LDSCI5207 Experimental Data Science Project Course Descriptor

Course Overview

This course is a research project, possibly interdisciplinary, in data science. Via directed study, students will be able to apply standard taught material on computer or data science (mainly, data-driven software development methods, tools, and techniques) by managing a software project that solves a substantial, real-world problem.

Learning Outcomes

On successful completion of the course, students will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Employability Skills

Teaching and Learning

This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage students in their studies.

The scheduled teaching and learning activities for this course are:

Directed study. 4-12 scheduled hours, the exact number varying according to the balance of 1:1s, 2:1s, or small groups. The plan will be confirmed by the start of the course, taking into account student numbers and the proposed topics, readings, and specific tasks.

Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.

Students are to attend and participate in all the scheduled teaching and learning activities for this course and to manage their directed learning and independent study.

Indicative total learning hours for this course: 150

Assessment

Both formative and summative assessment are used as part of this course, with purely formative opportunities typically embedded within interactive teaching sessions, office hours, and/or the VLE.

Summative Assessments

Further information about the assessments can be found in the Course Syllabus.

Feedback

Students will receive formative and summative feedback in a variety of ways, written (e.g. marked up on assignments, through email or the VLE) or oral (e.g. as part of interactive teaching sessions or in office hours).

Indicative Reading

Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.

• William Strunk Jr. and E. B. White. 1999. The Elements of Style. Pearson.
• Perdita Stevens. How to Write Good Programs: A Guide for Students. 2020. Cambridge University Press.

Indicative Topics

Note: Comprehensive and current topics for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative topics provided below are used as a general guide and part of the approval/modification process only.

• Problem statement definition
• Software design and implementation
• Debugging and testing of software components
• Documentation
• Presentation and demonstration