Foundations of Data Science Course Descriptor

Course Overview

Data science is about extracting generalisable, useful and meaningful knowledge from data in a systematic manner. The course covers the emerging field of data science at breadth, honing on both programming and data analytics skills.

Students will learn to work with tensors (i.e., multi-dimensional arrays) and apply linear algebra transformations; load, integrate and process structured and unstructured data from multiple sources; apply statistical and machine learning analysis algorithms; and visualise results.

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:

Lectures/labs. 40 scheduled hours – typically including induction, consolidation or revision, and assessment activity hours:

• Version 1:All sessions in the same sized group, or
• Version 2: most of the sessions in larger groups; some of the sessions in smaller groups

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.

• Jake VanderPlas. 2016. Python Data Science Handbook. O’Reilly Media.
• Joel Grus. 2019. Data Science from Scratch, 2nd Edition. O’Reilly Media.

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.

• Arrays, tensors, and tensor computations
• Relational data transformations (e.g., data aggregation)
• Data summarisation and descriptive statistics
• Regression and classification