Data Analysis and Visualization (4cr)
Code: C-02504-TTC8040-3013
General information
- Enrollment
- 18.11.2024 - 09.01.2025
- Registration for the implementation has ended.
- Timing
- 10.02.2025 - 28.03.2025
- Implementation has ended.
- Number of ECTS credits allocated
- 4 cr
- Institution
- JAMK University of Applied Sciences, Opintojakso toteutetaan kevätlukukaudella 2025.
- Teaching languages
- English
- Seats
- 0 - 5
- Course
- C-02504-TTC8040
Evaluation scale
0-5
Objective
Purpose: For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content. EUR-ACE Competences: Knowledge and Understanding Engineering Practice Investigations and information retrieval Course objectives: You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.
Content
- Quantity and quality of data - Datan analysis as a part of information processing - Describing data - Modifying data - Data visualization - Statistics - Time series - Correlation - Linear ja nonlinear regression model - Modelling periodical data - Representing the analysed results
Location and time
The course will be implemented in the spring semester of 2025.
Materials
The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.10+ environment, Git version control, NumPy and Pandas libraries, visualization libraries and other applicable libraries.
Teaching methods
Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.
Employer connections
The aim is to connect the content of the course to problems that occur in working life.
Completion alternatives
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
The workload of one credit corresponds to 27 hours of study. The total amount of study work (4 ECTS) in the course is 108 hours.
Qualifications
Basics in computing, programming, knowledge and know-how of Python programming language.