Study Guide

Objective

The student knows how to perform advanced data analytics and modifications to datasets used by other applications, such as machine learning algorithms. The student is able to inspect and decide suitable methods for different use cases depending on the data structure.

Content

- Optimizing datasets and reducing dimensions
- Decision-making strategies for dataset optimizations
- Advanced imputation and managing incomplete data
- Operations and management regarding large and/or complex datasets
- Combining data systems and data harmonization
- Advanced statistical measurements
- Analytics pipelines

Location and time

Classroom locations and exact dates according to timetable.

Materials

Lecture materials and exercises will be placed in the Moodle workspace.

Teaching methods

Lectures, workshops, examples, exercises and self-supervised work.

Content scheduling

- Optimizing datasets and reducing dimensions (PCA etc.)
- Decision-making strategies for dataset optimizations
- Advanced imputation and managing incomplete data
- Operations and management regarding large and/or complex datasets
- Combining data systems and data harmonization
- Advanced statistical measurements
- Analytics and optimization pipelines
+ other advanced features with common data analytics and machine learning tools

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

Grade 1: The student is able to perform selected advanced data analytics operations for a given dataset under guidance. The student has the basic knowledge of different advanced techniques that can be considered for manipulating different datasets.

Assessment criteria, good (3)

Grade 3: The student is able to perform selected advanced data analytics operations for a given dataset independently. The student has the basic knowledge of different advanced techniques that can be considered for manipulating different datasets.

Assessment criteria, excellent (5)

Grade 5: The student is able to perform various advanced data analytics operations for a given dataset independently. The student has the basic knowledge of different advanced techniques that can be considered for manipulating different datasets. The student is able to search for more advanced data analytics methods and independently apply them to their dataset applications.

Assessment methods and criteria

The course will be graded on the scale of 1 - 5 and failed (0). The grading will be based on the submitted exercises/assignments.