The internet offers abundant possibilities to collect data (e.g., from social networks, from digital media providers, from price comparison websites, from online platforms) that can be used in empirical research projects and/or provide business value. After successful completion of this course, students will be able to:
- Identify online data sources and evaluate their value in the context of a specific research question or business problem
- Assess the terms and conditions for collecting, storing, and sharing data
- Collect data via web scraping and Application Protocol Interfaces (APIs) by mixing, extending and repurposing code snippets
- Transform semi-structured JSON data to structured data sets for statistical analysis (“parsing”)
- Store and manage data using file-based systems
- Draft, execute, monitor and audit online data collections locally and remotely
- Document and archive collected data, and make it available for public (re)use
- Combination of self-paced tutorials (e.g., using Jupyter Notebooks or pre-recorded clips), and interactive live streams for feedback and coaching
- Learn state-of-the-art tools popular among scientists, marketing analysts and data scientists (e.g., Python), and collect data from real websites and APIs
- Open education & open science (all material is open; publicly accessible course page that stays with you, even after the end of the course)
Student profile / prerequisites
- The course is instructed to MSc students in the Marketing Analytics (TiSEM) program.
- The course expects students to have acquired working knowledge in Python (e.g., from introductory courses at Datacamp), including an understanding of data types (e.g., characters, integers), loops, if-else statements, and functions.
- The course welcomes novices, of whom extra preparation prior to the start of the course is expected. Preparation material will be shared with students in advance in the form of Jupyter Notebooks or course recommendations at Datacamp. Novices may further benefit from following other courses at Tilburg University in which Python is used, for example, Research Skills: Data Processing and Research Skills: Data Processing Advanced.
- Students are recommended to use their own computer for this course (Windows, Mac or Linux). Android/Chromebook/iOS devices are not supported.
- Team project (4-5 team members) with self- and peer assessment (see below) (50%)
- Computer exam (50%)
Students pass this course if the final course grade (i.e., the weighted average of the group project and exam; weights indicated above) is ≥ 5.5, and the exam is passed (≥ 5.5). Final course grades are rounded to multiples of half points (e.g., 6, 6.5, 7, etc.).
Grades are made available on Canvas.
Students will submit a final team project (4-5 team members) at the end of the course.
Students’ individual grades will be corrected upwards or downwards, depending on their individual contribution to the overall team effort (e.g., measured by means of scoring themselves and their team members on, among others, the quantity and quality of their contributions).