This course consists of face-to-face teaching and online learning in Moodle (Research Ethics online course).

The course invites students in various stages of their doctoral thesis to think through, write and process the ethical questions and themes in their own ongoing research projects. It provides a compact knowledge package on ethical issues concerning the research design, data collection/field work and writing-up phases. The course consists of lectures, discussion & student presentations in class and assignments.

Learning outcomes: After the course, students will

- be better equipped to think about and deal with ethical issues at different phases of their research projects
- have skills to discuss and identify issues concerning the ethics both in relation to research participant and the academic community (treatment of participants, informed consent, data lifespan, publication and ethical reporting)
- have knowledge on the key guidelines concerning ethical conduct and know where to go for further information
- have practical tools and means for the writing-up of ethical question in the doctoral thesis.
- have access to an information package on ethics
- know the basic functions of Turnitin plagiarism checker programme

Course Schedule:

1. Face-to-face lectures on 12.9.2017 at 16-20 in Room A08 (Main building)

2. Individual learning using Moodle (Research Ethics online course)

3. Face-to-face workshop on

- 26.9. (Room C7, Main building) at 10-16 and
- 3.10.2017 (Room A2A, Main building) at 10-16

Teacher: PhD Marika Enwald

Enrolment: At the maximum 20 students (2 places reserved for TUT students). Priority in selection is given to non-Finnish-speaking students. Otherwise, selection method is draw. Enrolment in NettiOpsu.  Please check the selection result from NettiOpsu after the enrolment period.

Completion mode: Those accepted to the course are required to send in a pre-assignment (max one page A 4) through Moodle. Active participation is required. Every student will give a 15-20 minutes presentation in the workshop. Every student will write an 6-7 pages essay after the course.

THE PRE-ASSIGNMENT: Please include the following in short:
1)    Your name & discipline
2)    Your Research topic
3)    Potential ethical issues you have encountered or anticipated to encounter as part of your doctoral research and solutions you may already have in mind
4)    Any issues relating to research ethics you wish to be discussed during the course
5)    It is presumed that you could present briefly during the course the ethical questions that you are facing during your research.

Visualization of quantitative data when reporting and publishing findings

Course description:

It is commonly said that “a picture is worth a thousand words”. The same is true when reporting findings of an analysis of quantitative data. A proper visualization of the results might make the difference between the success and failure in telling a story or in publishing one’s findings. This course gives, first, a brief introduction to the R software and to D3 JavaScript library for manipulating documents based on data. Second, the course focuses on visualization of quantitative data which is of utmost importance when reporting and publishing findings. Examples and applications will be done for multivariate, temporal, spatial and text data. Examples used during the course will be based on the R software, and preliminary knowledge of this software is advisable but nor required. Participants can learn more about the R software prior to the course online at: http://www.uta.fi/cast/events/Ronline.html.

Goals: The course:

• Introduces participants to the R environment and to D3
• Introduces the participants to data exploration and data visualization
• Provides a recent variety of techniques and strategies to visualize quantitative data

Place: Computer classroom Ml 50 Linna

Programme

Day 1: 3.11.2017

09.15-12.00   Introduction to R

12.00-13.00   Lunch break

13.00-16.00   Introduction to data exploration and data visualization: types of data and of databases; online databases; Visualization of multivariate data

Day 2: 10.11.2017

09.15-12.00   Visualization of temporal data and of spatial data; text visualization

12.00-13.00   lunch break

13.00-16.00 Introduction to D3; Real time big data applications

PLEASE NOTE: Attendance to BOTH days is required for the completion of the course.

Teacher: Paulo Canas Rodrigues

Pre-assignment: Please write a short (one A4) text stating:

1)    Your name & disciplinary background

2)    State your own motivation for participating on this course and what do you expect to learn.

DEADLINE for the pre-assignments to be announced.

In addition, participants will write a mini-assignment after the second meeting with a two weeks’ deadline.

Enrolment via NettiOpsu. Maximum number of students is 24. Selection method is draw. Students should check the selection result via NettiOpsu after the enrolment period.

Visualization of quantitative data when reporting and publishing findings

Course description:

It is commonly said that “a picture is worth a thousand words”. The same is true when reporting findings of an analysis of quantitative data. A proper visualization of the results might make the difference between the success and failure in telling a story or in publishing one’s findings. This course gives, first, a brief introduction to the R software and to D3 JavaScript library for manipulating documents based on data. Second, the course focuses on visualization of quantitative data which is of utmost importance when reporting and publishing findings. Examples and applications will be done for multivariate, temporal, spatial and text data. Examples used during the course will be based on the R software, and preliminary knowledge of this software is advisable but nor required. Participants can learn more about the R software prior to the course online at: http://www.uta.fi/cast/events/Ronline.html.

