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DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Health Information

Postgraduate Course: Data Types and Structures in Python and R (HEIN11068)

Course Outline
SchoolDeanery of Molecular, Genetic and Population Health Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course provides an introduction to data types and structures in health, social and care service settings. This course is designed to equip students with the skills required to handle, analyse and create tools to manage different data types and structures. Concepts are illustrated with examples from health, social and care services.
The course makes minimal assumptions about students' previous data use, the management or programming experience. This course is a stepping stone into more advanced programming and software development courses.
Course description 1) Academic description
Health, social, and care service systems collect and process many data types that support shared decision making and optimal service delivery and provision.
A data type is the most basic classification of data. It is the data type through which the computer program gets to know the form or the type of information that will be used. Data is stored differently depending on its type. Numbers are stored as integers or real numbers, text as string or characters.
A data structure is a collection of data types. Data structures provide a means to manage large volumes of data for use in health, social and care service institutions. Within a data structure, data is better organised. Efficient data structures are vital for obtaining maintainable software design. Some examples of data structures are arrays, linked lists, graphs and binary trees.
Software applications designed for problem-solving purposes require data to be stored as particular data types within certain data structures so that data operations yield specific results. The more common programming languages define data types such as 'integers' (whole numbers, for example, 33), 'non-integers' (numbers with decimal points, for example, 0.33) and 'characters' (text). They also have standard libraries that implement the most common data structures.
Much data manipulation and analysis can be undertaken without an in-depth understanding of computer science. Functional code and basic programs can be written without expertise in computer science or software engineering. However, data scientists working in health, social and care services will ultimately need to move beyond writing simple code and using others' applications. Understanding how data is represented in computer systems and manipulated with programming languages will benefit people writing code that runs fast, scales to large amounts of data, and is portable to other platforms. This course will introduce students to foundational computer science concepts, the data life cycle, and data management and coding best practices.
Understanding data types and structures, how data is represented in computer systems and best data management and programming practices, and the context from which data is derived, is crucial for data scientists working in health, social and care services.
This course is designed to equip students with the skills required to realise the values of data and apply best practice, manipulate, analyse, and present data from health data social and care service settings.
2) Course outline
The course focuses on the different data types and structures. The course will first introduce data types, such as numerical and time series, textual and unstructured data, the data life cycle and best data management practices. Implementation of data structures such as arrays, linked lists, graphs and binary trees, the advantages and disadvantages of their use will be covered. The course will compare and contrast these concepts programming in R and Python.
3) Student Learning Experience
Students will learn from data handling experts. The course is delivered online and is divided into five sessions, each lasting a week. Teaching sessions will be composed of written materials and video presentations, accompanied by guided reading in the form of links to journal articles with problem-based learning questions.
Discussion of the content and reading materials will be posted to an online forum, along with students' answers to the problem-based learning questions. Course tutors will moderate discussion boards.
Formative peer and teacher-led feedback will be given throughout the course through the discussion boards, and summative assessment feedback will be provided at the end of the course.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Additional Costs Students will be responsible for their computer equipment and internet access.
Information for Visiting Students
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  None
Course Start Flexible
Course Start Date 08/04/2024
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 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %

Coding task
Descriptive report
Feedback Feedback is information provided to the students about their learning relative to learning outcomes. Feedback is also important to identify areas for improvement; for example, course feedback surveys will be an integral component of course development. The two main types of feedback are formative and summative. Formative feedback involves feedback given during an assessment, while summative feedback is provided after an assessment has been completed.
Formative feedback will be provided throughout the course, for example, during live question and answer sessions, quizzes, and on discussion boards. All assignments will be marked, and feedback is provided within fifteen working days (where possible).
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a critical understanding of how data is stored in computer systems, the data life cycle, the benefits and limitations of R and Python programming languages for handling data, and the importance of best data management and programming practices in health and social care systems.
  2. Apply R and Python programming skills to collect, handle, document, store, and use data, best data management practice to make data findable, accessible, interoperable and reusable, and best programming practice to write maintainable, dependable, efficient and reusable code.
  3. Utilise logical, analytical, and problem-solving skills to make informed decisions about the most appropriate data types and structures to use when creating data sets for downstream analysis or designing software.
  4. Demonstrate the ability to effectively communicate about different data types and structures and their advantages and disadvantages with a wide variety of audiences.
Reading List
P. Brass (2008) Advanced Data Structures
M.T. Goodrich, R. Tamassia, and M. Goldwasser (2013) Data structures and algorithms in Python.
Electronic copies of Brass (2008) and Goodrich, Tamassia, and Goldwasser (2013) are available to download from the University of Edinburgh Library.
R.A. Irizarry (2020) Chapter 20 Introduction to data wrangling in 'Introduction to Data Science: Data Analysis and Prediction Algorithms with R'.
Web pages:
Health and Social Care Integration: Data Types Specification.
NHS application programming interfaces (API): Explore and make use of nationally defined messaging API.
NHS data model and dictionary.
Specific journal articles will also be selected nearer the time of course delivery.

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 handling the different health and social care data types and structures. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to resolve ethical challenges and data ownership 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 also be supported through 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.

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.
Special Arrangements This course will be taught online using the Learn virtual learning environment. All course materials are protected by secure username and password access.
KeywordsData types,data structures,data life cycle,programming (R and Python),best data management prac
Course organiserMiss Brittany Blankinship
Course secretaryMrs Laura Miller
Tel: (0131 6)51 5575
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