting
Session 6: Statistics using R and plotting in R
Session 7: Introduction to numerical methods
Session 8: Spatial data using QGIS
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed:
Co-requisites
Prohibited Combinations Other requirements This course supports first year PhD students on the E3 Doctoral Training Programme and is open to other registered PhD students in the School of GeoSciences. For GeoSciences PhD students not on the E3 programme please contact the Course Organiser to be enrolled.
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  None
Course Start Semester 1
Course Start Date 17/09/2018
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Supervised Practical/Workshop/Studio Hours 16, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 82 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) This non credit bearing course does not have an assessment component.
Feedback Students attend practical/workshop sessions at during which feedback is provided by staff.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Use version control software to track their data, programs and text. Students will learn how to create repositories, commit changes, collaborate with others and push their repositories to online repository hubs
  2. Use the python programming language to make simple numerical models, ingest data in various formats, perform statistics and visualise data.
  3. Use the R programming language to assimilate data, perform statistical analyses and visualise the data.
  4. Assimilate and display spatial data using QGIS.
Reading List
None
Additional Information
Graduate Attributes and Skills Students will gain personal intellectual autonomy by learning how to write their own scripts for data analysis. They will learn communication skills by becoming proficient at popular scientific visualisation software (Matplotlib and QGIS). These skills are also highly sought after in the research community and will lead to research independence of the students.
KeywordsNumeracy,Modelling,Data Management,Programming,Statistics
Contacts
Course organiserDr Richard Essery
Tel:
Email: Richard.Essery@ed.ac.uk
Course secretaryMrs Katy Cameron
Tel: (0131 6)50 5854
Email: Katy.Cameron@ed.ac.uk
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