2023/2024 KAN-CINTO1011U Big Data Management
English Title | |
Big Data Management |
Course information |
|
Language | English |
Course ECTS | 7.5 ECTS |
Type | Mandatory (also offered as elective) |
Level | Full Degree Master |
Duration | One Semester |
Start time of the course | Autumn |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Study board |
Study Board for BSc/MSc in Business Administration and
Information Systems, MSc
|
Course coordinator | |
|
|
Main academic disciplines | |
|
|
Teaching methods | |
|
|
Last updated on 31-01-2023 |
Relevant links |
Learning objectives | ||||||||||||||||||||||||||
|
||||||||||||||||||||||||||
Course prerequisites | ||||||||||||||||||||||||||
Students should have a basic understanding of quantitative data analysis and a willingness to work with computational methods. | ||||||||||||||||||||||||||
Examination | ||||||||||||||||||||||||||
|
||||||||||||||||||||||||||
Course content, structure and pedagogical approach | ||||||||||||||||||||||||||
This course is designed to equip students with conceptual and technical knowledge of tools and techniques for analyzing large data sets — namely, machine learning models. Students will study how organizations leverage big data for innovation and value creation, how to implement and evaluate different types of machine learning models, and also gain an understanding of the practical and ethical boundaries that accompany big data applications.
The course is planned to run in person and will be comprised of a weekly lecture and a weekly exercise session with Python and Jupyter Notebooks. Students with no prior programming experience will be encouraged to complete a basic online tutorial upon beginning the course (e.g., https://pandas.pydata.org/pandas-docs/version/0.15/10min.html).
The course also includes an independently chosen project on big data management to be completed in groups of 2-4 students. Students will develop a business case, select an appropriate data set, implement machine learning models, and assess them from business and data science perspectives.
The course will cover the following main topic areas:
|
||||||||||||||||||||||||||
Description of the teaching methods | ||||||||||||||||||||||||||
A combination of in-person lectures and in-person, hands-on exercise sessions. | ||||||||||||||||||||||||||
Feedback during the teaching period | ||||||||||||||||||||||||||
Students will receive feedback in three ways
throughout the course. (1) The lecture sessions will incorporate
anonymous polls whereby the students can test their understanding
of concepts covered previously and then ask questions publicly. (2)
During the exercise sessions the students will work in groups and
receive peer-to-peer feedback, and also have the opportunity to
receive specialised feedback from the professor as they work to
ensure understanding of the practical aspects of the course. (3)
Finally, students will be given the option of submitting a brief
project plan mid-way through the course for the professor to
provide written comments on.
|
||||||||||||||||||||||||||
Student workload | ||||||||||||||||||||||||||
|
||||||||||||||||||||||||||
Expected literature | ||||||||||||||||||||||||||
The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy any material.
|