NCHNAP452 Data Science Bootcamp Course Descriptor

Course Summary

This course is an intensive two-week, face-to-face bootcamp and gives learners hands-on experience of a mini data science project. Learners will be required to complete preliminary reading and preparation before the two-week bootcamp and complete their assignments after the bootcamp. Learners will explore the data science workflow with practical and collaborative tasks. A wide variety of datasets will be used to mirror real-world settings. Learners will engage in hands-on programming (Python), database interrogation (SQL) and exploratory data analytics and visualisation. Group discussion will include the context of data science, the data science community and the data protection and regulatory environment.

Course Aims

• To consolidate learner’s knowledge of the key aspects of data science with practical experience.
• For learners to explore the data science workflow and understand the processes and tools at each stage.
• To meet with other data scientists, learn from each other 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 correspondence); oral and indirectly through discussion during group tutorials. Learners will also attend a formal meeting with their Academic Mentor and Employer. These 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

Journals

Learners are encouraged to read material from relevant journals on Data Science as directed by the course trainer.

Electronic Resources

Learners are encouraged to consult websites on Data Science.

Indicative Topics

• A typical data science workflow
• Working effectively to complete a focussed data science project
• The context of data science in the real-world