NCHNAP694 Big Data Course Descriptor

Course Summary

This course explores the challenges associated with interrogating, storing and analysing big (typically unstructured) data. Learners will conceptually explore how to build large-scale information storage structures using distributed storage facilities and data warehouses. The challenges of data quality assurance, storage reliability, and working with very large data volumes will be examined in detail. Learners will study how to model big data and use analytic services provided by host institutions. This course uses NoSQL to interrogate unstructured data sets and tools such as Candela and Chart Studio to visualise analytics for big data.

Course Aims

• Train learners in how to store, interrogate, model and visualise big (unstructured) data. 
• For learners to understand the challenges and opportunities of working with big (unstructured) data.
• Train learners with the skills required for modern data science in the real-world. 

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

• Buyya, R., Calheiros, R.N. and Dastjerdi, A.V., (2016), Big Data Principles and Paradigm, Morgan Kaufmann (imprint of Elsevier)
• Inmon, W.H., (2011), Building the unstructured data warehouse : architecture, analysis, and design, Technics

Journals

Learners are encouraged to consult relevant journals on big/unstructured data. 

Electronic Resources

Learners are encouraged to consult relevant electronic resources on big/unstructured data.

Indicative Topics

• Big data, focussing on unstructured data
• Big data storage, data warehousing and NoSQL
• Online analytical services and software for visualisation