NCHNAP488 Data Science, Data Visualisation and Communication Bootcamp Course Descriptor

Course Summary

This course is an intensive two-week, face-to-face bootcamp that gives learners hands-on experience of a mini data analysis project aligned to the learners workplace sector. Learners will explore the data science workflow with practical and collaborative tasks. A variety of real-life datasets will be used for analysis and visualisation. Learners will engage in hands-on programming (using languages such as R, MATLAB /or Python), database interrogation, exploratory data analytics and visualisation (using applications such as Tableau). The course explores methods of communication, the digital tools and techniques available to support data visualisation and the impact they have on professional practice.

Course Aims

• To introduce learners to data science and data visualisation techniques.
• For learners to undertake a mini data analysis project using a variety of datasets.
• For learners to work and communicate effectively in a group.

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: 

• Online learning
• Online discussion groups
• Online 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
• One-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 

• Kelleher, J. D. and Tierney, B., (2018), Data Science, Cambridge, Massachusetts : The MIT Press
• Van Emden, J. and Becker, L., (2016), Presentation Skills for Students, Basingstoke : Palgrave Macmillan
• Mueller, J., (2019), Python for data science, Hoboken, NJ : John Wiley & Sons
• Burstein, L. (2011). Matlab® in Bioscience and Biotechnology (1st edition). Woodhead Publishing

Journals

Learners are encouraged to read material from relevant journals on data science, data visualisation and communication as directed by their course leader.

Electronic Resources

Learners are encouraged to consult relevant websites on data science, data visualisation and communication.

Indicative Topics

• Data analysis
• Programming
• Data visualisation and communication