Data Analysis and Visualization (10cr)

Objectives (course unit)

The student understands basic concepts of Statistics and Classical Data Analysis. The student knows about methods for collecting and preprocessing data for analysis and visualization. The student can 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 methods
- Visual analytics process
- Basic methods of Statistics and Classical Data Analysis
- Data analysis with Analytics Tools and Python
- Visualization models
- Critical evaluation of results

Basics of data analysis and visualization (3 cr): Concepts: population, sample, sampling. Statistical indicators: Mean, standard deviation, median, mode, confidence intervals. P-value and tests (single variable, correlation, khii ^ 2, t-test of two independent / dependent samples. Data visualization. Linear regression, fitting the line to a set of points, the correlation coefficient and its square. Excel / Matlab etc. as a tool.

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

The student knows at least one data collection and data preprocessing method. The student can implement a visual analytics process. The student knows some simple methods of data analysis and visualization. The student can implement a data analysis cases with an analytic tool and with Python. The student can use a visualization model. The student can conduct an evaluation of the results.

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

The student knows some different data collection and data preprocessing methods. The student can implement some appropriate visual analytics processes. The student knows some basic methods of data analysis and visualization. The student can implement a data analysis cases with appropriate analytics tools and with Python. The student can use some visualization models. The student can conduct a critical evaluation of the results.

Assessment criteria, excellent (5) (course unit)

The student knows and can exploit comprehensively different data collection and data preprocessing methods. The student can implement different visual analytics processes. The student knows the common basic methods of data analysis and visualization. The student can implement different data analysis cases with appropriate analytics tools and with Python. The student can exploit visualization models. The student can conduct a comprehensive critical evaluation of the results.

Exam schedules

No exam.

Assessment methods and criteria

Assignment and presentation scores and the related activity.

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.