Credits: 10

Schedule: 11.09.2019 - 05.12.2019

Contact information for the course (applies in this implementation): 

Senior University Lecturer Jorma Laaksonen, <>, tel. +358 50 3058719, room B326

Teaching Period (valid 01.08.2018-31.07.2020): 

I-II (Autumn 2020)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After the course, you can work as a data scientist in a team. You understand the structure and technical and non-technical challenges of data science projects. Furthermore, you learn to apply data analysis tools in a real-world data analysis project. Finally, you learn to document your project work and its outcomes, and to present its results and conclusions thereof both in writing and verbally.

Content (valid 01.08.2018-31.07.2020): 

The course consists of a data science project which will be done in a small group for a real client from industry or academia. The activities include project management, requirements specification, design, coding, data collection and curating, experimenting, documentation, and presenting.

Details on the course content (applies in this implementation): 

In Autumn 2019, the course is implemented as a pilot of two groups. Project topics and visiting lectures will come from two companies, Futurice and Reaktor.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Project work, its documentation and presentation.

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 

The students report their progress and learning in MyCourses and their progress is followed weekly. Some extra reading tasks will be given in MyCourses and the students report after finishing each task. The final grade is based on the outcomes of the project work, each student's individual contributions to it and the student's self- and peer-reported activity.

Workload (valid 01.08.2018-31.07.2020): 

Lectures 10h, meetings with the instructor 36h, project work in groups 221h.

Details on calculating the workload (applies in this implementation): 

in Autumn 2019 there will be:

  • 5 x 2-hour lectures (3 by Jorma Laaksonen, 2 by visitors)
  • 9 x 2-hour supervised group meetings
  • 2 x 2-hour excursions to the companies
  • 2 x 1-hour final presentations in the companies
  • 233 hours group and individual work

Details on the course materials (applies in this implementation): 

Wes McKinney: Python for Data Analysis, 2nd ed. Available:

Prerequisites (valid 01.08.2018-31.07.2020): 

CS-A1110 Programming 1, CS-A1120 Programming 2, CS-A1150 Databases, CS-C3160 Data Science and at least 90 credits of bachelor studies.

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 

Registration via WebOodi. Please see WebOodi for registration dates.

Further Information (valid 01.08.2018-31.07.2020): 

The course is primarily targeted to the students of the Data Science major, but also others can register by prior request.

Details on the schedule (applies in this implementation): 

11.9.Wed10-12T5L1intro, topicsJorma Laaksonen
13.9.Fri12-14T5G1kick-offAntti Ajanki, Jaakko Särelä
scikit, pandas, NLPJorma Laaksonen
20.9.Fri12-14T5G2 Jorma Laaksonen
2.10.Wed10-12T5L3project managementJorma Laaksonen
4.10.Fri12-14T5G3Reaktor 1st check-up Jorma Laaksonen, Jaakko Särelä
Wed10-12T5L4DS real-world casesJaakko Särelä
11.10.Fri12-14T5G4Futurice 1st check-upJorma Laaksonen, Antti Ajanki
16.10.Wed10-12T5L5DS toolbox, best practicesAntti Ajanki
18.10.Fri12-14T5G5 Jorma Laaksonen
31.10.Thu14-16T4G6 Jorma Laaksonen
4.11.Mon14-16FuturiceS1site visitAntti Ajanki
7.11.Thu14-16T4G72nd check-upAntti Ajanki, Jaakko Särelä
18.11.Mon14-16ReaktorS2site visitJaakko Särelä
21.11.Thu14-16T4G8 Jorma Laaksonen
28.11.Thu14-16T4G9 Jorma Laaksonen
2.12.Mon14-16FuturiceP1final presentationAntti Ajanki
5.12.Thu14-16ReaktorP2final presentationJaakko Särelä


Registration and further information