Advances in Data Science Course Descriptor

Course Summary

This course will explore cutting-edge advances in data science, such as artificial intelligence, deep learning, advanced computer vision and natural language processing and meta analysis. Learners will explore the topics through real-world case studies.

Course Aims

• To expose learners to the latest techniques and the thinking in data science. 
• Train learners in advanced techniques, such as artificial intelligence, meta analysis, advanced Computer Vision and NLP.
• To equip learners with the knowledge and tools to work effectively in cutting-edge organisations. 

Learning Outcomes

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

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year.

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• On-line learning
• On-line discussion groups
• On-line assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
• On-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
• Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Assessment will be in two forms:

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi- or tri-partite reviews will monitor and evaluate the learner’s progress.

Feedback is provided on summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.

Indicative Reading

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

Books 

• Callan, R., (2003), Artificial Intelligence, Basingstoke : Palgrave Macmillan
• Forsyth, D., (2012), Computer Vision: A Modern Approach, Boston, Mass. ; London : Pearson ; Harlow : Pearson Education
• Eisenstein, J., (2019), Introduction to Natural Language Processing, Cambridge, Massachusetts : The MIT Press
• Borenstein, M., (2009), Introduction to meta-analysis, Wiley

Journals

Learners are encouraged to consult relevant journals on artificial intelligence, computer vision, NLP, deep learning and meta analysis. 

Electronic Resources

Learners are encouraged to consult relevant electronic resources on artificial intelligence, computer vision, NLP, deep learning and meta analysis. 

Indicative Topics

• Artificial Intelligence and deep learning
• Computer Vision and NLP
• Meta Analysis