Postgraduate Course: Data Science for Decision Makers (MSc) (PGEE11201)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces key concepts and ideas from data science and data management and looks at how these relate to decision making processes in organisations. It is aimed at those with a less technical background, but with experience in project management. The course is designed to be incorporated into the Leading Major Programme and will be delivered in part by partners from EPCC (University of Edinburgh) and from industry. |
| Course description |
The course will cover:
- Definitions of 'Data Science', 'Big Data' and related terms
- Questions that data can address
- Data lifecycles & data management planning
- Data analysis processes
- Where data is stored (e.g. files, relational databases, NoSQL databases)
- How data is stored (e.g. file formats)
- Types of data science tools, libraries (incl. workbooks)
- Basic machine learning: supervised vs unsupervised learning
- Processing data: cluster vs cloud vs high performance computing
- Ethical & legal considerations for data
- The role of data in a digital strategy
- How data science fits with existing processes and infrastructure
- Data science organisational roles
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2022/23, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 16,
Seminar/Tutorial Hours 3,
Online Activities 4,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 25,
Other Study Hours 48,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
0 )
|
| Additional Information (Learning and Teaching) |
Other study hours. Course delivered online.
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam %: 0%
Practical Exam %: 0%
Coursework %: 100%
|
| Feedback |
Feedback will be provided in three ways:
- On the online forum before and after the online face to face block.
- The course will be delivered in short block mode with lectures intertwined with tutorials/workshops. This will allow for a two-way dialogue between students and course tutors to allow judgement on course material understanding.
- Formal written feedback will be provided after the submission of the coursework exercise
|
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Explain important concepts relating to data science and its relationship to decision making;
- Identify and specify relevant roles for a data science team, understanding what could be expected in each role;
- Identify potential data science approaches to business problems and recognise factors which might affect their suitability;
- Communicate with a data science team and discuss the relationship between business questions and relevant data science techniques;
- Incorporate data-driven decision-making processes into a wider digital strategy.
|
Reading List
- C. O'Neil and R. Schutt, Doing Data Science: Straight Talk from the Frontline, O'Reilly Media (2013), ISBN: 978-1449358655
- A number of videos (from related UoE courses) will also be provided to the students in advance of the face-to-face teaching to provide background material
|
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Additional Class Delivery Information |
Online. |
| Keywords | Data Science,Data Management,Data Strategy |
Contacts
| Course organiser | Dr Frederic Bosche
Tel: (0131 6)51 3525
Email: |
Course secretary | Miss Margaret Robertson
Tel: (0131 6)50 5565
Email: |
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