IS-914 Data Science and Automated Machine Learning
- ECTS Credits:
- 5
- Responsible department:
- Faculty of Social Sciences
- Course Leader:
- Ilias Pappas
- Lecture Semester:
- Spring
- Teaching language:
- English
- Duration:
- 1 term
The course is connected to the following study programs
- Multimedia and Digital Tools
Teaching language
EnglishPrerequisites
Bachelor´s degree and Job experience for minimum 2 years after achieving the Bachelor´s degree.
Course contents
- Intro to Data Science and basic concepts on Data Mining and Machine Learning
- Data transformations and intermediate data transformations
- Automated Machine Learning (AutoML)
- Business Problem development and solutions with AutoML
Learning outcomes
Upon succesful completion of the course, students will
- Acquire an understanding of data science and its fundamental principles by offering a high-level overview of concepts and principles
- Learn how to prepare datasets, a critical part of Machine Learning
- Gain experience with data transformation and Automated Machine Learning (AutoML) tools
- Evaluate and assess business problems using real data
Examination requirements
Completed lab assignments are required to take the exam. Participation in class discussions is required in order to complete and obtain certain mandatory assignments. Information about these terms is in Canvas.
Teaching methods
Lectures, in the form of webinars over 3 days, including lab exercises applying data transformation software (e.g. Alteryx), an AutoML tool (e.g., DataRobot) and project work.
There are compulsory lab exercises.
Expected working hours are 140 hours
Evaluation
The person responsible for the course, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.
Offered as Single Standing Module
Yes
Admission Requirement if given as Single Standing Module
Bachelor´s degree and Job experience for minimum 2 years after achieving the Bachelor´s degree.
Assessment methods and criteria
The exam is conducted as a directory evaluation of submitted project report. The project is carried out individually or in pairs of two. Further information is in Canvas. The exam is assessed in the form of Pass/Fail