Goals: The course:

• Introduces participants to the R environment and to D3
• Introduces the participants to data exploration and data visualization
• Provides a recent variety of techniques and strategies to visualize quantitative data

Place: Computer classroom Ml 50 Linna

Programme

Day 1: 3.11.2017

09.15-12.00   Introduction to R

12.00-13.00   Lunch break

13.00-16.00   Introduction to data exploration and data visualization: types of data and of databases; online databases; Visualization of multivariate data

Day 2: 10.11.2017

09.15-12.00   Visualization of temporal data and of spatial data; text visualization

12.00-13.00   lunch break

13.00-16.00 Introduction to D3; Real time big data applications

PLEASE NOTE: Attendance to BOTH days is required for the completion of the course.

Teacher: Paulo Canas Rodrigues

Pre-assignment: Please write a short (one A4) text stating:

1)    Your name & disciplinary background

2)    State your own motivation for participating on this course and what do you expect to learn.

DEADLINE for the pre-assignments to be announced.

In addition, participants will write a mini-assignment after the second meeting with a two weeks’ deadline.

Enrolment via NettiOpsu. Maximum number of students is 24. Selection method is draw. Students should check the selection result via NettiOpsu after the enrolment period.

Learning outcomes
After completing the course, the participants
- know the phases of the process of knowledge discovery (data prepocessing, data mining and postprocessing)
- know basic data mining tasks and methods
- are aware of possibilities of utilising data mining in different research fields

Description

In data mining, large quantities of data are explored and analysed by automatic and semi-automatic means to discover novel, interesting information. Data mining is an interdisciplinary field combining e.g. methods from computer sciences and statistics. It has wide, diverse application areas from education, social, business and administrative sciences to medical and life sciences.

Course contents
-    Lectures 10 h
-    Hands-on exercises with data mining tools 10 h
-    Reading research articles related to applications of data mining methods in participant’s own field and writing a short report
-    Giving a presentation on applications of data mining in participant’s own field (presentation session 3 h)

Teachers: Kati Iltanen, Martti Juhola, Henry Joutsijoki

Target group
The course is intended for post-graduate students who are interested in data mining. No computer sciences or statistics background is required.

Enrolment: At the maximum 15 students. Selection method is draw.

Teaching:

Lectures:
Wed 2.5.2018 at 10-12 Pinni B1083
Fri 4.5.2018 at 10-12 Pinni B1083
Wed 9.5.2018 at 10-12 Pinni B1083
Fri 11.5.2018 at 10-12 Pinni B1083
Wed 16.5.2018 at 10-12 Pinni B1083

Practices:

Wed 2.5.2018 at 12-14 computer classroom Pinni B1084
Fri 4.5.2018 at 12-14 computer classroom Pinni B1084
Wed 9.5.2018 at 12-14 computer classroom Pinni B1084
Fri 11.5.2018 at 12-14 computer classroom Pinni B1084
Wed 16.5.2018 at 12-14 computer classroom Pinni B1084

Presentation session
Fri 25.5.2018 at 10-13 Pinni B1083


Evaluation: Pass/fail

This course invites students in various stages of their doctoral thesis to think through, write and process the ethical questions and themes in their own ongoing research projects. It provides a compact knowledge package on ethical issues concerning the research design, data collection/field work and writing-up phases. The course consists of lectures, discussion & student presentations in class and assignments.

Learning outcomes: After the course, students will

- be better equipped to think about and deal with ethical issues at different phases of their research projects
- have skills to discuss and identify issues concerning the ethics both in relation to research participant and the academic community (treatment of participants, informed consent, data lifespan, publication and ethical reporting)
- have knowledge on the key guidelines concerning ethical conduct and know where to go for further information
- have practical tools and means for the writing-up of ethical question in the doctoral thesis.
- have access to an information package on ethics
- know the basic functions of Turnitin plagiarism checker programme

Course Schedule:

Lectures

Fri 13.4.2018 at 12-16 o'clock, Room E221 (Main building)

Fri 20.4.2018 at 12-16 o'clock, Room E221 (Main building)

Fri 27.4.2018 at 12 -16 o'clock, Room E221 (Main building)

Workshop

Fri 4.5.2018 at 10-16 o'clock, Room D14 (Main building)

Fri 11.5.2018 at 10-16 o'clock, Room D14 (Main building)

Enrolment in NettiOpsu. At the maximum 20 students (2 places reserved for TUT students). TUT students apply the right to study through the Service of Cross-Institutional Studies. 

Priority in selection is given to non-Finnish-speaking students. Otherwise, selection method is draw. Please check the selection result from NettiOpsu after the enrolment period.

Completion mode: Those accepted to the course are required to send in a pre-assignment (max one page A 4) through Moodle. Active participation is required. Every student will give a 15-20 minutes presentation in the workshop. Every student will write an 6-7 pages essay after the course.

THE PRE-ASSIGNMENT: Please include the following in short:
1)    Your name & discipline
2)    Your Research topic
3)    Potential ethical issues you have encountered or anticipated to encounter as part of your doctoral research and solutions you may already have in mind
4)    Any issues relating to research ethics you wish to be discussed during the course
5)    It is presumed that you could present briefly during the course the ethical questions that you are facing during your research.