Study Guide

Objective

The student knows the main content of the selected data analytics libraries and is able to utilize them for data preparation and statistical processing for utilization in machine learning.

Content

- Data preparation: filtering, extraction, aggregation and classification
- Data visualization, research and analysis
- Use of suitable data analytics libraries

Materials

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

Teaching methods

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

Exam schedules

The course will be graded based on personal work and exercises.

Content scheduling

Topics include, but are not limited to:
- Quick Python recap
- NumPy
- pandas
- seaborn and matplotlib
- Data formats and management
- EDA - Exploratory Data Analysis
+ other relevant topics

Evaluation scale

H-5

Assessment criteria, satisfactory (1)

The student is able to prepare and modify the data of a simple example case in a way that it can be utilized in machine learning algorithms or cloud services.

Assessment criteria, good (3)

The student is able to choose case-specific methods for data preparation and to modify the data in such a way that it can be utilized in machine learning and cloud services.

Assessment criteria, excellent (5)

The student is able to select the best case-specific methods for data preparation and to modify the data in a way that they can be utilized further in machine learning algorithms and cloud services.

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.