About this course
In our increasingly digitized society, with sensors embedded in our bodies, equipment, and surroundings, we are generating, collecting, and storing data at unprecedented rates. Within this vast sea of data lie insights crucial for understanding, predicting, and impacting every aspect of our existence, including human behavior, financial trends, sustainable development, and health and illness. Extracting these insights requires careful execution at each step in the data analytics pipeline.
In this course, we will take a hands-on approach to explore the key steps in the data analytics pipeline: data gathering, curation, and transformation; the use of computational and statistical tools to analyze both small and large datasets; and data visualization and reporting of analytical insights. Through real-world case scenarios, we will also evaluate and reflect on the validity of the analytical models.
Syllabus
Pre-requisites
One year of computer science at university level. One of the computer science courses should be in algorithms and data structures. A course in statistics and a course in linear algebra are recommended. Knowledge of at least one object-oriented programming language (e.g. Java, Python). Notes: Students who have taken other courses with a strong computational background are encouraged to apply and indicate which courses would be equivalent in content to the stated prerequisites. Students who have not taken the recommended courses should consider enrolling in DIS Linear Algebra and/or DIS Statistics in the same term.
Long Study Tour
Short Study Tour
About this tour
During our three-day Study Tour within Sweden, we will visit professionals from academia and industry to learn about the latest developments in the field of data science.
Through our visits, will we investigate how the field is evolving in Sweden, and reflect upon major opportunities and challenges within data science research and implementation in real-world applications. In addition, we will participate in cultural activities (e.g. city tours, outdoor activities, visits to museums) to learn about Swedish history, culture, and nature.
Learning outcomes
- Obtain an understanding of the dynamic interplay between research, development, industry, and everyday applications of data analytics through conversations with data science experts working in Sweden
- Broaden your perspective of data analytics in the Swedish context through field visits, while assessing affordances and limitations of incorporating data analytics approaches in various sectors
- Engage in your personal learning process outside the classroom by actively participating and challenging your current ideas and assumptions
Possible activities
- Meet with research groups from renowned Swedish universities (e.g. Chalmers University of Technology) that specialize in data analytics, machine learning, and artificial intelligence
- Visit specialized companies that incorporate data analytics in their processes (e.g Volvo Group, IKEA)
- Visit hubs that support incorporation of data analytics and data visualization in various sectors