Master's Programme in Data Science is responsible for the course.
The course belongs to the DATA11100 Academic Skills for Data Science module.
The course is not available to students from other degree programmes.
Prerequisites in terms of knowledge
Admission to the Data Science Programme.
Prerequisites for students in the Data Science programme, in terms of courses
Prerequisites for other students in terms of courses
Recommended preceding courses
993734 Academic writing I is a compulsory course for the degree students in Data Science, and it should be taken at the same time as this course.
Information retrieval and reference management related courses organised by the Helsinki University Library are also recommended for the data science students, either during this course or later.
Courses supporting the further development of the competence provided by this course are Data Science seminars I and II, 993735 Academic writing II (to be taken during the first seminar), and Master's thesis.
After completing this course, the student is orientated to study successfully in the programme. The student can describe the degree structure and tell what kind of the teaching, learning and assessment methods are used in the programme. The student is also aware of the different learning and working environments at the departments related to the programme, and can use different tools in those environments to support her/his studies. Based on that, the student is able to make a personal study plan.
After completing the course, the student can describe the scientific writing process and its steps. The student can search for scientific articles on a certain topic, read, analyse and review those articles, and then select of them those articles that are most relevant for the further use. The student is also able to organise and compose a structure for a scientific essay, article or report, and then write such scientific texts based on articles written by other researchers. The student can describe different ethical questions related to scientific writing and how unethical behaviour can be avoided, and furthermore, the student can also apply this knowledge of her/his own writing process.
Recommended time/stage of studies for completion: during the first term of the master's degree studies
Term/teaching period when the course will be offered: the course is offered every year in the autumn term during both periods.
The course focuses on orientation to studies in the master's degree programme in data science. The main focus of the course is in the scientific reading and writing skills needed in those studies.
The first part of the course helps the students to orientate into their studies in the programme and to make their study plan. In this part, the students will get more detailed information on the structure and the practices of the programme, including the teaching, learning and assessment methods used in the courses. They will also get acquainted with the the learning and working environments at the departments related to the programme.
The second part of the course is related to skills needed in scientific writing: searching, selecting and reading scientific articles, making notes during reading, organising the found material, and writing scientific text. This part of the course also covers themes like how scientific articles should be reviewed, and what kind of writing-related ethical issues, like stealing, cheating, and plagiarism, are not tolerated by the academic community. The students will learn these different themes both in theory and in practice by writing a short scientific essay on a given topic.
Some articles related to scientific reading and writing are provided by the teacher during the course. In addition to that, each student must read at least 3-5 scientific articles related to the topic of her/his scientific report.
Recommended as supplementary reading:
any kind material related to scientific reading and writing.
Students are expected to actively participate the lectures and other teaching sessions organised during the course. Each participant reads some scientific articles on a chosen topic in data science, and writes a short scientific essay based on those articles. Students also peer-review texts of other students on this course.
Teachers give lectures on the main topics of the course. They also guide the writing process and give feedback on the scientific essays.
Grading scale of the course is pass/fail.
The course is completed by participating the organised sessions and writing a short scientific report.