Data Analysis
Course code
DTI8011.DT
old course code
Course title in Estonian
Andmeanalüüs
Course title in English
Data Analysis
ECTS credits
6.0
Assessment form
Examination
lecturer of 2023/2024 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
To create opportunities for acquiring the data analysis knowledge needed to write a doctoral thesis and to contribute to the development of data analysis skills.
Brief description of the course
This course introduces modern data analysis methods, focusing on theoretical foundations, practical applications, and recent advances in the field. Students will gain expertise in various techniques for extracting meaningful insights from diverse data sets, including large and complex data sets, preparing them for research in various domains related to their research.
Learning outcomes in the course
Upon completing the course the student:
- demonstrates an enhanced understanding of the fundamental principles of modern data analysis, encompassing statistical inference, machine learning, and data mining, across diverse domains;
- acquires proficiency in data wrangling, cleaning, and preprocessing techniques essential for preparing datasets for analysis;
- develops a deeper understanding of both quantitative and qualitative data analysis methods and techniques, enabling comprehensive exploration and interpretation of data;
- gains familiarity with advanced methods tailored for analysing large and complex datasets, facilitating the extraction of meaningful insights from extensive and intricate data structures;
- engages in critical evaluation of the strengths and limitations inherent in various data analysis methods, fostering a discerning approach to selecting appropriate techniques for specific analytical tasks;
- applies advanced data analysis techniques effectively to address real-world challenges across different domains, demonstrating the capability to utilise theoretical knowledge in practical problem-solving contexts;
- exhibits proficiency in communicating findings of data analysis through both written reports and oral presentations, demonstrating clarity, coherence, and persuasiveness in conveying complex analytical results to diverse audiences.
- demonstrates an enhanced understanding of the fundamental principles of modern data analysis, encompassing statistical inference, machine learning, and data mining, across diverse domains;
- acquires proficiency in data wrangling, cleaning, and preprocessing techniques essential for preparing datasets for analysis;
- develops a deeper understanding of both quantitative and qualitative data analysis methods and techniques, enabling comprehensive exploration and interpretation of data;
- gains familiarity with advanced methods tailored for analysing large and complex datasets, facilitating the extraction of meaningful insights from extensive and intricate data structures;
- engages in critical evaluation of the strengths and limitations inherent in various data analysis methods, fostering a discerning approach to selecting appropriate techniques for specific analytical tasks;
- applies advanced data analysis techniques effectively to address real-world challenges across different domains, demonstrating the capability to utilise theoretical knowledge in practical problem-solving contexts;
- exhibits proficiency in communicating findings of data analysis through both written reports and oral presentations, demonstrating clarity, coherence, and persuasiveness in conveying complex analytical results to diverse audiences.
Teacher
Sirje Virkus
Prerequisite course 1
Study programmes containing that course