MSc Data Science (Earth and Environmental Analytics) / Course details

This course unit is designed to create a real world environment for students to apply the knowledge and skills acquired in all the course units of the MSc Data Science in a real project developed in partnership with an industry partner. Students will be organised into groups of  members and the group will act as a consultant team and deliver to the client a project along the lines set out in the associated project brief. To help guide you 2 through this process, students will be provided with support by the project mentors both at the University and at the industry partner. Students will have a series of surgery sessions to receive support from lecturers on their work. The course assessment is designed to replicate the different stages of a data management project. The structure and submission times for the assignments are designed also to allow formative support to be provided to the groups as the work develops.  

• develop an understanding of the work in data analytics in practice
• introduce students to the work in data analytics in the professional environment
• promote student engagement with external industry partners
• develop a set of competences in project management, communication and team work

The course unit will: 

• develop an understanding of the work in data analytics in practice;
• introduce students to the work in data analytics in the professional environment;
• promote student engagement with external industry partners;
• develop a set of competences in project management, communication and team work

The course is delivered in the normal face-to-face format, but meetings with industry partners may take place online.  

The course unit is based on group work. It will focus on the work with the industry partners in the group project. Two workshops will be delivered professional experts invited to lecture on specific topics. Assessment will be based on the development of a practical project in collaboration with an external industry partners. The groups will mainly work in collaboration with the industry partners. A series of supervision sessions will be available to provide formative feedback and help the groups to develop the project and deliver the final report.  

Groups will be formed and allocated to predefined projects with briefs designed in collaboration with the partners. The students will be organised into cross-pathway,  multidisciplinary  groups of 5 students that include members of the different pathways. The subjects of the projects are not linked to any specific MSc pathway,  being its focus the development of the data management project regardless of any possible thematic link to a specific pathway. Peer evaluation is available to monitor  group work. Each group will be allocated to an industry partner which will help  define a concrete data project that will respond to a brief designed by lecturers and  the partners. 

• Describe the critical issues of working with an organisation solving a problem involving data analytics;
• Describe and apply methods and processes of project management when data is a key project component;
• Develop methods of communication of data driven reports;
• Develop competences in multidisciplinary team work

• Manage the task of solving the problem and delivering solutions using scientific knowledge, initiative and skills;
• Report and develop presentation skills;
• Communicate with partners and create project specifications;

• Collaborate in team work and cross-disciplinary work;
• Negotiate, specify and define a problem;
• Design a method of solving a problem with the resources, facilities and time available.

Assessment is done in groups of 5 students. Each group is assigned to an external industry partner which designs a project brief. Each project/group has an industry mentor with the industry partner and one mentor among UoM MSc DS lecturers.

Changes in contact (lectures, surgeries, workshops with professionals) moments during the course:

1. Week 1 lecture: project launch, team work, project planning, management, only kick-off meetings between groups and industry mentors, UoM Mentors will participate too;.

2. Week 2, lecture: presentations, professional communication, report writing and assessment;

3. Week 3 Workshop with data science professional;

4. Week 4 project surgery with UoM Mentor;

5. Week 5 Workshop with data science professional;

6. Week 6 lecture: feedback on progress so far, how to resolve problems in projects, to extend ideas, to support the ongoing project development;

7. Weeks 8, 11 project surgery with UoM Mentor.

Presentations done in weeks 6 and 12 are substituted by video presentations submitted on blackboard

Formative assessment format: One 5-minute-long video presentation (1 minute per group member) per group.

Summative assignment format: 10-minute-long video presentation (2 minutes per group member) worth 30%, Formal group report worth 70% 

Informal feedback on your work will be available as the course progresses.

Formal feedback and marking on coursework will follow the standard university procedures.

Outside these times, meetings with the lecturers will happen on office hours or by appointment.

The UoM learning skills webpage My Learning Essentials has good information on research skills, time management, etc., and it is available at http://www.library.manchester.ac.uk/using-the-library/students/training-and-skills-support/my-learning-essentials/. http://www.humanities.manchester.ac.uk/studyskills/

Project management

• The AMA Handbook of Project Management. (2014). AMACOM.
• Hughes, R. (2013). Agile data warehousing project management business intelligence systems using Scrum and XP. Waltham, MA: Morgan Kaufmann.
• Shin, B. (2002). A case of data warehousing project management. Information & Management, 39(7), 581-592.

Presentational techniques

• Kirk, A. (2016). Data visualisation : A handbook for data driven design.
• Rahlf, T. (2017). Data visualisation with R : 100 examples.
• Lima, M. (2011). Visual complexity : Mapping patterns of information. New York: Princeton Architectural Press.
• Mark Zastrow. (2015). Data visualization: Science on the map. Nature, 519(7541), 119-20.

Team work

• Stewart, G., Manz, Charles C, & Sims, Henry P. (1999). Team work and group dynamics. New York ; Chichester: Wiley.