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Module

PHY2029 : Introduction to Observational Astronomy

  • Offered for Year: 2020/21
  • Module Leader(s): Dr Daniel Naylor
  • Owning School: Mathematics, Statistics and Physics
  • Teaching Location: Newcastle City Campus
Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

To introduce the students to the programming language Python 3 along with the packages NumPy, SciPy and matplotlib, such that students will be able to perform data analysis and solve some challenging mathematical/physical problems computationally.

Outline Of Syllabus

Students will gain practical skills in using Python that can be used to solve complex mathematical problems found in many areas of Physics, as well as developing scripting skills to simplify data analysis.

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture14:304:30Present in Person (1 x 30 minutes lecture plus 4 x 1hr lectures)
Scheduled Learning And Teaching ActivitiesLecture14:304:30Synchronous On Line Material
Structured Guided LearningLecture materials181:0018:00Non Synchronous Materials
Guided Independent StudyAssessment preparation and completion151:0015:00N/A
Structured Guided LearningStructured non-synchronous discussion91:009:00N/A
Scheduled Learning And Teaching ActivitiesDrop-in/surgery21:002:00Office hours/discussion board activity
Guided Independent StudyIndependent study471:0047:00N/A
Total100:00
Teaching Rationale And Relationship

Non-synchronous online materials are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on assessed work. Present-in-person and synchronous online sessions are used to help develop the students’ abilities at applying the theory to solving problems and to identify and resolve specific queries raised by students, and to allow students to receive individual feedback on marked work. Students who cannot attend a present-in-person session will be provided with an alternative activity allowing them to access the learning outcomes of that session. In addition, office hours/discussion board activity will provide an opportunity for more direct contact between individual students and the lecturer: a typical student might spend a total of one or two hours over the course of the module, either individually or as part of a group.

Alternatives will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.
Student’s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Written exercise2M10Simple Python 3 exercises, plotting and curve fitting
Written exercise2M45Problem solving using Python 3
Written exercise2M45Problem solving using Python 3
Assessment Rationale And Relationship

The first assignment is designed to test the understanding of the students of the basics of Python 3 and ability to put together a simple script to plot and analyse data. The final two assignments are designed to test the students’ understanding of the presented numerical techniques and their limitations in order to solve complex mathematical problems.

Reading Lists

Timetable