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DRPS : Course Catalogue : School of Geosciences : Earth Science

Undergraduate Course: Geophysical Data Science (EASC08025)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryIn Geophysical Data Science you will learn the basics of scientific computing using Python. The course balances the development of fundamental computing skills and the application of these for data presentation and analysis. You will work with a range of solid earth and climate + met datasets in order to practice these data analysis skills. These skills will prepare you for data analysis in project work, set you up to understand computational modelling and give you transferable coding skills in a computing language that is highly sought after in industry.
Course description Syllabus
Week 1: Introduction to Python for Data Science and Scientific Computing
Week 2: Intermediate Python for Data Science
Week 3: Data Visualisation with Python
Week 4: Data Ingestion and Cleaning
Week 5: Geospatial Data Analysis
Week 6: Statistical and Trend Analysis
Week 7: Understanding Data using Probability
Week 8: Geophsical Time Series Data
Week 9: Meteorological and Climate Data
Week 10: Assessment - Climate Data Analysis
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2020/21, Not available to visiting students (SS1) Quota:  27
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Assessment Details
Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
This course will be assessed solely with coursework and ongoing assessment.
The assessment focuses on providing you with opportunities to practice your coding skills in your own time.
There are three elements that are assessed:
- You are required to complete each of the DataCamp courses in your own time. Completion is tracked by the software.
- The first assessment will be analysis of a dataset in small groups and the hand in will be individual reports. This exercise will look for creative analysis of data to meet an objective. You will use the data analysis and coding skills you have been developing. The write up will be in the form of a Jupyter Notebook. Submission of your initial plan with verbal feedback will be in Week 4. Submission of your final report will be 12 noon Wednesday Week 8
- The final assessment with be individual analysis of a dataset in the week 10 class with the hand in being a report. The assessment with be a Jupyter notebook exercise started in class and completed in your own time. Submission 12 noon Wednesday Week 12

100% Coursework
- 5% for each of the 5 DataCamp courses (2 week turn-around)
- 40% for the group data analysis and project writeup (Wednesday Week 8 deadline)
- 35% for the Met and Climate assessment (Wednesday Week 12 deadline)

Assessment Deadlines
- Weekly DataCamp Courses (5) to be completed after class with 2 week turnaround time
- Group Data Analysis and Project: Initial Plan (formative) Week 4, Group Data Analysis and Project 12noon Wed week 8. There will a project working in small groups running through most of the semester . This will build on the skills learnt in class and you will work in groups to support each other learning coding skills, so you can critically discuss the limitations of data and the analysis. Your submissions will be written as a Jupyter notebook.
- Met and Climate Assessment 12noon wed week 12. The final assessment will be the analysis of a climate or meteorological dataset with individual write ups. Your submissions will be written as a Jupyter notebook. Week 12 deadline.
Feedback - Feedback from lecturers and demonstrators during the interactive computing sessions
- Ongoing feedback from the online python learning environment
- Class discussion on the geophysics graph gallery contributions
- Written feedback on the assessed coursework
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand the role of coding in geophysical data analysis and modelling
  2. Build a foundation for scientific computing using Python
  3. Build confidence in using Python to model geophysical data
  4. Be able to undertake exploratory data analysis using a range of geophysical data, including choosing how to present data and extract statistical properties
  5. Learn how to use the internet to find appropriate pieces of code to hack to solve a new problem
Reading List
Edina's Noetable server:
Additional Information
Graduate Attributes and Skills - Data literacy: Scientific computing and data analysis
- Computer literacy: competency in Python
- Working in small groups
Additional Class Delivery Information One weekly 3 hour combined lecture and practical class.
KeywordsGeophysics,data science,python,coding
Course organiserDr Mark Naylor
Tel: (0131 6)50 4918
Course secretaryMs Katerina Sykioti
Tel: (0131 6)50 5430
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