Master's Programme in Data Science is responsible for the course.
The course belongs to the Data Science Methods module.
The course is primarily intended for students of the Data Science Master's program. Other students can enrol for the course, but in case it fills up preference is given for the Data Science students.
Prerequisites in terms of knowledge
Software development skills on a level that is sufficient for working as part of a larger software development team (good programming skills, version control etc), for example as obtained during Bachelor in Computer Science. Some background on modeling data; no requirements are assumed on specific set of algorithms, but one should be familiar with the basic process of learning models from data and evaluating their accuracy, and should know some practical models or algorithms that can be applied for such tasks.
Prerequisites for students in the Data Science programme, in terms of courses
DATA11002 Introduction to Machine Learning (or DATA12002 Probabilistic Graphical Models)
Prerequisites for other students in terms of courses
Good programming skills; DATA11001 Introduction to Data Science; at least one of: DATA11002 Introduction to Machine Learning, DATA20001 Deep Learning, DATA12002 Probabilistic Graphical Models
Recommended preceding courses
The project is about applying theoretical knowledge into solving practical problems, and hence all other courses in the program support the course.
Other courses that support the further development of the competence provided by this
course: Data Science Project II
Student is able to solve a practical data science challenge as part of a group, taking responsibility of individual elements of a bigger project while actively interacting with the group towards solving a common goal. Can identify and formalise a need or target for a data-driven service given a context (typically a data source or device that produces data), can choose suitable tools for solving the problem, and is able to deliver a functioning service that fills the need. Is aware of the challenges associated with working on real data and recognises potential limitations and challenges of data science tools, and can find information for solving them. Can analyse practical data science tools and make presentable conclusions about their usability. Is able to apply theoretical knowledge learned during other courses in practice.
Recommended time/stage of studies for completion: first year spring or during second year
Term/teaching period when the course will be offered: offered during both spring and fall, covering periods I-II and III-IV
Application of data science skills in producing a practical data science product or service. The detailed content, such as algorithms and tools used for creating the solution, depends on the practical problem and domain chosen by the group.
The course material is provided as lecture notes, slides and links to external sources.
The course combines instructions by the lecturer, presentations by the students, and long-term group work. The details of the supervision of the group work will be determined case-by-case. The students will write a study diary analysing and reflecting their learning during the course.
Grading scale is 1...5.
The grading is based on active participation in the group work, demonstrable individual contributions in the final result, the quality and complexity of the solution and its presentation, and the quality of the individual work not carried out as a group member, such as a tool presentation and the study diary.
The course is completed as a group project. The group is together responsible for delivering a practical data science solution for a problem they have jointly identified. The group will also present the solution for the rest of the course. In addition, the course typically involves elements the student completes alone, such as analysing a particular tool and presenting it for the rest of the course attendants as well as a study diary. The groups receive supervision from the teacher and possibly other instructors.