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

All the necessary course materials will be compiled and made available via the Moodle workspace.

Self-study prerequisites, if not familiar: Basics of Python:

Familiarity with the basic concepts and syntax of the Python programming language. Basics of Data Management: Understanding of fundamental aspects of data handling, including JSON and databases. Basics of Information Technology: Knowledge of key IT concepts, including understanding what a CPU and GPU are.

Teaching methods

Face-to-face teaching in classroom, online material and assignments

Content scheduling

- Analytics tools and programs. Introduction to data analytics. Overview and implementation
- Data preparation. Filtering, retrieval, merging, and classification
- Use of appropriate data analytics libraries. Pandas, NumPy, scikit-learn, and others
- Data visualization and analysis. Visualization tools and principles. Exploring and analyzing data

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

Grades are based on the quality, quantity, and comprehensiveness of the exercises.