NCHNAL6124 Implementing Data Science Course Descriptor

Course Summary

This course offers students an opportunity to learn how to approach data analysis problems in a systematic manner and to learn how to design data analysis pipelines, as well as how to implement them at scale in the context of real-world problems. Data science is at the intersection of statistics, machine learning, and software development. Data analysis problems are solved in a series of datacentric steps: data acquisition, data cleaning, data transformation, data modelling, and data visualization.

Course Aims

• To consolidate student’s knowledge and apply data science thinking, processes and tools to a range of real-world problems. 
• To expose students to real-world data sets and organisation constraints.
• To allow students to evaluate new real-world tasks and develop solutions. 

Learning Outcomes

On successful completion of the course, students 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).

Students are required to attend and participate in all the formal and timetabled sessions for this course. Students 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:

• Learning and teaching (6 days x 8 hours) = 48 hours 
• Independent study = 102 hours 

Indicative total learning hours for this course: 150 hours 

Assignments (see below) will be completed as part of private study.

Assessment

Formative

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

Summative

The summative assessment will be assessed in accordance with the assessment aims set out in the programme specification.

Feedback

Students will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Students 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 student’s progress.

Feedback is provided on summative written assignments which will be handed back to the students.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Syllabus or other documentation provided to students; 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
• Said, A., and Torra, V., (2019), Data Science in Practice, Cham: Springer International Publishing: Imprint: Springer

Journals

Students are encouraged to consult relevant journals on data science. 

Electronic Resources

Students are encouraged to consult relevant electronic resources on data science.

Indicative Topics

• Data science workflow
• Data science in the real-world and organisational objectives
• Real-world data