Veidlapa Nr. M-3 (8)
Study Course Description
Programming Basics for Healthcare Professionals
Course Code
SVUEK_148
Branch of Science
Other social sciences
ECTS
5.00
Target Audience
Health Management; Information and Communication Science; Medical Services; Medical Technologies; Public Health
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Didzis Lauva
Structure Unit Manager
Daiga Behmane
Structural Unit
Health Management Teaching Group
Contacts
Riga, 9 Kronvalda boulevard, svek@rsu.lv, +371 67338307
About Study Course
Objective
The objective of this course is to provide prospective health care professionals with essential programming skills to improve their ability to analyse patient data, automate administrative tasks and data handling, as well as collaborate with IT professionals. This course promotes algorithmic and computational thinking, allowing health care professionals to make informed decisions when dealing with different types of data. The course aims to strengthen technical expertise among health care professionals by ensuring they are well prepared to integrate advanced digital health solutions into their practices.
Preliminary Knowledge
Prior knowledge of mathematics at secondary school level.
Learning Outcomes
Knowledge
1.After completing this course, students will gain knowledge of the fundamentals of programming, understanding of programming languages and their application. They will learn the basics of data analysis by acquiring knowledge of methods of data acquisition, cleaning and analysis. Students will gain an understanding of the development of algorithms and their application in the context of healthcare. They will also gain knowledge of modern technologies and tools used in digital health solutions.
Skills
1.Students will learn programming skills, will be able to read, understand and write a code using programming languages like Python or R. Will learn to work with large amounts of data using a variety of data processing tools, and be able to create scripts and programs to automate routine tasks. Students will learn data visualisation skills using a variety of graphic tools and techniques.
Competences
1.The study process will develop problem solving competences that allow students to identify and solve problems using algorithmic and computational thinking. Will be able to cooperate effectively with IT professionals to implement and maintain digital health solutions. Students will gain an understanding of data security and privacy issues in healthcare, as well as be able to integrate digital solutions at a strategic level to improve the quality and efficiency of healthcare.
Assessment
Individual work
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Individual work |
-
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-
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During the course, each student should create and present a project to work with large amounts of data using the knowledge acquired during the course.
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Examination
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Title
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% from total grade
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Grade
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|---|---|---|
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1.
Examination |
-
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-
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By developing and defending a project, it is passed and the student is admitted to the examination. The final assessment of the course consists of defending a project (50%) and an examination (50%). If the project is not defended, the student is not allowed to pass the examination.
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Study Course Theme Plan
FULL-TIME
Part 1
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Lecture
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Modality
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Location
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Contact hours
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On site
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Computer room
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1
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Topics
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Introduction to programming and its role in the modern digital world.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Introduction to the programming environment and algorithm principles. Setting up the environment, its properties, basic syntax. Libraries and methods to be connected.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Introduction to the programming environment and algorithm principles. Setting up the environment, its properties, basic syntax. Libraries and methods to be connected.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Data types and handling them. Integers, floating point numbers (decimal numbers), symbol strings and logical values, arithmetic and string operations.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Data types and handling them. Integers, floating point numbers (decimal numbers), symbol strings and logical values, arithmetic and string operations.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Work with variables and input and output of information. Setting variables, assigning values, reading data and its formatted output.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Work with variables and input and output of information. Setting variables, assigning values, reading data and its formatted output.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Control structures. Branching. If-then conditions, statements. Branching conditions.
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Cycles. Cycle structures and their branches.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Control structures. Branching. If-then conditions, statements. Branching conditions.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Cycles. Cycle structures and their branches.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Functions. Meaning, invocation and use of functions. Function parameters and return calculated values.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Functions. Meaning, invocation and use of functions. Function parameters and return calculated values.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Data structures: closed and open lists, arrays.
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Data structures: dictionaries. Dictionary access elements.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Data structures: closed and open lists, arrays.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Data structures: dictionaries. Dictionary access elements.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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String manipulation and methods. Working with text information, manipulations. Regular expressions (regex).
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File processing. Reading and writing text files. File modes (read, write, append).
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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String manipulation and methods. Working with text information, manipulations. Regular expressions (regex).
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
|
Computer room
|
2
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Topics
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File processing. Reading and writing text files. File modes (read, write, append).
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Processing errors using the exception processing mechanism Try-Catch.
Registering exceptions and error types.
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Object-oriented programming. Classes. Encapsulation, inheritance and polymorphism.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Processing errors using the exception processing mechanism Try-Catch.
Registering exceptions and error types.
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Object-oriented programming. Classes. Encapsulation, inheritance and polymorphism.
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Lecture
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Study room
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1
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Topics
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Installing and viewing third party libraries. Numpy library.
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Installing and viewing third party libraries. Pandas library.
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Work with different types of data formats.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
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On site
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Computer room
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2
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Topics
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Installing and viewing third party libraries. Numpy library.
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Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
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2
|
Topics
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Installing and viewing third party libraries. Pandas library.
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-
Class/Seminar
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Modality
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Location
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Contact hours
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|---|---|---|
|
On site
|
Computer room
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2
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Topics
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Work with different types of data formats.
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Total ECTS (Creditpoints):
5.00
Contact hours:
40 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
Tim Hall, J.P. Stacey. Python 3 for absolute beginners. 2009 (akceptējams izdevums)
2.
Phuong Vo.T.H, Martin Czygan, Ashish Kumar, Kirthi Raman. Python: Data Analytics and Visualization. 2017
3.
Fabrizio Romano. Learn Python Programming: The No-Nonsense, Beginner's Guide to Programming, Data Science, and Web Development with Python 3. 7, 2018
4.
Joakim Wassberg. Computer Programming for Absolute Beginners: Learn Essential Computer Science Concepts and Coding Techniques to Kick-start Your Programming Career. 2020
Additional Reading
1.
Mark Lutz. Learning Python. 2009
Other Information Sources
1.
Jānis Zuters. Programmēšanas pamati ar valodu Python. Latvijas Universitāte, 2021
2.
Raivis Ieviņš. Programmēšanas pamati, C++ un Java. 2018