This course familiarizes you with various aspects of conducting research with online consumer behavior and social media data. Specifically, you will study state-of-the-art literature, learn emerging programming and statistical methods, and discuss insights during lectures and computer lab sessions.|
At the end of this course, you will be able to:
- Explain the structure of databases and handle the first steps in retrieving and managing (incl. merging and aggregating) large datasets using SQL.
- Write new code (assisted by the use of cheat sheets), and edit existing code in Python using Jupyter Notebook.
- Conduct statistical analyses on large datasets from companies such as Twitter, Spotify, and Amazon.com.
- Translate the results of analyses and the theoretical insights gained from studying the literature into managerially relevant findings.
- Critically evaluate your contribution as well as the contribution of others.
- Discuss used methods, results of analyses, and relevant academic literature in writing and/or orally.
ATTENTION! The deadlines for application deviate from the standard OSIRIS deadlines.
Enrollment opens on January 28th and closes on February 16th, 23.59 hrs via https://forms.gle/R4MfyfcDKTjLY5RE9
Enrollment is open to students in:
If a course is not visible, it means that all seats are taken.
- MSc Marketing Management (only if you have not completed 2 cluster B courses yet).
- MSc Marketing Analytics
Research Master in Business students need to send a message to TiSEM-MSc-Marketing-Management-Research@uvt.nl before February 16th (23.59hrs).
This course is not open to student from other programs. We make no exceptions.
All students who registered correctly will be enrolled in the OSIRIS course by program management, after which they will be automatically added to the Canvas page of the course.
The content is divided into four blocks, consisting of
- Data I
- Database technology: Data retrieval, management, and analysis using SQL
- Data II
- Data science skills in Python
- Web scraping and APIs (for data retrieval and for artificial intelligence)
- Methods I
- User-generated content on social and digital media
- Conducting sentiment analysis in Python
- Methods II
- Display advertising and search engine advertising
- Evaluating large-scale online (quasi) field experiments
Typically, students should have the following characteristics when following this class:
- Keen interest in (learning about) statistical analyses and data science; albeit no profound knowledge is required beforehand (except a course in Marketing Research)
- Willingness to learn about a diverse set of computer programs, and readiness to learn writing computer code (e.g., Python code)
- Willingness to engage in self-studying (e.g., via web lectures, Jupyter Notebooks)
- Willingness to work in teams, and being evaluated by other team members
- Willingness to work hard
Type of instructions
Lectures, computer lab sessions, self-study (e.g., using web clips, or Jupyter Notebooks)
Type of exams
Quizzes and team assignments (during the course of the semester, in total 40%), and computer exam (60%). Both the computer exam grade and the final course grade need to equal 5.5 or higher to pass the course.
- The syllabus for the course will be made available at the start of the course.
- There is no need to purchase books.