Veidlapa Nr. M-3 (8)
Study Course Description
Data Processing and Visualization
Course Code
SVI_004
Branch of Science
Mathematics
ECTS
5.00
Target Audience
Business Management; Communication Science; Dentistry; Health Management; Information and Communication Science; Life Science; Management Science; Marketing and Advertising; Medical Services; Medical Technologies; Medicine; Midwifery; Political Science; Psychology; Public Health
LQF
Level 7
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Sergio Andres Uribe Espinoza
Structure Unit Manager
Daiga Behmane
Structural Unit
Health Management Teaching Group
Contacts
Riga, Kronvalda boulevard 9, +371 67338307
About Study Course
Objective
The "Data Processing and Visualization" course aims to provide students with the essential skills and knowledge required to proficiently manipulate, analyze, and visualize data using the R programming language. The course is designed to provide a comprehensive introduction to data science concepts, focusing on data manipulation, cleaning, and exploratory data analysis. Through hands-on and project-based learning, students will develop the ability to create insightful visualizations, effectively communicate data-driven insights, and apply analytical techniques to solve real-world problems in digital health and health management.
Preliminary Knowledge
Basic knowledge of spreadsheets or Excel is desirable but not required for this course. It is designed to accommodate students with no prior experience in programming or data analysis and provides a foundational understanding of data science concepts using R.
Learning Outcomes
Knowledge
1.Upon completing the module´s course the students will: Understand the principles and techniques of exploratory data analysis, focusing on summarization and visualization of data. Recognize the importance of tidy data and its role in facilitating analysis. Learn about data cleaning processes essential for accurate exploratory analysis.
Skills
1.The students will be able to: Conduct thorough exploratory analyses using various visualization techniques. Identify patterns, trends, and anomalies in datasets. Utilize R to manipulate and prepare data for analysis effectively.
Competences
1.Students will be able to: Assess data quality and make necessary adjustments to facilitate insightful explorations. Create informative visualizations that communicate the findings clearly and effectively. Develop an analytical mindset that prioritizes understanding data structures and relationships within data.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
• Independent acquisition of scientific literature;
• Collection and analysis of information;
• Preparation of individual and group works and presentation of results.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
Engagement and Participation: Assessment of active and meaningful engagement during interactive lectures.
Quality and Completion of Tasks: Evaluation based on the quality, creativity, and timely completion of individual and group class activities.
Capstone Project Excellence: Grading based on the thoroughness, innovation, and analytical depth demonstrated in the final capstone project.
Students will be assessed based on class participation and practical application of skills, constituting 40% of their overall grade, along with a final capstone project for the remaining 60%.
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Intro to R – Tidy Data - The grammar of Graphics
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Intro to R – Tidy Data - The grammar of Graphics
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Visualization Fundamentals
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Visualization Fundamentals
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Manipulation Techniques: Split-Apply-Combine
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Manipulation Techniques: Split-Apply-Combine
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Exploratory Data Analysis Techniques
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Exploratory Data Analysis Techniques
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Importing and Cleaning Data
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Importing and Cleaning Data
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Working with Strings and Dates
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Working with Strings and Dates
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Wrangling
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data Wrangling
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Joining Datasets in R
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Joining Datasets in R
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Effective Data Reporting with R Markdown
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Effective Data Reporting with R Markdown
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Project Presentations and Closing Remarks
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Project Presentations and Closing Remarks
|
Total ECTS (Creditpoints):
5.00
Contact hours:
40 Academic Hours
Final Examination:
Exam
Bibliography
Required Reading
1.
"R for Data Science" - Grolemund. 2016. Garrett & Wickham, Hadley.
2.
Broman KW, Woo KH. 2017. Data organization in spreadsheets. PeerJ Preprints 5:e3183v1
3.
Ellis, S. E., & Leek, J. T. 2018. How to Share Data for Collaboration. The American Statistician, 72(1), 53–57.
Additional Reading
1.
"Modern Dive" - An Introduction to Statistical and Data Sciences
2.
Krzywinski, M., 2013a. Points of view: Elements of visual style. Nat. Methods 10, 371–371.
3.
Krzywinski, M., 2013b. Points of view: Labels and callouts. Nat. Methods 10, 275–275.
4.
Krzywinski, M., 2013c. Points of view: Axes, ticks and grids. Nat. Methods 10, 183–183.
5.
Krzywinski, M., Cairo, A., 2013. Points of view: Storytelling. Nat. Methods 10, 687–687.
6.
Krzywinski, M., Savig, E., 2013. Points of view: Multidimensional data. Nat. Methods 10, 595–595.
7.
Krzywinski, M., Wong, B., 2013. Points of view: Plotting symbols. Nat. Methods 10, 451–451.
8.
Streit, M., Gehlenborg, N., 2014. Points of View: Bar charts and box plots. Nat. Methods 11, 117–117.
9.
Wong, B., 2010. Design of data figures. Nat. Methods 7, 665.
10.
Producers, G.F., n.d. Effective tables and graphs in official statistics.
Other Information Sources
1.
"Data Visualization: A Practical Introduction" - Kieran Healy.
2.
"Fundamentals of Data Visualization" - Claus O. Wilke. Wilke, C.O., n.d. Fundamentals of Data Visualization [WWW Document]. (accessed 4.17.20).
3.
Holtz, Y., n.d. The R Graph Gallery [WWW Document]. (accessed 10.13.20).