Postgraduate Course: Introduction to statistics in health and social care (HEIN11039)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course provides an introduction to statistics on a theoretical and practical level. It will include in-depth coverage of topics relevant to data science, leaving students well prepared for further study as practitioners in the health, social and care services sector. |
| Course description |
Statistics is collection, analysis, interpretation, and presentation of data. Statistics is an essential tool for data scientists in the health and social care sector. This is an introductory statistics course for data scientists working in health and social care. The course employs an applied approach by analysing real-life data from health, social and care services using the statistical software R.
No prior experience in statistics or R programming is required. However, familiarity with basic mathematical concepts is assumed. These include the mean, median, variance, exponents, logarithms, and summations.
The course will cover the core basics of statistics, populations and samples. Next, data measurement and presentation, descriptive statistics, probability distributions and statistical inference will be introduced. The course will then focus on analysis of variance (ANOVA), correlation and regression. The material is presented to understand rather than memorise statistical concepts.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: None |
| Course Start |
Flexible |
| Course Start Date |
08/08/2022 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Formative feedback will be provided throughout the course through discussion board postings.
Assessment summary:
LO1, LO2, LO3: Weekly online questions (20%)
LO1, LO2, LO3 and LO4: Discussion post (10%)
LO1, LO2, LO3 and LO4: Individual report (70%)
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| Feedback |
Formative feedback will be provided throughout the course, for example, during live question and answer sessions, quizzes, and discussion boards. A formative task will also be offered before the student submitting their summative assessed course work. All assignments will be marked, and feedback provided.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of the fundamental concepts of statistics.
- Apply basic statistical techniques using the statistical software R to solve simple problems from health, social and care settings.
- Analyse and interpret data from health and social care, using appropriate statistical tests
- Demonstrate the ability to effectively communicate basic statistical techniques with peers and a wide range of audiences within the health and social care sector.
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Reading List
Recommended, but not essential:
Julie Vu and David Harrington (2020). Introduction to Statistics for the Life and Biomedical Sciences. OpenIntro.
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Additional Information
| Graduate Attributes and Skills |
1) Mindsets:
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in probability and statistics. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to resolve statistical issues as they arise in health and social care systems.
Aspiration and personal development
Students will be encouraged to draw on the quality, depth and breadth of their experiences to expand their potential and identify areas in which they wish to develop and grow. Students will also be encouraged to understand their responsibility within, and contribute positively, ethically and respectfully to the health and social care community, while acknowledging that different students and community members will have other priorities and goals.
Outlook and engagement
Students will be expected to take responsibility for their learning. Students will be asked to use on their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on the experience of their peers and identify opportunities to enhance their learning.
2) Skills:
Research and enquiry
Students will use self-reflection to seek out learning opportunities. Students will also use the newly acquired knowledge and critical assessment to identify and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate learning materials and exercises. Students will be supported through their active participation in self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and superiors from an open-minded and reasoned perspective.
Personal effectiveness
Students will need to be effective and proactive learners that can articulate what they have learned, and have an awareness of their strengths and limitations, and a commitment to learning and reflection to complete this course successfully.
Communication
Effective data scientists' practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the interactive (problem-based learning examples, discussion boards and collaborative activities) and assessment elements incorporate constant reinforcement and development of these skills. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Kasia Banas
Tel:
Email: |
Course secretary | Miss Magdalena Mazurczak
Tel:
Email: |
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