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2020/2021 KAN-CBUSV2036U Applying Data Analytics in Digital Business (B)
| English Title | |
| Applying Data Analytics in Digital Business (B) |
Course information |
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| Language | English |
| Course ECTS | 7.5 ECTS |
| Type | Elective |
| Level | Full Degree Master |
| Duration | One Semester |
| Start time of the course | Spring |
| Timetable | Course schedule will be posted at calendar.cbs.dk |
| Min. participants | 30 |
| Max. participants | 100 |
| Study board |
BUS Study Board for BSc/MSc in Business Administration and
Information Systems, MSc
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| Last updated on 14-12-2020 | |
Relevant links |
| Learning objectives | ||||||||||||||||||||||
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| Prerequisites for registering for the exam (activities during the teaching period) | ||||||||||||||||||||||
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Number of compulsory
activities which must be approved (see s. 13 of the Programme
Regulations): 2
Compulsory home
assignments
There are 3 mandatory activities through the semester. For each of the main course sections, (1) survey research, (2) experimental research, and (3) prediction models, there will be a multiple choice test at the end of the section. Each student has get 2 activities approved in order to go to the ordinary exam. There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot participate due to documented illness, or if a student does not get the activity approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (10 pages) which will cover 2 mandatory assignments. |
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| Course content, structure and pedagogical approach | ||||||||||||||||||||||
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Strategic use of data can help organizations to generate business insights that provide objective advice relating to the strategy, structure, management and operations of an organization. The digitalization of industries has given firms easy access to large amounts of data that can be used to derive valuable business insights. Especially, small and medium sized firms can benefit from the increased access to relevant data to generate valuable business insights without significant investment. For instances, large scale online surveys provide detailed and comprehensive insights on consumers’ and citizens’ perceptions. E-commerce leaders are conducting a plethora of experiments to find the best performing design of their mobile app and website. Music streaming providers use data on our listening behavior to accurately predict our music preferences. For companies, the question is how to collect and analyze relevant data to gain the valuable business insights.
When analyzed rigorously, data offers tremendous business potential. This is why firms are looking for university graduates possessing the skills to collect, analyze, and interpret quantitative data. Especially employees at the intersection of business and analytics can provide significant value to companies. These employees possess fundamental knowledge of both business and analytics that helps firms to steer their analytics efforts into strategically valuable directions. The aim of this course is to understand the business value of data and become able to unlock this value for businesses.
This course will enable you to learn how to apply different methods to collect and analyze data to inform business decisions. Deploying rigorous methods is the cornerstone to answer important business questions. We will use statistical analysis software and interpret the business implications of our empirical findings. We will train you in how to ask research questions and how to answer them using quantitative research methodologies. To achieve our learning objectives, you will work on interesting hands-on projects in a digital context applying survey methodology, experimentation, and predictive analysis.
Importantly, this course does not require any pre-experience in statistics but rather your motivation and interest to acquire these valuable skills. This course can also help you to conduct quantitative research for your Master thesis project. |
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| Description of the teaching methods | ||||||||||||||||||||||
| Lectures and workshops based on case studies. | ||||||||||||||||||||||
| Feedback during the teaching period | ||||||||||||||||||||||
| Students will receive feedback on their
performance and how to improve during:
* Exercises in lectures that are being prepared by students and discussed in class. The teachers will give feedback on the solutions suggested by students. * Group presentations that are being conducted in the workshop sessions. Students will be working on activities related to material covered in the lectures and present their work. Students will then receive feedback from peers as well as the teachers. * Individual multiple choice tests at the end of each section. Students will get feedback when conducting the mandatory individual multiple choice tests by comparing the answers they provided with the correct answers. |
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| Expected literature | ||||||||||||||||||||||
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The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books. |
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