Research Topics in Data Science
This course explores contemporary research in data science, focusing on recent advancements in machine learning and data mining.
Students will engage with cutting-edge research papers, emerging methodologies, and open challenges in the field. Topics include machine learning techniques and algorithms, covering developments in supervised, unsupervised, and reinforcement learning, as well as novel deep learning and statistical methods.
The course also examines processes in data science research, including interpretability, reproducibility, and ethical considerations. Key challenges such as scalability, bias, fairness, and robustness are discussed alongside methods for handling complex data structures.
Additionally, students will explore emerging trends and applications. Through research discussions, hands-on projects, and presentations, students will critically analyse developments and identify open research questions. The course content is updated regularly to reflect the latest trends in data science.
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Course structure
Teaching format
The teaching activities consist of lectures and seminars.
The language of instruction is English.Assessment
The course is examined through assignments and presentation of research results.
Examiner
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Schedule
The schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course. -
Course literature
Note that the course literature can be changed up to two months before the start of the course.
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Contact
Study counsellors - master
- Visiting address
Nod Buildning, Borgarfjordsgatan 12, Kista
- Office hours
Please contact us via email if you want to book a meeting. We are available on Campus in Kista and via Zoom.
- Phone hours
Thursday 12.30–2 pm
- Irregular office hours
No phone hours on Thursday 1 May.
