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Module

CSC8321 : Computing for Digital Biology

  • Offered for Year: 2021/22
  • Module Leader(s): Dr Harold Fellermann
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

To identify the appropriate manner with which biological data and services may be provided by a variety of different enabling technologies and presented in a cohesive manner to users. The module provides an elementary introduction to some important topics of computer science that are relevant for biological data science and software development. These topics include Computer Architecture, Data Representation and Storage, and Systems Administration.

Outline Of Syllabus

Introduction to Computer Architecture,
Networks and Distributed Computing,
CPU/Memory/HD/Network bound computation,
Data Modelling (including Object-based data representation),
Data Storage (including relational databases, and SQL, and other databases such as RDF),
Data Transfer, Web Services, and DATA sharing (XML, JSon, REST),
Data Standards,
Workflows,
System Administration.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture41:004:00Synchronous present-in-person sessions, if applicable. Else additional online sessions.
Structured Guided LearningLecture materials81:3012:00Asynchronous online materials
Guided Independent StudyAssessment preparation and completion46:0024:00Lecture follow-up, including time for practical exercises
Guided Independent StudyAssessment preparation and completion128:0028:00Assessment preparation and completion
Scheduled Learning And Teaching ActivitiesDrop-in/surgery42:008:00Synchronous present-in-person sessions, if available. Else additional synchronous online sessions
Guided Independent StudyIndependent study46:0024:00Background reading
Total100:00
Teaching Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to prepare for lectures using asynchronous online material and to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

Students are expected to spend time on coursework outside timetabled practical classes.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Written exercise1M50A written report demonstrating understanding of the theory and computational technologies behind biological data organization. 1000
Prob solv exercises1M50A programming exercise demonstrating ability to operate over biological data. Equivalent to 1000 words.
Assessment Rationale And Relationship

Assessment consists of two complementary exercises. A written report assesses the student’s understanding of the theory of computational technologies for digital biology, as delivered in the lecture material of the module. A programming exercise assesses the students ability to apply learning content in a practical environment. The programming exercise is supported by guided study material. Scheduled dropin/surgery sessions support students in working towards these exercises.

Reading Lists

Timetable