Opinto-opas

Tavoitteet (OJ)

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.

Sisältö (OJ)

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

Lisätiedot (OJ)

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

Arviointikriteerit, tyydyttävä (1-2) (OJ)

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.

Arviointikriteerit, hyvä (3-4) (OJ)

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.

Arviointikriteerit, kiitettävä (5) (OJ)

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.

Arviointimenetelmät ja arvioinnin perusteet

Weekly assignments and project work presentation.

Arviointiasteikko

0-5

Tavoitteet (OJ)

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.

Sisältö (OJ)

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

Lisätiedot (OJ)

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

Arviointikriteerit, tyydyttävä (1-2) (OJ)

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.

Arviointikriteerit, hyvä (3-4) (OJ)

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.

Arviointikriteerit, kiitettävä (5) (OJ)

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.

Arviointiasteikko

0-5

Tavoitteet (OJ)

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.

Sisältö (OJ)

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

Lisätiedot (OJ)

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

Arviointikriteerit, tyydyttävä (1-2) (OJ)

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.

Arviointikriteerit, hyvä (3-4) (OJ)

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.

Arviointikriteerit, kiitettävä (5) (OJ)

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.

Arviointiasteikko

0-5

Tavoitteet (OJ)

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.

Sisältö (OJ)

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

Lisätiedot (OJ)

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

Arviointikriteerit, tyydyttävä (1-2) (OJ)

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.

Arviointikriteerit, hyvä (3-4) (OJ)

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.

Arviointikriteerit, kiitettävä (5) (OJ)

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.

Arviointiasteikko

0-5