NCHNAP565 Data Synthesis Course Descriptor

Course Summary

This course is an intensive two-week, face-to-face bootcamp, which covers how data is brought together from different sources (text, audio, images etc) to enrich data science analysis. The course focuses on two aspects: parsing images using Computer Vision and parsing language, using Natural Language Processing (NLP). Learners will gain hands-on experience in using Computer Vision and NLP and will explore how to extract information from a variety of formats to inform data analysis. 

Course Aims

• Train learners in how to use data warehousing for effective data science solutions.
• Train learners in how to synthesis data using a wide variety of sources and formats, including audio, text and images.
• Introduce learners to computer vision and natural language processing.
• To learn from other data scientists and become part of a wider data science community.

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 a face-to-face bootcamp, of two weeks duration, taught once every year.

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• Lectures
• Informal discussion groups
• Practical sessions
• 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 (12 days x 7 hours) = 84 hours
• On-the-job learning (10 days x 7 hours) = 70 hours

Apprentices will complete workplace activities before and after the bootcamp, as part of

their on-the-job learning. Preliminary activities include reading and workplace research and

post-bootcamp activities will include completion of the assignments (see below).

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); oral  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

• 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
• Farzindar, A., (2020), Natural language processing for social media, San Rafael, California: Morgan & Claypool

Journals

Learners are encouraged to consult relevant journals on data synthesis, Computer Vision and/or NLP. 

Electronic Resources

Learners are encouraged to consult relevant electronic resources on data synthesis, Computer Vision and/or NLP.

Indicative Topic

• Computer Vision for data science
• NLP for data science
• Combining diverse data and formats