Curricula

Objective

Data analysis and visualization are needed in solutions to both data-based and machine learning problems. You get to know statistical methods, clean and pre-process data, and visualize data. You learn data analysis and visualization in practical applications.

EUR-ACE Knowledge and Understanding
You understand the implementation methods of data preprocessing.

EUR-ACE Engineering practice
You can use statistical analysis methods. You can present data in an understandable way and draw conclusions from the results of the analysis. You can choose diagram types suitable for certain data and present the data visually.

Content

Data analysis methods
Filtering and sorting data
Data cleaning and preprocessing indexing
Data visualization
Creating Diagram
Statistics
Correlation
Cross tabulation
Creating the data analysis

Qualifications

Basics of Programming

Assessment criteria, satisfactory (1)

Sufficient (1)
You know the basics of data preprocessing. You know some statistical analysis methods. You know how to present data. You know different types of diagrams.

Satisfactory (2)
You know how to pre-process data. You know statistical analysis methods. You know how to present data in different ways. You can produce some types of diagrams.

Assessment criteria, good (3)

Good (3)
You know data preprocessing. You know how to use and apply some statistical analysis methods. You can present data in an understandable way, and draw concise conclusions from the results. You can produce different types of diagrams.

Very good (4)
You know how to preprocess data and compare the obtained results. You know how to use and analyze statistical analysis methods. You can present data in an understandable way and draw conclusions from the results. You can produce different types of diagrams.

Assessment criteria, excellent (5)

Excellent (5)
You know how to preprocess data and interpret the obtained results. You can interpret the results of statistical analysis methods. You can present data comprehensibly and draw conclusions from the results critically. You can choose the most appropriate diagram types for the data and justify your choices.