In this course students learn to translate a research question into a formal model that can be tested, and how to use R to perform various tests and analyses.
The understanding of research methods to analyse large datasets and how these methods can be used becomes ever more important. Combined with the increased interest in data analysis, alternative software packages like R in data analysis have become mainstream, both in academia as in the industry.
dr. Dennis Herhausen
Course Coordinator, lecturer (dennis.herhausen@vu.nl) Dennis Herhausen (Ph.D., University of St.Gallen) is Associate Professor of Marketing at Vrije Universiteit Amsterdam. Previous he was an Associate Professor of Marketing at KEDGE Business School, Visiting Professor at the St.Gallen Institute of Management in Asia, a Visiting Academic at Cardiff University, and an Assistant Professor of Marketing at the University of St.Gallen. Before joining academia, he worked as an International Marketing Manager for a German Food Producer and a Sales and Marketing Consultant. Dennis’ research, teaching, and executive education revolve around the themes of digital communication, customer journeys and experience, multichannel management, digital capabilities, and social media management.
Barış Kocaman
Baris Kocaman is an Assistant Professor of Marketing at Vrije Universiteit Amsterdam since January 2023. His research focuses on the impact of new technologies, business models, and marketing instruments on customer value creation (acquisition, retention, and expansion). He employs advanced regression-based approaches and causal inference to understand customer behavior and provide data-driven insights. Baris has taught business statistics with applications in marketing, operations, and innovation management at BSc, MSc, and EMBA levels. He holds a PhD from Eindhoven University of Technology and an MSc from Columbia Business School.
Course Description
The understanding of research methods to analyse large datasets and how these methods can be used to compare countries and cultures becomes ever more important. To use analytics to solve research problems, you need to have a solid background not only in the available statistical methods, but also in the inherent boundaries of these statistical methods. This course teaches technical skills while simultaneously deepening the understanding of modelling, research designs, and the limitations of data analysis.
For the course manual please click here.
Study Characteristics
- Study period: October 2024– December 2024 (Period 2)
- Credits: 5 ECTS
- Tuition fee: €1250 (20% discount for early bird registration)
- Registration deadline: 14-10-2024 (early bird registration: 30-09-2024)
- Recommendation: This is an ideal course for first year students.
- The course consists of hands-on tutorials, alternated with more generic reflections on the materials when needed, It is very important that you actively apply what you’ve learned during the tutorials. Practice is vital to mastering a programming language. We will meet at least twice a week in the first two weeks of the course, and twice a week in week 5 and 6. You are expected to work on the assignments in the weeks where no tutorials are planned. Each session lasts about 4 hours. Although the tutorials are not mandatory, your attendance is highly recommended to keep up with the course materials. During the course you’ll work on two assignments. During week 1-4, you work on assignment 1 (available on Canvas). During week 5-8 you work on assignment 2. Note that at the start of week 5 we introduce assignment 2. At the end of week 3 (Friday 23.59) and the end of week 6 (Friday 23.59) you have to submit drafts of your assignments using Canvas. These drafts will receive feedback from your fellow students. At the end of the course you need to hand-in a revised version of both assignments. Please prepare for the tutorials by reading the assignment beforehand and downloading the data in advance.
- Assessments: Your overall course grade is based on two assignments. During the course you will work on these two assignments. You submit each assignment twice: first you receive feedback from another student (and have to give feedback to the same student), the second time you receive a grade. The quality of the feedback you provide will be graded by the lecturers, and each counts for 10% of your grade. The final submission of the assignments will be graded and count for 40% each. The overall course grade needs to be a 5.50 or higher.
- Admission requirements: All participants are expected to be proficient in English
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Course Description & Study Characteristics
Course Description
The understanding of research methods to analyse large datasets and how these methods can be used to compare countries and cultures becomes ever more important. To use analytics to solve research problems, you need to have a solid background not only in the available statistical methods, but also in the inherent boundaries of these statistical methods. This course teaches technical skills while simultaneously deepening the understanding of modelling, research designs, and the limitations of data analysis.
For the course manual please click here.Study Characteristics
- Study period: October 2024– December 2024 (Period 2)
- Credits: 5 ECTS
- Tuition fee: €1250 (20% discount for early bird registration)
- Registration deadline: 14-10-2024 (early bird registration: 30-09-2024)
- Recommendation: This is an ideal course for first year students.
- The course consists of hands-on tutorials, alternated with more generic reflections on the materials when needed, It is very important that you actively apply what you’ve learned during the tutorials. Practice is vital to mastering a programming language. We will meet at least twice a week in the first two weeks of the course, and twice a week in week 5 and 6. You are expected to work on the assignments in the weeks where no tutorials are planned. Each session lasts about 4 hours. Although the tutorials are not mandatory, your attendance is highly recommended to keep up with the course materials. During the course you’ll work on two assignments. During week 1-4, you work on assignment 1 (available on Canvas). During week 5-8 you work on assignment 2. Note that at the start of week 5 we introduce assignment 2. At the end of week 3 (Friday 23.59) and the end of week 6 (Friday 23.59) you have to submit drafts of your assignments using Canvas. These drafts will receive feedback from your fellow students. At the end of the course you need to hand-in a revised version of both assignments. Please prepare for the tutorials by reading the assignment beforehand and downloading the data in advance.
- Assessments: Your overall course grade is based on two assignments. During the course you will work on these two assignments. You submit each assignment twice: first you receive feedback from another student (and have to give feedback to the same student), the second time you receive a grade. The quality of the feedback you provide will be graded by the lecturers, and each counts for 10% of your grade. The final submission of the assignments will be graded and count for 40% each. The overall course grade needs to be a 5.50 or higher.
- Admission requirements: All participants are expected to be proficient in English
Would you like to register or want to know more?
Please register with the Apply Now button at the top of this page. For more info please contact the course coordinator dr. Dennis Herhausen: