Data Analysis and VisualizationLaajuus (7 cr)
Code: 5G00FT11
Credits
7 op
Objectives
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 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
Assessment criteria, satisfactory (1-2)
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)
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)
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.
Further information
Includes content of previous Mathematics 3 course. The course eliminates duplication observed in courses.
Enrolment period
23.11.2023 - 07.01.2024
Timing
12.01.2024 - 05.05.2024
Credits
7 op
Mode of delivery
Contact teaching
Unit
Software Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Seats
0 - 42
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Ossi Nykänen
Person in charge
Ossi Nykänen
Groups
-
22I260EA
-
22I260EBDegree Programme in Software Engineering
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.
Enrolment period
15.12.2022 - 08.01.2023
Timing
11.01.2023 - 07.05.2023
Credits
7 op
Mode of delivery
Contact teaching
Unit
ICT Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Seats
0 - 45
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Ossi Nykänen
Person in charge
Ossi Nykänen
Groups
-
21I260EA
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.
Enrolment period
15.12.2022 - 08.01.2023
Timing
01.01.2023 - 07.05.2023
Credits
7 op
Mode of delivery
Contact teaching
Unit
ICT Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Ossi Nykänen
Person in charge
Ossi Nykänen
Groups
-
21I260EB
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 methods and criteria
Less than 30% of the exercises completed.
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.
Enrolment period
15.11.2021 - 09.01.2022
Timing
14.01.2022 - 29.04.2022
Credits
7 op
Mode of delivery
Contact teaching
Unit
ICT Engineering
Campus
TAMK Main Campus
Teaching languages
- English
Seats
0 - 62
Degree programmes
- Bachelor's Degree Programme in Software Engineering
Teachers
- Ossi Nykänen
Person in charge
Ossi Nykänen
Groups
-
20I260E
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.
Further information
NOTE: In accordance with the current Covid-19 guidelines, teaching in the Industrial Technology Unit shall be arranged (at least) UNTIL January 16, 2022 ONLY ONLINE.I .e. the course at least starts remotely via MS Teams.
Old info (prior to the aforementioned covid instructions): By default, the course would be organized f2f at TAMK main campus (partial remote or hybrid participation via MS Teams might be available). See the Moodle course for instructions how to attend the contact teaching hours: https://moodle.tuni.fi/course/view.php?id=24456 . Please note that any additional covid restrictions might imply organizational changes to the course.
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.