Study Guide

Objectives (course unit)

The student understands basic concepts of Statistics and Classical Data Analysis. The student can collect and preprocess data for analysis and visualization. The student is able make appropriate data analyses and visualizations for the problem. The student can evaluate both the quality and the applicability of the results.

Content (course unit)

Course content is:
- Data collection and Data Preprocessing
- Visual analytics process
- Basic methods of Statistics and Classical Data Analysis
- Data analysis with Analytics Tools and Python
- Visualization models
- Critical evaluation of results

Further information (course unit)

Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.

Assessment criteria, satisfactory (1-2) (course unit)

Student can sufficiently implement data collection and data preprocessing for a given task. Student knows how to implement visual analytics processes. Student knows some basic methods of statistics and classical data analysis. The student can solve some given data analysis problems with analytics tools or python. Student can use given visualization models. Student understands the meaning of the results.

Assessment criteria, good (3-4) (course unit)

Student can implement data collection and data preprocessing for a given task. Student can implement visual analytics processes. Student knows and understands basic methods of statistics and classical data analysis. The student can solve given data analysis problems with analytics tools and python. Student knows and can exploit given visualization models. Student can evaluate the meaning of the results.

Assessment criteria, excellent (5) (course unit)

Student can implement data collection and data preprocessing with the appropriate methods. Student can implement various visual analytics processes. Student knows and understands in depth basic methods of statistics and classical data analysis. The student can solve versatile data analysis problems with analytics tools and python. Student knows and can exploit visualization models as appropriate. Student can critically evaluate and interpret the meaning of the results.

Exam schedules

No exam.

Assessment scale

0-5

Teaching methods

Contact teaching
Assignments (the primary learning method)
Group work and presentation

Learning materials

Moodle course with links to additional material.

Student workload

See the period timetable. See the Moodle course for instructions when and how to attend the contact teaching hours.

Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)

Less than 30% of the exercises completed.

Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)

The student is familiar with the essential concepts and can implement simple applications with specific instructions. At least 30% of the exercises completed.

Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)

The student is familiar with the basic concepts and can implement simple applications autonomously. At least 60% of the exercises completed.

Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)

The student is familiar with the main concepts, is able to critically evaluate application requirements, and can implement realistic applications autonomously. At least 90% of the exercises completed. A good group work completed and presented.