| Unit name | Data Science Methods and Practice |
|---|---|
| Unit code | SEMTM0045 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Abdallah |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
Being a capable data scientist involves much more than understanding key techniques for interrogating and analysing data. Both in academic research and in industry, a data scientist needs to be able to find and critically analyse relevant sources of information, prepare written and verbal reports for a range of different audiences, manage themselves (and potentially others) effectively, create well-documented code while following excellent version control practices, and ensure that data is used ethically and sustainably with due regard to access control and security of sensitive data.
In this unit, you will develop and enhance your research skills and practical data science skills through a range of activities covering literature reviews, academic and business writing, team management, presentation skills, and other essential aspects of the methods and practice of being a data scientist. You will complete this unit with a practical understanding of best practice in data science and in industrial and academic research more broadly, and you will be well prepared for your final project, whether you pursue the industry-based Group Project or the more academic Individual Project. You will develop your ability to express yourself professionally as a data science researcher and you will learn how to craft a proposal for practical data science research.
How does this unit fit into your programme of study
The unit gives you training in the key skills that you will need to undertake effective research for the final projects in your programme. These skills will enable you to apply material learned elsewhere in your degree to any major project in data science. As a result, this unit will prepare you not only for your final project but also for further work in academic or industrial data science research.
An overview of content
The content of this unit will be delivered in two different ways. Some sessions will be interactive teaching sessions that will explore key aspects of data science methods and practice, which may include:
Other sessions will involve presentations and question-and-answer sessions on cutting-edge research topics. You will be assessed on a portfolio of work in which you apply the skills covered in the interactive teaching sessions to an application of one of the research topics covered in the research presentation sessions.
How will students, personally, be different as a result of the unit
As a result of this unit, you will become confident in the skills required to design and manage a data science research project and to articulate your plans to a range of different stakeholders. You will better appreciate the role of ethics, sustainability, and security in data science and you will be better able to obtain and evaluate essential information from a range of sources
Learning Outcomes
On successful completion of this unit, you will be able to:
The main form of teaching for this unit will be through “lectorial” sessions that students attend in person. During these sessions, some content will be delivered in a lecture format but a substantial part of the session will be devoted to interactive activities in which you will consolidate and explore the material covered in the lecture portions. Teaching may also be delivered through lectures, pre-recorded presentations, facilitated research sessions, question-and-answer sessions, assigned reading, and workshops.
The School of Engineering Mathematics and Technology will run Community and Integrity Training at the beginning of the academic year. Attendance at a Community and Integrity training session is a must-do component to be awarded credit for the unit.
Tasks which help you learn and prepare you for summative tasks (formative):
Formative feedback on your work will be embedded into the regular “lectorial” workshop sessions. This may include guided peer feedback sessions and opportunities to discuss your work on exercises with teaching staff. You may also receive feedback on draft versions of some elements in your portfolio.
Tasks which count towards your unit mark (summative):
This unit will be assessed by a single portfolio assessment that is worth 100% of the unit mark and that assesses all Learning Outcomes. Through your portfolio, you will demonstrate your ability to apply the methods taught in this unit to proposing and planning a substantial research project that relates to one of the research topics covered in the research presentation sessions. You will complete your portfolio individually.
The portfolio may contain elements such as a research proposal (including a literature review), a business case, a project plan, and/or a short video recording that summarises the project and plan.
When assessment does not go to plan:
Reassessment takes the same form as the original summative assessment
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMTM0045).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
